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CSCR Resource

TRANSPORTATION TECHNOLOGY INNOVATIONS

Prepare by Kusumal Ruamsook, September 25, 2016

About This Document

This document is prepared as part of presentation materials on the topic of transportation technology

innovations. Applications of game-changing technologies in transportation and logistics identified in this

document are based on a literature survey. The study includes literature and secondary data sources

published on topics related to transport and logistics technologies within approximately three-year timeframe

(2014–2017). Data sources are Penn State library database, managerial magazines, industry reports, and

websites of relevant industry associations.

Table of Contents

Robotics: Autonomous and Semi-Autonomous Vehicles ................................................................................................ 2

Ground Vehicles: Self-driving/ Driverless/ Autonomous Robot, Ground-based Drones ........................................ 4

Aerial Vehicles: Unmanned Aerial Vehicles/Autonomous Flying Vehicles/Drones .................................................. 8

Mobile Internet (MI) and Communication Technology .................................................................................................... 9

Digitalization: 3D Printing, Internet of Things (IoT), Machine-to-Machine (M2M) Communication, Big

Data Analytics, Deep Learning .................................................................................................................................................. 12

Big Data ............................................................................................................................................................................................. 13

Internet of Things (IoT) and M2M Communication ........................................................................................................... 23

3D Printing ....................................................................................................................................................................................... 32

Deep-learning/Self-learning/Machine-learning Systems ................................................................................................ 32

Cloud-based Technology ............................................................................................................................................................. 33

Augmented Reality ......................................................................................................................................................................... 34

Transportation Management System .................................................................................................................................... 35

Mobile TMS ....................................................................................................................................................................................... 37

IoT connected TMS ........................................................................................................................................................................ 38

Cloud-based TMS ........................................................................................................................................................................... 39

References ........................................................................................................................................................................................... 42

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ROBOTICS: AUTONOMOUS AND SEMI-AUTONOMOUS VEHICLES

� Fully automated vehicles not yet available. There are currently numerous vehicle models that feature

some degree of automation, but fully automated models are not yet available. Those vehicles that do

possess high levels of automation can generally only operate in automated mode under certain

conditions, and in most cases, the driver must resume control of the vehicle if a problem is encountered

during automated operation. Currently, basic vehicle automated features, such as adaptive cruise control

and imminent collision braking, are found in newer-model cars. However, the field of automated vehicles

is rapidly advancing, and fully automated vehicles could be on the road anywhere from within the next

few years to a decade from now. These technologies could fundamentally change both how drivers

interact with the roadway environment and how government agencies manage transportation

infrastructure (Baker et al. 2016).

Various technologies used. Automated vehicles rely on a variety of technologies to function. First,

the vehicles use previously developed high-definition maps of the roadway environment, which provide

a reference point for navigation. Automated vehicles then use high-accuracy location technology (such

as that obtained from GPS) to determine where the vehicle is, and onboard sensors and vision-based

systems (such as LiDAR, radar, and ultrasonic devices) to detect and compile information about the

dynamic road environment (Baker et al. 2016).

Connected vehicle system: IoT and automated vehicles beyond sensor equipment. In order for

the safety benefits of automated vehicles to be fully realized, connectivity between vehicles (Vehicle-to-

Vehicle, or V2V communication) and between vehicles and infrastructure (Vehicle-to-Infrastructure, or V2I)

will be needed. These connected vehicle systems provide automated vehicles with information that

might not be available through sensor equipment alone. For example, V2V applications would allow a

vehicle that is making a sudden braking maneuver to transmit that information to other vehicles

behind it that might not otherwise be able to detect the impending slowdown. Similarly, roadway-

based sensors in a V2I application might detect the presence of a pedestrian, about to cross a roadway,

that is in the vehicle’s sensory blind spot. Connected vehicle applications, and particularly V2I systems

that require the installation of equipment within the right of way, would be largely dependent on

government initiative and funding. Very few agencies have the funding available to invest in this sort

of infrastructure, and many have not yet accounted for these types of technologies in their strategic

planning and associated fund programming processes (Baker et al. 2016).

V2V. Vehicle-to-vehicle (V2V) communications comprises a wireless network where automobiles send

messages to each other with information about what they’re doing. This data would include speed,

location, direction of travel, braking, and loss of stability. Vehicle-to-vehicle technology uses dedicated

short-range communications (DSRC), a standard set forth by bodies like FCC and ISO. Sometimes it’s

described as being a WiFi network because one of the possible frequencies is 5.9GHz, which is used by

WiFi, but it’s more accurate to say “WiFi-like.” The range is up to 300 meters or 1000 feet or about 10

seconds at highway speeds. In the United States, V2V is an important part of the intelligent transport

system (ITS), a concept that is being sponsored by the United States Department of Transportation

(DOT) and the National Highway Traffic Safety Administration (NHTSA). The technology could become

mandatory in the not-too-distant future. V2V communication is expected to be more effective than

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current automotive original equipment manufacturer (OEM) embedded systems for lane departure,

adaptive cruise control, blind spot detection, rear parking sonar and backup camera because V2V

technology enables an ubiquitous 360-degree awareness of surrounding threats. Already many cars

have instruments that use radar or ultrasound to detect obstacles or vehicles. But the range of these

sensors is limited to a few car lengths, and they cannot see past the nearest obstruction. Simply put,

the first generation of V2V systems would warn the driver but not take control of the car. Later

implementations would improve to brake or steer around obstacles and eventually merge with self-

driving cars [see self-driving truck platoon]. For example, V2V applications would allow a vehicle that is

making a sudden braking maneuver to transmit that information to other vehicles behind it that might

not otherwise be able to detect the impending slowdown. V2V warnings might come to the driver as

an alert, perhaps a red light that flashes in the instrument panel, or an amber then red alert for

escalating problems. It might indicate the direction of the threat. All that is fluid for now since V2V is

still a concept with several thousand working prototypes or retrofitted test cars. Most of the prototypes

have advanced to stage where the cars brake and sometimes steer around hazards.V2V could capture

and transmit these inputs, among others. By the time V2V arrives in cars, some may be stripped out for

the sake of simplicity or cost-cutting (Baker et al. 2016; Extreme Tech 2014; IoT Agenda n.d.; MIT

Technology Review n.d.; Transport Topics 2016).1

Vehicle speed

Vehicle position and heading (direction of travel)

On or off the throttle (accelerating, driving, slowing)

Brakes on, anti-lock braking

Lane changes

Stability control, traction control engaged

Windshield wipers on, defroster on, headlamps on in daytime (raining, snowing)

Gear position (a car in reverse might be backing out of a parking stall)

Vehicle-to-infrastructure (V2I) signs and signals could transmit traffic and weather indicators

(Baker et al. 2016; Extreme Tech 2014; IoT Agenda n.d.; MIT Technology Review n.d.; Transport Topics

2016):

Traffic signal phase (green-yellow-red)

Stop sign

No left turn at intersection

Temperature (at a bridge that freezes over before the ground)

Signals from cars ahead

Approaching emergency vehicle

� Autonomous and semi-autonomous transportation is on the rise both on the ground and in the air.

1 http://www.extremetech.com/extreme/176093-v2v-what-are-vehicle-to-vehicle-communications-and-how-does-it-

work

http://internetofthingsagenda.techtarget.com/definition/vehicle-to-vehicle-communication-V2V-communication

https://www.technologyreview.com/s/534981/car-to-car-communication/

http://www.ttnews.com/articles/basetemplate.aspx?storyid=42540&page=1

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Self-driving vehicles. Breakthroughs in sensor and imaging technologies have resulted in a new

generation of self-driving vehicles that are more flexible and reliable than ever before. In logistics, self-

driving vehicles have gradually been adopted in carefully controlled environments such as warehouses

and yards over the last few years. The next evolutionary step will be to deploy self-driving vehicles in

shared and public spaces such as on highways and city streets to further optimize logistics operations

and increase safety (DHL 2016).

Unmanned aerial vehicles (UAVs)/Drones. Unmanned aerial vehicles (UAVs) or ‘drones’ could

change tomorrow’s logistics by adding a new form of express delivery via carefully coordinated air

networks. While UAVs won’t replace traditional ground-based transportation, they will provide value in

areas of high traffic congestion and in remote locations (DHL 2016).

Ground Vehicles: Self-driving/ Driverless/ Autonomous Robot, Ground-

based Drones

� Applications in warehouses: Autonomous shuttles and forklifts. Self-driving vehicles have already

made inroads in logistics, reaching a level of maturity for commercial use in warehouse operations. First

generations of autonomous shuttles and forklifts (e.g., Linde and Balyo) are being deployed in clearly

defined and controlled areas of the warehouse, unlocking new levels of process efficiency and

performance (DHL 2016).

� Applications on the road: Robot taxi, Self-driving truck, and Robot truck platoon

Robot taxis (Unmanned/Autonomous). Robot taxis developed by General Motors and operated by

the ride-sharing service Lyft. The deal would immediately make GM a “preferred provider” of short-

term use vehicles available for rent to today’s Lyft drivers. But the new partnership has also clearly set

its sights on the long-term goal of developing a robotic version of current ride-sharing services such as

Lyft and Uber. The GM and Lyft partnership has plenty of competition from other companies that have

nurtured similar self-driving car ambitions. Japanese automaker Nissan has perhaps the most

aggressive development schedule of any company with plans to test prototype robot taxis within the

next two years. Uber, a direct U.S. ride-sharing competitor for Lyft, has also pursued development of

robot taxis to the extent of poaching many Carnegie Mellon researchers from the university’s robotics

department. It has been pushing to become the first to test completely driverless cars in public. Other

car and tech companies have more amorphous plans for self-driving cars. Tech giant Google has been

gathering talent to flesh out the business side of its self-driving car venture after testing prototypes on

public roads. Automaker Toyota has invested heavily in autonomous car technologies with a planned

$1 billion being spent on AI and robots at a Silicon Valley R&D center over five years. Similarly, Ford has

been racing to staff up its own Silicon Valley research lab. Even Silicon Valley giant Apple has been

hoovering up talent for its own mysterious self-driving car plans (Hsu 2016). In early 2015, Apple

announced plans to ship its first, albeit far from ready today, self-driving car in 2019 (Robinson 2016).

An electric future: Value proposition and obstacle. In a fully autonomous vehicular future, cars

won’t need nearly as much fuel, if any at all. In turn, this would mean a significant decrease in

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emission related pollution. One major obstacle keeping developers from converting self-driving

cars to entirely electric motors is the fact that car batteries don’t last nearly as long as gas-powered

engines. Most taxis plug anywhere from 40 to 70 thousand miles per year, while personal cars in

the US usually last to about 150,000 miles. It seems that until a longer lasting battery can be

created, fully electric cars might not quite hit the mark (Miller 2016).

Self-driving/Autonomous highway (Manned/Semiautonomous): Daimler Trucks North America

awarded license in Nevada. Line-haul transportation often involves long journeys overnight and also

during rough weather conditions. Logistics providers can utilize various driverless technologies to

support each driver’s health and safety. One concept is the autonomous highway which requires

manual operation only when the truck enters or leaves the highway (DHL 2016). Daimler Trucks North

America have just been awarded the first license for an autonomous commercial vehicle on the roads of

the United States. The license was granted in Nevada, a state known for its progressive attitudes to

self-driving vehicles. The system was first demonstrated in Germany last year [2014] but on a closed

section of road. The system is called ‘Highway Pilot’ so this is on freeways and highways at this point,

not for the inner city. Daimler’s truck is capable of “level three” self-driving2 – on a scale that goes from

zero to four – which means it can take over the driving itself if required. A driver will have to be behind

the wheel, ready to take over in situations the computer cannot handle, such as roadworks or bad

weather, but in other situations he or she would be free to take their eyes off the road. The truck’s

system tells the driver when autonomous mode can be activated – and also gives him a countdown

when he needs to resume control of the vehicle (Stewart 2015).

Robot truck platoon/Robo Convoy/Robotic Road Train (Manned/Semiautonomous). As more

companies become more involved in the idea and benefits of a self-driving truck, the role of this

technology will become increasingly linked with the logistics industry (Robinson 2016). Truck platoon

technology aims to create convoys of wireless-linked semi-autonomous vehicles where a vehicle enters

a snaking train of vehicles under the command of the lead vehicle. In Japan Demo, the speed of the

leader was communicated wirelessly every 20 milliseconds to allow the train to make constant

2 NHTSA’s four levels of automation

No-Automation (Level 0): The driver is in complete and sole control of the primary vehicle controls – brake,

steering, throttle, and motive power – at all times.

Function-specific Automation (Level 1): Automation at this level involves one or more specific control functions.

Examples include electronic stability control or pre-charged brakes, where the vehicle automatically assists with

braking to enable the driver to regain control of the vehicle or stop faster than possible by acting alone.

Combined Function Automation (Level 2): This level involves automation of at least two primary control

functions designed to work in unison to relieve the driver of control of those functions. An example of combined

functions enabling a Level 2 system is adaptive cruise control in combination with lane centering.

Limited Self-Driving Automation (Level 3): Vehicles at this level of automation enable the driver to cede full

control of all safety-critical functions under certain traffic or environmental conditions and in those conditions to

rely heavily on the vehicle to monitor for changes in those conditions requiring transition back to driver control. The

driver is expected to be available for occasional control but with sufficiently comfortable transition time. The

Google car is an example of limited self-driving automation.

Full Self-Driving Automation (Level 4): The vehicle is designed to perform all safety-critical driving functions and

monitor roadway conditions for an entire trip. Such a design anticipates that the driver will provide destination or

navigation input, but is not expected to be available for control at any time during the trip. This includes both

occupied and unoccupied vehicles.

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adjustments and to ensure that they were driving at both an optimum and safe distance. The third,

and fourth vehicles were also equipped with millimeter-wave radar and infrared laser radars to detect

obstacles and recognize lane markings, as well as a series of algorithms and fail-safe controls to better

manage the vehicles. The drivers are then free to do whatever they like – read a book, take a nap or just

sit. When they are ready to leave, the driver takes back control and exits the train. In theory the

technology offers several benefits, such as cutting down on accidents and improving fuel efficiency

(Ashley 2014).

Demonstrations. The Japanese demonstration in February this year [2014], a line-up of four large

trucks circled an oval test track in Tsukuba City, Japan, was the latest of a couple of projects set up

to trial and develop the technology. A couple of years ago a project at RWTH Aachen University in

Germany operated a platoon of four trucks spaced at 10m (33ft) intervals. In the US, research at the

University of California, Berkeley put three-truck caravans on the road with spacing from 3 to 6m

(Ashley 2014). Also in US, two computer-assisted 18-wheeler trucks were tested in Nevada [2012].

The technology, developed by Peloton Tech, uses radar and a wireless link so that the following

trucks travel at the same speed, braking simultaneously for safety, and doing so on an automated

system that doesn't have the delays of human reaction time (Atherton 2014). And last year [2013],

the Scania Transport Laboratory in Sweden tested aspects of truck platooning on a 520km (325

miles) shipping route between the cities of Sodertalje and Helsingborg. In addition, a recently

completed European project led by Volvo called Safe Road Trains for the Environment (Sartre) has

explored using cars and lorries simultaneously. Its platoons cruised at 85 km/h (50mph) with a gap

between each vehicle of 6m. The study vehicles put in some 10,000 km (6,200 miles) of road, and –

like the Japanese study – indicated that platooning could offer substantial benefits (Ashley 2014). In

April 2016, six platoons of self-driving trucks converged in Rotterdam in the Netherlands, part of the

2016 Volvo/European Truck Platooning Challenge and marking the first time that trucks equipped

with the technology had crossed international borders. The convoys were made up of trucks

manufactured by six companies: DAF Trucks, Daimler Trucks, Iveco, MAN Truck & Bus, Scania, and

Volvo Group. Each truck platoon left on March 29 and followed a different route, traveling from

Belgium, Germany, and Sweden. The Scania team, based in Sweden, travelled the furthest, 1,250

miles across four borders. Each truck had a human driver inside who contributed to some steering

tasks (Griggs 2016).

Value propositions. Truck platoon technology will save money on fuel costs, which results in

savings of the cost of shipping. A procession of trucks moving in tandem, at a safe distance from

one another, and able to communicate with one another in the event of a blow out, accident, or

other event, would help to reduce drag on the overall caravan, which results greater fuel efficiency

(Robinson 2016). Demonstration in Japan showed that the trucks’ fuel economy improved by 15%

or more on average, by allowing the vehicles to slip stream each other like drafting Tour de France

riders. It also showed that the lead truck can benefit from less drag at its rear as the ‘bow-wave’ of

the tailing vehicle in-effect “pushes” the lead truck forward (Ashley 2014).

Autonomous last-mile delivery: Self-driving trolleys/parcel vehicles; Personal delivery devices

(PDDs). Autonomous last-mile solutions such as self-driving trolleys that autonomously follow a

delivery person can be used to support workers as they cope with growing parcel volumes. Self-driving

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parcel vehicles that use sidewalks to deliver individual orders could also enable rapid delivery services

(DHL 2016).

Assistance robots for local delivery. Assistance robots for local delivery will be useful to meet

the growing demand for convenience logistics. They could follow delivery personnel to transport

heavy items, presort parcels inside delivery vehicles, and autonomously deliver letters and parcels

to dedicated collection points (DHL 2016).

Example: Starship delivery robot to test in DC. Six-wheeled delivery robot has received

approval from the Washington, DC Department of Transportation to start testing a delivery

program as early as this September [2016]. The local government to develop the Personal

Delivery Device Pilot Act of 2016 defines “personal delivery devices,” or PDDs, as “a device powered

by an electric motor, for use primarily on sidewalks, capable of: transporting items with or without

an operator directly controlling the device; identifying and yielding to pedestrians, bicyclists, other

lawful users of public space, and property; and navigating public thoroughfares; and interpreting

traffic signals and signs at crosswalks.” Starship’s delivery robot, obviously, fits that description.

It’s electric, has a trunk that can fit about 20 pounds of cargo, and has a suite of cameras around

the outside that can be used to identify obstacles and help guide the robot to its destination. The

company will be allowed to test up to five of the robots, and tests can begin as soon as September

15th and are allowed to run until December 31st of 2017. All robots will be restricted to a top

speed of 10 miles per hour and a gross weight of 50 pounds (excluding cargo). The robots must

be able to automatically alert their operators if it encounters either a technology failure or loss of

communication occurs, according to the act. When this happens, it’s the operator’s responsibility

to either to assume direct control of the PDD, or command it to pull off the sidewalk, where the

company has 24 hours to remove the robot (O’Kane 2016). [Public demonstration in July 2016 in

Austin, TX]

Examples: Transwheel drone still a concept design. Transwheel is a unicycle drone that use a

single self-balancing wheel and a robotic arm to pick up and carry packages. The Transwheel

concept reimagines package distribution as a round-the-clock autonomous service carried out by

robotic single-wheel drones that work independently and together to ensure timely, efficient

delivery. Each wheel features a self-balancing gyroscopic system, electric arms, and GPS-driven

communication capability. The electric robots would work alone for small deliveries or operate

together as a swarm to carry larger loads. They would use GPS to help them navigate and facial

recognition to confirm the identity of a recipient. Smaller parcels can be handled by a single robot

while larger packages will be tag-teamed by an appropriate number of robots that self-configure to

the package’s unique dimensions. The Transwheel is still a concept design (Gray 2015).

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Source: Gray (2015)

Aerial Vehicles: Unmanned Aerial Vehicles/Autonomous Flying

Vehicles/Drones

� Unmanned aerial vehicles (UAVs) or drones in logistics. Although ‘hobby drones’ have become

popular with consumers, the adoption of UAVs in logistics is still in its early stages. This is largely due to

technological limitations (e.g., poor stability in rough weather), regulations (e.g., approval is required on a

case-by-case basis), and public concerns about the use of UAVs in densely populated areas. However, first

commercial tests (e.g., Google, Amazon, and DHL) have successfully demonstrated UAV potential, and key

regulatory bodies are expected to ease legislations for commercial UAV deliveries over the next few years

(DHL 2016).

Surveillance and logistics coordination. Surveillance of infrastructure can be supported by UAVs.

Equipped with cameras, they can monitor sites and assets to prevent theft and report suspected

damage or maintenance requirements. They can also be used to coordinate major logistics

operations on the ground (DHL 2016).

Rural delivery. Rural delivery using UAVs is attractive for remote regions that have limited logistics

infrastructure or are hazardous to access (e.g., islands during rough weather conditions, villages

located in mountain ranges). Logistics providers can set up emergency delivery services (e.g.,

medicines) for these communities (DHL 2016).

DHL Parcelcopter trial in Germany. First tests have demonstrated the future potential of UAVs

especially in rural delivery scenarios. DHL’s Parcelcopter, for example, has been successfully tested;

it delivered medications and other urgently needed goods to an island as well as to a remote

mountain region in Germany (DHL 2016).

Urban delivery. Urban UAV networks for first- and last-mile delivery will be required to handle single

shipments that cannot be achieved in an economical way with traditional delivery vehicles. By

potentially reducing the amount of vehicle movements, UAVs can provide traffic congestion relief to

densely populated cities. Each UAV can be prepared for flight along with its shipment at a logistics

hub or even directly at the retail store, and is likely to use fixed programmed routes to safely deliver

goods at designated drop-off points (DHL 2016).

Flying robotic taxi: Ehang trial in Las Vegas. A drone that can transport humans has been given the

go ahead to carry out trials in the US. The Ehang 184, which was first unveiled at CES 2016, is a small,

personal helicopter that can transport a single passenger. Ehang will start running tests in Las Vegas

later in 2016 in the hope that it could eventually be used as part of the state’s transport system. The

autonomous flying vehicle is electric-powered, and on one two-hour charge can fly at sea level for 23

minutes with a passenger and item of small luggage that weigh up to 100kg, according to the

company. When flying at greater heights it can travel up to 63 miles per hour for 10 minutes. To fly

the Ehang 184, all the passenger needs to do is enter their destination into an app on their phone. The

drone is then able to navigate the route and avoid obstacles. It can’t fly directly to any destination, but

hops from one Ehang landing spot to another. On landing, its propellers fold up so that it can fit into a

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single parking space designed for a car. At the moment the company has only developed a prototype

of the vehicle. Ehang is teaming up with the Nevada Institute for Autonomous Systems, a group

sponsored by the governor of Nevada, to develop and test the system (McGoogan 2016).

� Challenges

Unauthorized interception or hacking of UAVs (DHL 2016)

Privacy and safety concerns from the public (DHL 2016)

Integration of UAV traffic in crowded airspace networks (DHL 2016)

Regulatory restrictions (DHL 2016)

MOBILE INTERNET (MI) AND COMMUNICATION TECHNOLOGY

� Mobile Internet (MI). MI represents the combination of mobile computing devices (such as smartphones

and tablets), high-speed wireless networks, and associated applications. It is increasingly common for

vehicles themselves to feature devices capable of accessing the Internet as a standard feature. The MI is

supported in large part by the nation’s high-speed cellular 4G network and the growing coverage of

wireless-radio-based telecommunications mediums (such as Wi-Fi) in urban areas (Baker et al. 2016).

� MOBILE DEVICE features for transportation. The functionality of mobile devices, ranging from

smartphones and iPhones to tablets and GPS devices, has expanded. Because of the nature of the

environment in which they work, many logistics and transportation personnel are using multipurpose,

ruggedized or commercial-grade mobile devices because they allow more efficient management of the

supply chain while allowing managers to work from wherever they are located. The technologies involved

include mobile phones with: built-in cameras, handheld computers, tablets, barcode and label printers,

scanners, RFID tags, GPS, near field communications (NFC), voice recognition software, and shared

logistics networks (Robinson 2015a). Using smartphones and tablets for logistics processes is a current

industry trend. Sensor-equipped mobile devices are ideal for seamless and real-time monitoring and

controlling of logistics processes along the supply chain (DHL 2016).

Barcode scanning, image documentation of freight, and signature capturing on delivery. The

first successful use cases (e.g., barcode scanning, image documentation of freight, and signature

capturing on delivery) exploit the diverse technical capabilities of mobile devices and utilize cloud-

based software-as-a-service models (DHL 2016). Mobile solutions have, in various forms, been in

existence within the transportation industry for many years. For example, couriers might use mobility

to provide proof of delivery by scanning a package and having the customer sign electronically (Fleet

Owner 2016).

Wireless item identification with RFID transponders, and electronic scanning with a smartphone

camera. With the spread of NFC-compatible smartphones, new logistics uses will appear (e.g.,

identifying items wirelessly with RFID transponders, and scanning electronically with a smartphone

camera, eliminating costly conventional scanner systems) (DHL 2016).

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In-vehicle interface with cellular-based internet connections and onboard Wi-Fi: Routing

assistance. Right now, the MI is composed mostly of smartphones, tablets, and portable devices that

allow for access to the Internet from almost anywhere. However, it is increasingly common for newer-

model vehicles to feature cellular-based Internet connections and onboard Wi-Fi. This allows for the

expansion of vehicular based-services that can be provided through an in-vehicle interface. In addition

to simply accessing the Internet for things like entertainment, vehicle drivers and passengers will be

able to access web-enabled applications such as routing assistance and concierge services (Baker et al.

2016).

Crowdsourcing model. Recent examples of applications that package mobility as a service include

transportation network companies such as Uber and Lyft, which for the consumer take the form of a

web-based mobile-phone app. These systems generally work by connecting people in need of a vehicle

with available drivers and vehicles. Users select their location (and in some cases destination), and the

app finds an available driver and directs that driver to the users. The user is notified of the impending

arrival of his or her vehicle and driver, with locations being determined through GPS data generated by

the driver and user’s phones. Payment for the ride occurs through the app itself through a credit or

debit card. Drivers receive real-time routing information for directions. All of these services and their

associated components are supported by the MI, and services can be expected to improve with higher

levels of computing power in mobile devices and better integration between services. Improvements

are also likely to occur with the continued development of the fifth generation of mobile technology

(5G) (Baker et al. 2016). [NOTE: This model starts to be used in a business setting, notably in

omnichannel retail delivery. Passenger transport use case is causing disruption in the taxi industry.

Disruptions could be expected in freight transport use case as well.]

� MOBILE AND WIRELESS (CONNECTED) TECHNOLOGIES for continuous connectivity with drivers,

assets, and cargo. Mobile technologies, including communications, vehicle telematics, geographic

information systems (GIS) data, dynamic content, and mobile computing, offer an advantage their

predecessors [transportation and fleet management] didn’t have—continuous connectivity with drivers,

assets and cargo (Robinson 2015a). Mobile and wireless (connected) technologies are significantly

impacting every sector of the transportation industry; from connected vehicles, to fleet management, to

air, sea and land traffic control, to public transit, and alternative transportation options like car-sharing,

ride-sharing and bike-sharing, and much more (MTAM n.d.).

Communications. The cost, speed and reliability of mobile communications networks have improved

greatly in the past decade. Driven by consumer adoption of mobile technologies and relentless

competition amongst wireless providers, we now have low-cost networks capable of delivering vast

amounts of data cheaply, quickly and reliably (Manhattan Associates n.d., Robinson 2015a). Mobile

data coverage has increased, as network providers have invested substantially in 3G, 4G, LTE (long-

term evolution) and WiMAX infrastructures to deliver broadband speeds over any distance to any

location (Descartes n.d.).

Vehicle Telematics. Vehicle Telematics Sensors located throughout a vehicle now capture and

communicate a wide array of data to back-office systems. Examples of how these data can be used

include (Manhattan Associates n.d.):

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GPS information tells dispatch where the vehicle is located and is the basis for providing

accurate ETA [estimated time of arrival] information (Manhattan Associates n.d.).

It can be used to notify if a driver is off route or has otherwise deviated from plan (Manhattan

Associates n.d.).

Engine diagnostics provide insight into both power unit and driver performance. Information

such as idle time, fuel usage and driver shift patterns can be analyzed to determine how well the

driver is adhering to standard operating procedures and “best practices.” These data can also

serve as the foundation for a vehicle maintenance program based on operational need versus a

fixed maintenance schedule (Manhattan Associates n.d.).

Trailer sensors enable un-tethered trailer tracking, multi-compartment temperature monitoring

and alerting when a trailer door has been opened at an unauthorized location (Manhattan

Associates n.d.).

Connected vehicles. Examples of capabilities are:3

Schedule maintenance based on car health

Weather station on wheels

Share incident data (location, speed, video) with authorities

Send road conditions to maintenance authorities

Adapt car performance based on road condition and weather

Adapt car behavior based on adjacent cars’ history

Configure car based on driving patterns

Geographic Information Systems (GIS) data. The underlying quality of the geographic information

used to calculate travel times and optimize travel paths has greatly improved in the last 20 years.

Leading providers now offer mapping solutions that are tailored toward freight operations and include

information such as truck restricted routes, bridge height and weight limits, tolls and time of day/day of

week cognizant travel times (Manhattan Associates n.d.).

Mobile navigation solutions: Billing and routing accuracy. There is a common problem of

properly billing and pricing jobs without accurate information. Some companies calculate miles

from city center to city center, but what if the customer is located in a different area of the city or

an area difficult to navigate? Not every vehicle can travel on every road. Whether the problem is

heavy traffic, cargo restrictions or low bridges and other obstructions, the most logical route

might not be the most cost effective. Mobile solutions help with these issues by determining the

optimal route for the current day’s traffic conditions, detours and other issues; intelligently

choosing the ideal route for each shipment while logging the exact distance traveled so the

customer is charged a price based on actual miles rather than estimates. In fact, companies that

use mobile tracking solutions for how they bill their customers and pay their drivers will see

approximately an 8% reduction in cost, due to improved reporting accuracy (Fleet Owner 2016).

Dynamic Content. Content providers now capture and disseminate real-time and predictive traffic

information captured from en-route vehicles. These data can be used to provide more accurate ETA

3 Red Bend’s EVP Strategy, Products and Marketing Oren Betzaleli presents at BWC

(http://www.slideshare.net/redbend/connected-car-example-slide-9)

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[estimated time of arrival] information or even re-route vehicles around traffic incidents. Additionally,

dynamic content such as weather, fuel prices and third-party loads can be integrated into back-office

systems to enable more accurate and efficient dispatch and route planning decision support

(Manhattan Associates n.d.; Robinson 2015a).

Mobile Computing. Low cost yet powerful mobile devices ranging from ruggedized handhelds to

smart phones to tablets—running customizable pickup and delivery applications—enable processes to

be automated and digitized. Information that captures the essence of a customer interaction can then

be communicated back to the enterprise in real time and disseminated to the appropriate resource.

Examples of how this technology can improve operations include (Manhattan Associates n.d.; Robinson

2015a):

Capture of the consignee signature at point of delivery (Manhattan Associates n.d.; Robinson

2015a)

Identify OS&D situations and initiate claims process (Manhattan Associates n.d.; Robinson 2015a)

Scan product off the vehicle to eliminate delivery failures (Manhattan Associates n.d.; Robinson

2015a)

Codify and standardize communications to enable reporting and analysis (Manhattan Associates

n.d.; Robinson 2015a)

Manage regulatory compliance (Manhattan Associates n.d.; Robinson 2015a). Mobile technology

can be leveraged to automate vital information, such as: Hours of service (HOS), Hours per work

week, Fuel tax reporting, Fuel consumption, Engine and driver performance (Descartes n.d.). For

example, in the U.S. and Canada, commercial interjurisdictional carriers leverage International Fuel

Tax Agreement (IFTA) to pay fuel taxes based on the jurisdictions they travel to. Mobile

technology can track where the company vehicle has traveled, and the exact number of miles

logged in each state, and whether you are on a toll or non-toll road. This allows organizations to

stay in compliance with IFTA (Fleet Owner 2016).

DIGITALIZATION: 3D PRINTING, INTERNET OF THINGS (IOT),

MACHINE-TO-MACHINE (M2M) COMMUNICATION, BIG DATA

ANALYTICS, DEEP LEARNING

� Digitalization of logistics processes. Technologies such as big data analytics, Internet of Things, and self-

learning systems will continue to digitalize logistics processes, enabling new ways of increasing process

efficiency, enhancing interaction with customers, and driving new business models (DHL 2016).

Big data. Logistics is being transformed through the power of data-driven insights. Unprecedented

amounts of data can now be captured from various sources along the supply chain. Capitalizing on the

value of big data offers massive potential to optimize capacity utilization, improve customer

experience, reduce risk, and create new business models (DHL 2016).

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IoT. The Internet of Things (IoT) empowers smart objects to be active participants in self-steering,

event-driven logistics processes. Logistics is one of the major industries that will benefit from the

intelligent conjunction of information and material flows. It is estimated that by 2020, more than 50

billion objects will be connected to the Internet, presenting an immense $1.9 trillion opportunity in

logistics (DHL 2016).

Self-learning system/Machine-learning system. Strong advancements in algorithms,

computational power, and hardware are enabling new forms of machine learning applications in

logistics. Self-learning or ‘machine learning’ systems will become a game-changing enabler for

completely autonomous data-driven decision making and process optimization in logistics. With

minimal/no human intervention, a self-learning system will adapt and improve its algorithms as it

receives more data, improving its results over time (DHL 2016).

Big Data

� Big data era

Over 90% of data in the world was created in the past two years (Sellin 2015).

The total volume of data being captured and stored by industry doubles every 1.2 years (Sellin 2015).

It is estimated that by 2020 the amount of big data in existence will have grown from 3.2 to 40

zettabytes (1 zettabyte = 1,000,000,000 terabytes) (Sellin 2015).

� Digital big data: Four core interlinked technological developments. The collection and exploitation

of large data sets – so-called “Big Data”– is not new and is not linked to a single technological change. At

their core are four interlinked technological developments. These technologies enable near real-time use

and transmission of massive amounts of data (ITF 2015).

Ubiquitous data logging and sensor platforms. Extensive software event logging (and storage) and

the deployment of millions of sensing devices enable the real-time production of petabytes of data

globally (ITF 2015).

Real-time in-stream data analysis. Sophisticated algorithms and distributed computing capacity

(often hard-wired to sensor platforms) enable the real-time parsing and analysis of data as it is

produced. In-memory analysis is especially useful for extracting relevant data from unstructured

analogue video or audio streams.

New analytic frameworks. New techniques have emerged that allow efficient processing of very

large data sets within the constraints of available runtime computing capacity. Many of these

techniques have been released under open-source licenses free of commercial rights. This has greatly

accelerated their uptake. Map-reduce work processes (such as Hadoop or its derivatives) leverage

parallel processing by breaking up large and complex semi-structured and unstructured data sets into

more manageable subsets. They then allocate coordinated processing tasks to multiple distributed

servers. These algorithms are fully scalable and are not bound by having to formalise database

relationships ahead of storage and analysis. They can be applied to directly to the data, irrespective of

size, format and complexity. Nonetheless, they may not be sufficiently reactive to use in the context of

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instream data analysis. Other approaches have emerged that are specifically geared to the analysis

real-time streaming data and involve some form of in-memory processing (that is, analysis occurs

without data storage) (ITF 2015).

Advances in data storage. Dropping data storage costs have increased the ratio of retained to

generated data. This data includes information that, in the past, had seemed insignificant or trivial (e.g.

“digital dust”) and was therefore discarded. However, when analysed by sophisticated algorithms or

merged with other sources of contextual data, “digital dust” may provide important new insights. This

data is increasingly being stored remotely (away from the systems that produce it) in data centres that

may even be in another jurisdiction. Related to the development of remote data centres is the

emergence of “cloud” computing capacity that can be used to analyse large and real-time data sets.

The “cloud” refers to remote data storage centres as well as the suite of data transfer and networking

protocols that allow access to and analysis of distributed data as if it were located on a single server.

Not only does “cloud computing” deliver economies of scale in relation to data storage, management

and support costs, it also opens up new possibilities for ad-hoc and customisable access to computing

capacity on public cloud-based platforms (e.g. Amazon Web Services, Google Cloud Platform, etc.) (ITF

2015).

� Digital big data collection and analysis

Source: ITF (2015)

Digital big data sources. Big data can be found in many data streams—both inside and outside the

organization (e.g. distributors/customers, suppliers, and the logistics providers between each party

such as forwarders, contract warehousing, contract manufacturing, and customs brokers) (Inbound

Logistics 2015).

Big data for shippers. For shippers, structured data can be found in many places including

enterprise resource planning (ERP), transportation management systems, and planning and

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procurement systems. Unstructured data can also be found in data sources surrounding the

company, including financial data, weather data, or GPS data from trucks (Inbound Logistics 2015).

Big data for 3PLs. For 3PLs, key sources of big data will be pallets, cases, trailers with RF tags,

personnel with mobile devices, and B2B hubs for EDI transactions. Insights from these data are: (1)

Trailer tags. Insights into container transit times and dwell times, temperature, integrity of loads;

(2) Pallet/Case/SKU tags. Insights into transit and dwell times from source to destination — on

the road, in the yard, at a warehouse; (3) EOBRs. Insights into travel times, load/unload times, and

driver hours (Swaminathan 2012).

Big data for carriers. For carriers, big data will come from RF tags/sensors on assets (trailers, rail

cars, ships), EOBRs, and mobile devices (Swaminathan 2012).

Example of a big data information stream. (1) An order is received from a client or a purchase

order is sent to a vendor. (2) The Warehouse Management System (WMS) notifies the warehouse

of the product that needs to be shipped. (3) An electronic load tender is broadcast to multiple

carriers. (4) A carrier is selected based on price, time performance, loss and damage experience,

and billing accuracy. This information is gathered from prior shipments. (5) When the carrier picks

up the load, its terminal is notified of the pickup, so the product can be tracked. (6) The WMS

makes the appropriate adjustment to inventory. (7) An electronic bill of lading is prepared and

accepted by the carrier. (8) An advanced shipping notice is sent to the receiver. (9) A record of the

shipment is passed to the payment process to serve as an authorization for shipment to be paid,

and to accrue the expense until the freight bill is received. (10) The carrier submits its freight bill

via electronic data interchange, and it is audited, matched to the shipment authorization record,

and staged for payment (Inbound Logistics 2015).

Examples of “born digital” data (not analog data) (PCAST, 2014 cited in ITF 2015):

� Global Positioning System (GPS) or other geo-localised spatial data stamps (PCAST, 2014 cited

in ITF 2015)

� Time stamps and process logs (PCAST, 2014 cited in ITF 2015)

� Metadata regarding device identity, status and location used by mobile devices to stay

connected to various networks (GSM, Wi-Fi, etc) (PCAST, 2014 cited in ITF 2015) – Insights into

mobile application usage by customers, partners, and employees

� Data produced by devices, vehicles and networked objects (PCAST, 2014 cited in ITF 2015)

� Public transport card tap-ins or swipes and other data associated with portal access (badge, key

cards, RFID tags) or cordon passage (e.g. toll roads, congestion charging systems, etc) (PCAST,

2014 cited in ITF 2015)

� Commercial transaction data (credit card use and transaction records, bar-code and RFID tag

reading, etc.) (PCAST, 2014 cited in ITF 2015)

� Emails and SMS, metadata relating to phone calls (PCAST, 2014 cited in ITF 2015)

Fitness for analysis. Beyond questions of availability and collection costs, an important factor to

consider when selecting a data source is its fitness for analysis. Data extracted from a single source is

generally considered clean and precise. However, meaningful analysis of a single source depends

largely on the generating system’s ability to serve as a proxy for the phenomenon of interest. The

reality is that data is often “messy”, in that it is heterogeneous, “dirty” (includes incorrect, mislabelled,

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missing or potentially spurious data) and, in its native format, is incompatible with other data sources.

Part of the challenge lies in the fact that some data may be highly structured (for example, GPS latitude

and longitude data and commercial transaction data) facilitating rapid analysis while other data may

comprise highly unstructured data sets (emails, social media content, video and audio streams) and

therefore be more difficult and time consuming to analyse (ITF 2015).

Techniques for data analysis. The techniques for data analysis can be grouped into, but are not

limited to, the following categories (McKinsey Global Institute, 2011 cited in ITF 2015):

Data fusion: techniques to consolidate data produced by multiple sources, such as location data

produced by mobile phones and GPS-enabled vehicles (ITF 2015).

Data mining: techniques to extract patterns from large data sets, such as the relationships

between discrete nodes in a transportation network (ITF 2015).

Optimisation: techniques to reorganise complex systems and processes to improve their

performance according to one or more parameters, such as travel time or fuel efficiency (ITF 2015).

Visualisation: techniques used for generating images, diagrams, or animations to communicate

the results of data analysis, such as traffic maps. Visualisation techniques are used both during

and after data analytics to make sense of the information (ITF 2015).

� Potential exploitation of big data in transport and logistics. Big data has already begun to make

inroads in the logistics industry by turning large-scale data volumes into a valuable asset to boost

efficiency in areas such as capacity planning and vehicle route optimization. Moving forward, logistics

providers will need to master the integration of structured and unstructured data (social, images, video,

etc.) from multiple data streams to harness the full potential of big data. This coupled with the

advancement of analytics technologies will further unlock exciting new ways to monetize data-driven

operating and business models (e.g., anticipatory logistics) (DHL 2016).

Big data-based anticipatory logistics. Powered by big data-based predictive algorithms, anticipatory

logistics enables logistics providers to significantly boost process efficiency and service quality,

shortening delivery times by predicting demand before a request or order is even placed. In addition,

new predictive maintenance and supply chain risk concepts will further optimize logistics operations

(DHL 2016).

Predictive modelling for fleet selection. Another way trucking companies use data to save money

on fuel is by using predictive modeling to select fuel efficient trucks. One company was depending on

this data to help them make the right choice in selecting a new fleet of 50 trucks – a $6 million decision.

The predictive model used to determine the actual fuel economy of the trucks analyzed much more

than standard metrics. They combined data variables like driving behavior, fuel tank levels, load

weight, road conditions and much more. The detail of the data provided executives with a clear picture

of which trucks would provide the most fuel savings over time (Nemschoff 2014).

Big data for operational efficiency. Operational efficiency can be improved by using big data to

optimize service properties such as delivery time, resource utilization, geographical coverage, and to

increase speed and transparency in decision making (DHL 2016).

Real-time scheduling, optimize load sequence, predict ETA. For example, in transportation the

intelligent correlation of data streams (shipment information, weather, traffic, etc.) can enable

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real-time scheduling of assignments, optimization of load sequences, and ‘down-to-the-minute’

prediction of the estimated time of arrival (ETA) (DHL 2016).

Optimize shipping routes. One of the ways big data is saving trucking companies big money is

with fuel consumption. In some cases, mathematical models are used to optimize shipping routes.

By honing in on excessive driving routes, drivers can see a reduction of nearly one mile of driving

every day. Now, to most of us a mile doesn’t seem like much. For a company like UPS, a reduction

of one mile per day per driver would equal a savings of as much as $50 million a year in fuel

(Nemschoff 2014).

Truck fleets deliver visibility and efficiency. With onboard telematics, common carrier and

private trucking fleets can have visibility into the driving patterns of every truck — location data as

well as every shift, acceleration, brake application and idle time. For fleet managers, turning that

data into actionable intelligence and communicating it through the enterprise becomes a

monumental challenge. With in-cab smartphones and tablets — often ruggedized versions of

consumer-level devices — fleet operators can communicate critical information back through the

driver pool. Drivers can receive customized feedback on their driving performance, and several

companies are developing tools that will allow drivers to compete with one another on factors like

fuel efficiency. Customized, crowdsourced routing recommendations and geo-fencing help ensure

safe and accurate access to customer sites (Wollenhaupt 2016).

Strategic and operational resource capacity planning. Planning on strategic level involves the

long-term configuration of the distribution network, while operational level planning scales

capacities on a daily or monthly basis. In both cases, Big Data techniques improve the reliability of

the planning and enable logistics providers to optimally match demand and available resources

(Mikavica, Kostić-Ljubisavljević, and Đogatović 2015).

� Strategic capacity planning. In order to significantly increase predictive value, a much higher

volume and variety of data is exploited by advanced regression and scenario modeling

techniques. This results in a new quality of planning with greater forecast periods. Hence, the

risk of long-term infrastructure investments and contracted external capacities is reduced

(Mikavica, Kostić-Ljubisavljević, and Đogatović 2015).

� Operational capacity planning. Operational planning tasks are often based on historical

averages or even on personal experience. As a result, resource utilization is inefficient. Real-

time information about shipments is aggregated to predict the allocation of resources. This

data is automatically sourced from warehouse management systems and sensor data along the

transportation chain. Also, detection of changes in demand is derived from externally available

customer information. The prediction of resource requirements enables scaling capacity in

each location. In addition, it reveals upcoming congestions on routes or at transit points which

cannot be addressed by local scaling. The distribution network can become self-organizing

infrastructure using Big Data analytics (Mikavica, Kostić-Ljubisavljević, and Đogatović 2015).

Optimize last-mile delivery: Track delivery vehicles, identify delivery time patterns,

understand consumer behavior, determine suitable delivery vehicle. For the logistics

industry, the so-called ‘last mile’ between local distribution centres and the customer's home, is

where the challenges lie, as this is typically the slowest and least cost-effective part of their

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operations. Until recently, gauging the efficiency of shipping routes has been limited to knowing

when a package left a given depot, how far it travelled and the amount of time or fuel consumed

in getting it there. But thanks to the consumerization of IT tools through smartphones, GPS-

enabled devices, and IoT sensors and scanners — as well as the emergence of a fast, mobile Internet

to collect and transmit large amounts of data from anywhere — shippers can now have a near-

complete view of a given delivery route at any point in time. Data-collecting tools can be used to

track the progress of delivery vehicles and identify patterns in delivery times in order to better inform

route planning, they can also provide "transactional data" that gives a clearer picture of what

happens between a delivery truck and a customer’s doorstep. Many shippers want to know why

some drop-offs take longer than others, a question that was hard to answer in the past as there

was very little data available other than that provided from the truck itself. But advanced

geospatial information reveals that longer drop-offs tend to occur in the most densely populated

parts of a city, where many people live in high-rise apartments. This indicates delivery workers are

struggling to park, walking farther after parking, and climbing stairs when they get there. It can

also help logistics providers get a better picture of consumer behaviour, such as pinpointing

customers who are typically not at home during delivery hours. This is not a factor that is usually

factored into route planning, but can greatly impact the efficiency of operations. The WSJ notes

this is all information that can be used to help create more efficient routes, inform training

programmes and determine the most suitable delivery vehicles. For example, the data may prove

that multiple short-route deliveries on smaller vehicles, including bicycles, makes more sense than

bulk deliveries in large trucks (Kognitio 2016).

Example: Waze. Waze is a roadway navigation application for smartphones that relies on actively

and passively crowdsourced data to recommend routes and estimate time of arrival. Its acquisition

last year by Google signals that it may be a particularly advanced and promising smartphone

navigation app (US DOT 2014).

Big data for risk management. End-to-end supply chain risk management based on predictive

analytics can increase the resiliency of global supply chains. Big data can be used to mitigate risk by

detecting, evaluating, and alerting all potential disruptions on key trade lanes (e.g., growing port

congestion or high flood risks) (DHL 2016).

Big data for predicting mechanical failure. United Parcel Service (UPS) uses big data analytics to

reduce its maintenance costs. Because on-the-road vehicle breakdowns tend to be expensive and

disruptive to its operations, UPS used to replace certain parts on its trucks every two to three years.

However, this led to the replacement of perfectly good parts; the simplistic maintenance plan was

wasting money. Starting in the early 2000s, UPS began to use predictive analytics to identify those

parts that were in fact nearing failure and in need of replacement. Equipping the vehicle undercarriage

with an array of sensors, UPS identifies patterns in the sensor readings that corresponded with part

failure. Armed with a fleet of sensor-equipped vehicles and knowledge of the patterns that presage failure,

UPS is now able to predict part failures and replace parts only as needed (Mayer-Schönberger & Cukier

2013 cited in US DOT 2014).

Improve customer service. Improve customer experience (e.g. performing more precise customer

segmentation and optimizing customer service). Including the vast data resources of the public

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Internet, Big Data propels CRM (Customer Relationship Management) techniques to the next

evolutionary stage (DHL 2016).

Enhanced safety. Sensors and data storage/transmission capacity in vehicles provide new opportunities

for enhanced safety. Work is underway to harmonise standards regarding these technologies and

communications protocols in order to accelerate safety improvements and lower implementation costs

for conventional and, increasingly, automated vehicles (ITF 2015).

Continuous locating and tracking. Precise geo-referenced location data represents a large and

growing subset of Big Data as mobile devices and location-sensing technologies become ubiquitous.

Multi-platform sensing technologies—mobile phone handset, tablet or computer, GNSS (such as GPS)

receiver (e.g. in a car), Wi-Fi enabled device, video or localised machine tracking and logging devices

(e.g. smartcard public transport turnstiles, tolling systems)—are now able to precisely locate and track

people, vehicles and objects. The location technologies deployed in today’s mobile phones are

increasingly being built into vehicles, enabling precise and persistent tracking (ITF 2015).

Cellular-generated location data. Cellular location logs constitute large, complex and growing

data sets owned and exploited by cellular network operators in the course of ensuring seamless

phone communications. Cellular-generated location data – especially when linked to consumer

location and demographic profiles – represent a large potential source of revenue for operators,

e.g. by selling analyses of their own data or by selling data for analysis by third-parties. This data

may also be relevant for certain transport policy applications. For example, by matching

triangulated cell data with map data relating to transport networks in order to estimate traffic

flows and speeds. However, the differential precision across large-scale areas may be problematic

for some applications (ITF 2015).

Global Navigation Satellite System-based location data. Almost all new phones and all

smartphones integrate a Global Navigation Satellite System (GNSS) system microchip that allows

precision location information to be generated from one of two (and soon three) dedicated

satellite networks. The most common of these is GPS. In open areas with clean lines of sight to at

least 4 satellites, GPS accuracy can be up to 5 metres. This accuracy degrades, however, in areas

where GPS signals are disrupted by tall buildings or trees and inside of buildings. Assisted-GPS (A-

GPS) increases location accuracy by combining GPS location signals with cellular location data

providing sub-10 metre precision. Other forms of hybridised GPS location systems can provide

similar levels of precision by using Wi-Fi network signals (ITF 2015).

Wi-Fi based location data. Wi-Fi-enabled outdoor and indoor location sensing can deliver even

greater precision by tracking individual media access control addresses (MAC addresses – unique

identifiers allocated to individual devices such as laptop computers, mobile phones, tablets, Wi-Fi-

enabled cars, etc…) within a network of Wi-Fi routers and transponders. Wi-Fi-enabled devices set

to automatically connect to one or several networks regularly ping the available networks in order

to join to known ones. This ping contains the MAC address unique to each device thus enabling

device location and tracking. Sometimes this ping also includes data on previous Wi-Fi networks

the device has connected to. With sufficiently dense Wi-Fi router networks, very precise location

and movement data can be inferred. Wi-Fi network configurations and node locations are also

collected by numerous commercial operators delivering a suite of geolocation services (ITF 2015).

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Digital pictures. Digital pictures, especially those taken on GPS-enabled devices, often include

geographic coordinates of where the picture was taken in the file header. Picture archives and

other image posting sites can be mined for location histories that are associated with the pictures

an individual has taken. This history can be explicitly tied to an identifiable person when cross-

referenced with volunteered information on, for instance, social media sites. In aggregate, this

data can help track where persons tend to congregate by examining the co-location of pictures in

space and time (ITF 2015).

Source: ITF (2015)

� Big data challenges for logistics. The effective use of Big Data techniques introduces great advantages

in economy transformation, but also raises many challenges, including, among others, difficulties in data

capture, storage, searching, shearing, analysis and visualization. These challenges need to overcome in

order to exploit capabilities of Big Data.

Computer architecture. Computer architecture is one of the greatest challenges.

Data inconsistence and incompleteness. Another important challenge related to the Big Data

analysis includes data inconsistence and incompleteness, scalability, timeliness and data security.

Hence, data must be appropriately constructed and a number of preprocessing techniques, such as

data cleaning, data integration, data transformation and date reduction need to be applied in order to

alleviate noise and correct inconsistencies (Mikavica, Kostić-Ljubisavljević, and Đogatović 2015).

Accessibility. The knowledge discovery process puts the highest priority on the accessibility of Big

Data. In that sense, Big Data should be accessed efficiently and enabled to fully or partially break the

constraint of computer architecture. Direct-attached storage (DAS), network-attached storage (NAS),

and storage area network (SAN) are often used storage architecture (Mikavica, Kostić-Ljubisavljević, and

Đogatović 2015).

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Data security. Security problems include cloud-storage security (cybercriminal), intellectual property

protection, personal privacy protection, commercial secrets and financial information protection. Data

protection laws are already established in most developing and developed countries. For Big Data

related applications, data security problems are harder to deal with because of extremely large amount

of Big Data and much more difficult workload of the security (Mikavica, Kostić-Ljubisavljević, and

Đogatović 2015).

Cyber-security risks related to vehicle. The growing importance of network-based information

and other connected services in transport obviously poses increased cyber-security risks,

especially when networked-based systems interact directly or indirectly with primary control

systems of vehicles. A recent survey of potential cyber-attack vulnerabilities of US cars identified a

number of potential attack surfaces posing variable risks depending on vehicle and sub-system

design. It notes that manufacturers’ anticipation of risks and design response is uneven, especially

for secondary systems – including the distributed network of electronic control units (ECUs) within

vehicles. Convergence between sensor networks and vehicle control systems (e.g. those found in

automatic cruise control, lane keeping or parking assistance functions) poses particularly strong

risks in that sensor inputs can potentially be modified or spoofed leading to degraded or lost

control of vehicles (Miller & Valasek, 2014).

� Digitalization in the form of Big data for transport infrastructure

Potential values. Digitization, in the form of big data, in infrastructure networks could improve

forecasting, promote reliability, and increase efficiency (Neumann 2015). Compelling cases have been

made for the value of Big Data analytics for urban planning (via the convergence of high definition

geographic data with information regarding the observed or interpreted use of urban space by

citizens), intelligent transport (via visualisation and analysis of the real-time usage of transport networks)

and safety (via the processing of real-time data regarding vehicle operation and the surrounding

environment to avoid or minimise potentially dangerous conflicts) (ITF 2015). The latest developments

in wireless technologies as well as the widespread usage of sensors have led to the recent prevalence of

Intelligent Transportation Systems (ITS) for realistic and effective monitoring, decision-making, and

management of the transportation systems (USC 2016).

Barriers. Based on conversations with industry practitioners, we (Mckinsey) have identified three

significant barriers to leveraging information effectively to improve transport-infrastructure usage. All

three barriers are interdependent and therefore need to be addressed at the same time. Without

transparency, there is no way to build trust and achieve equitable sharing. Without equitable sharing

(and clear public benefits), regulators will not be sympathetic. Without responsible regulation, players

will be reluctant to make their data available (Neumann 2015).

Intensive data management. Considering the large size of the transportation data, variety of the

data (different modality and resolutions), and frequent changes of the data, the integration,

visualization, querying and analysis of such data for large-scale real-time systems are intrinsically

challenging data management tasks. Due to these challenges, current ITS applications only

support limited data monitoring and analysis capabilities (USC 2016).

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Lack of transparency. There is a lack of transparency. Transport infrastructure involves complex

networks with many participants. An airport, for example, will have dozens of different airlines,

ground-handling companies, and retailers, plus air-traffic control, customs, and the airport-

operating company itself. Each player collects its own data and does not necessarily want to share

it (Neumann 2015). Transparency regarding the nature of data and the conditions under which it

was collected is crucial for data-driven transport policy making. In this respect, the initial

recording and subsequent preservation of metadata plays an essential role in enabling data

interpretation and re-interpretation. This metadata may include information on data structure,

the context in which it was collected and how it was generated (e.g. its provenance). For sensor-

based data, provenance data is especially important as the type of sensor platform may affect the

representativeness of the data produced (ITF 2015).

Uncertain costs and other resource implications. A transition to increased use of big data

techniques for data capture, management and analysis would likely introduce additional costs or

resource requirements – costs that may or may not be off-set by other cost savings or benefits

generation – but it is not at all clear at this point. More likely still, using big data approaches may

require some investment in in-house training and/or outside consulting services, such as those

associated with mining data to identify patterns and profiles, developing predictive algorithms,

and incorporating the algorithms into existing expert systems or Decision Support Systems. Those

possibilities likewise depend on further elaboration of specific big data use cases as well as

consideration of public versus private and state/local versus Federal roles and responsibilities in

connected vehicle data capture overall. The fact that some agencies have begun to outsource

probe vehicle data capture, management and/or analysis to INRIX, IBM and others, suggests that

outsourcing of some functions may provide a favorable value proposition (US DOT 2014).

Cost and benefit sharing scheme. Another issue is how to divvy up the costs and benefits of

sharing information; different players do not always have the same goals. Airlines might want

faster transit times—for example, in order to minimize travel times for connecting passengers—

while retailers might prefer passengers to linger to increase store sales. Airports would prefer a

high utilization of assets, but they might value lower utilization to foster flexibility and enable

them to recover quickly after irregular events (Neumann 2015).

Regulatory constraints. There are regulatory constraints. Infrastructure in many cases is a natural

monopoly. Governments therefore have an important role to play—in ensuring that operations

are fair and cost-effective, and in creating a regulatory environment that allows data to be

collected and used while protecting confidentiality and privacy. But before that can happen,

competition and data-protection authorities need to be convinced of digitization’s benefits. One

sizable challenge would be to overcome users’ privacy concerns by clearly stating what data are

being collected, how they’re being used, and the ultimate benefit to consumers of cost-effective

solutions emerging from data insights (Neumann 2015). In addition, new models of public-private

partnership involving data-sharing may be necessary to leverage all the benefits of Big Data. An

increasing amount of the actionable data pertaining to road safety, traffic management and travel

behaviour is held by the private sector. Yet public authorities are still, and will likely continue to

be, mandated to provide essential services. Innovative data-sharing partnerships between the

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public and private sectors may need to go beyond today’s simple supplier-client relationship.

These new arrangements should not obviate the need for market power tests, cost-benefit

assessment and public utility objectives (ITF 2015).

Internet of Things (IoT) and M2M Communication

� IoT as subset of IoE

Internet of Things (IoT): IT and OT convergence. This refers to the use of sensors and data

communications technologies embedded in physical objects, including roadway infrastructure and

mobile devices, that enable those objects to be tracked, coordinated, or controlled across a data

network or the Internet. McKinsey & Associates estimates that there are currently 9 billion devices

around the world connected to the Internet and estimates that by 2025 that number will grow to

between 50 billion and 1 trillion (Baker et al. 2016). Increasingly, IoT represents the convergence of

information technology (IT) and so-called “operational technology” (OT). OT is characterized by more

specialized, and historically proprietary, industrial network protocols and applications that are common

in settings such as plant floors, energy grids, and the like (Macaulay, Buckalew, and Chung 2015).

Technology push for IoT adoption. Today, we see optimal conditions for IoT to take off in the

industry. There is a clear technology push through the rise of mobile computing, consumerization

of IT, 5G networks, and big data analytics, as well as a pull from customers who are increasingly

demanding IoT-based solutions. Combined, these factors are enabling logistics providers to adopt

IoT at an accelerating rate (Macaulay, Buckalew, and Chung 2015).

Internet of Everyting (IoE). IoT is a vital enabler of certain types of connection that together make up

what Cisco refers to as the “Internet of Everything” (IoE). IoE connections can be machine-to-machine

(M2M); machine-toperson (M2P); or person-to-person (P2P). IoE includes not just the networked

connection of physical objects, but also includes the links between people, process, and data (see Figure

2). IoT is most often equated to M2M connections. While IoT is one of IoE’s key technology enablers, so

too are cloud and big data, P2P video/social collaboration, mobility (including locationbased services),

and security. Together, they create the opportunity for unprecedented innovation and organizational

transformation. IoE is dissimilar from IoT in that it is not of itself a single technology transition, but

rather a larger platform for digital disruption comprised of multiple technologies. In this sense, IoT is a

subset of IoE (Macaulay, Buckalew, and Chung 2015).

24

Source: Macaulay, Buckalew, and Chung (2015)

� Value at stake of IoT. IoT will generate $8 trillion worldwide in Value at Stake over the next decade which

accounts for more than 42 percent of IoE’s overall Value at Stake. This value will come from five primary

drivers: innovation and revenue; asset utilization; supply chain and logistics; employee productivity

improvements; and enhanced customer and citizen experience. Supply chain and logistics alone are

estimated to provide $1.9 trillion in value, which is a promising indication of the untapped potential and

profits to gain from utilizing IoT in the logistics industry. The Value at Stake calculations stem from a

bottom-up economic analysis conducted by Cisco on dozens of IoT use cases, both public and private

sector. Each use case represents a business capability and resulting economic value brought about by

connecting the unconnected (Macaulay, Buckalew, and Chung 2015).

25

Source: Macaulay, Buckalew, and Chung (2015)

� IoT-enabled capability in logistics: Creating value through data captured from connected assets.

There is more to IoT than merely connecting assets. Connecting “things” is a means to an end. IoT creates

value through the data that can be captured from connected assets and the resulting insights that will

drive business and operational transformation. Thus the use of analytics and complementary business

applications (e.g., data visualization) is crucial if organizations are to capture and make sense of the data

generated from connected devices (Macaulay, Buckalew, and Chung 2015).

26

Source: Macaulay, Buckalew, and Chung (2015)

Operational efficiency: Traffic and fleet management. Optimizing asset utilization to drive greater

operational efficiency is at the very heart of IoT value; according to Cisco’s calculations, it accounts for

roughly 25 percent of the total Value at Stake from IoT. Vehicles are among the assets most ripe for

improved efficiency, especially in terms of traffic and fleet management. In-vehicle telematics and

vehicle-infrastructure integration have been vanguard applications in the use of sensor data. And

automotive manufacturers and transportation operators have invested substantially in connected

vehicles, including “recovery” systems, such as LoJack, and in-vehicle driver services, such as General

Motors’ OnStar. With IoT, traffic and fleet management applications herald a new wave of efficiency

gains (Macaulay, Buckalew, and Chung 2015). Intelligent transportation solutions can increase

transparency and integrity in the supply chain through innovative smart truck concepts (DHL 2016).

Predictive asset lifecycle management. In-vehicle telematics can collect data on movements

and idle time to maximize fleet and asset utilization. IoT can also be used to reduce vehicle

downtime via the prediction of asset failure and automated maintenance scheduling (DHL 2016).

One example is MoDe (Maintenance on Demand). This 2012 EU-backed research project between

Volvo, DHL, and other partners sought to create a commercially viable truck that autonomously

27

decides when and how it requires maintenance. The latest sensor technology was embedded in

key areas such as oil and damper systems to identify material degradation or damages. Data was

then transmitted first to a central unit in the truck via a wireless network, then to a maintenance

platform for analysis. The driver or maintenance crews were then alerted to potential problems.

The system was found to increase vehicle uptime by up to 30 percent and decrease potential

danger to truck drivers through constant condition monitoring of vehicles (Macaulay, Buckalew,

and Chung 2015).

Consolidating and optimizing route. Sensors can monitor how often a truck, container or ULD is

in use or idle. They then transmit this data for analysis on optimal utilization. Many logistics

vehicles today are already brimming with sensors, embedded processors, and wireless

connectivity. Sensors that measure the capacity of each load can provide additional insights

concerning spare capacities in vehicles on certain routes. IoT could then enable a central

dashboard that focuses on identifying spare capacity along fixed routes across all business units.

From there, it could recommend suggestions for consolidating and optimizing the route. This

would create fleet efficiencies, improve fuel economy, and reduce deadhead miles, which account

for up to 10 percent of truck miles (Macaulay, Buckalew, and Chung 2015). Trucks equipped with

telematics and sensors can measure load capacity and help identify historical spare capacity on

certain routes along with providing recommendations to decrease deadhead miles (Sunol 2016).

Safety and Security with analytics: Equipment and employee monitoring. Monitoring of

equipment and people to increase safety and security is another main value proposition of IoT,

particularly when combined with analytics (Macaulay, Buckalew, and Chung 2015).

Driver safety monitoring with proactive alert. From a safety and security perspective, sensors are

also able to track and detect damage, hazardous driving patterns, frequency of hard braking,

average speed, non-stop traveled distances, and driver fatigue – just to mention a few. Once

these events (e.g. are recorded, the system is able to alert decision makers and drivers if and when

necessary (Sunol 2016). IoT can also play an additional role in health and safety, preventing

potential collisions and alerting drivers when they need to take a break. Long-distance truck

drivers are often on the road for days in hazardous conditions. Cameras in the vehicle can monitor

driver fatigue by tracking key indicators such as pupil size and blink frequency. This is already

being applied by Caterpillar, the world‘s largest manufacturer of construction and mining

equipment, which is using this technology to keep sleepy truck drivers from getting into

accidents. If the solution senses the driver is losing attention on the road, it activates audio alarms

and seat vibrations. An infrared camera is capable of analyzing a driver‘s eyes through glasses and

in the dark (Macaulay, Buckalew, and Chung 2015).

Example: Union Pacific. Union Pacific, the largest railroad in the United States, uses IoT to predict

equipment failures and reduce derailment risks. By placing acoustic and visual sensors on tracks to

monitor the integrity of train wheels, the company has been able to reduce bearing-related

derailments, which can result in costly delays and up to $40 million in damages per incident. By

applying analytics to sensor data, Union Pacific can predict not just imminent problems but also

potentially dangerous developments well in advance. Train operators can be informed of

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potential hazards within five minutes of detecting anomalies in bearings or tracks (Macaulay,

Buckalew, and Chung 2015).

Location and condition monitoring: Visibility and Security. Location and condition monitoring

through IoT will provide a new level of transport visibility and security. Telematics sensors in trucks and

multi-sensor tags on items transmit data on location, condition (whether any thresholds have been

crossed), and if a package has been opened (to detect possible theft) (Macaulay, Buckalew, and Chung

2015). Current RFID technologies are already enabling IoT development through the use of

inexpensive stickers and tags placed on freight supplies and a multitude of other items such as fleet

vehicles that enable the automatic identification and tracking of the tagged items based on the

information contained within each tag (Baker et al. 2016).

Example: Agheera IoT platform for logistics. The IoT Platform for Logistics, Agheera, is an open

IoT platform that can combine telematics data from various IoT hardware devices for end-to-end

integrity control of supply chains. The platform merges multiple assets such as a connected swap

body or truck into one easy-to-use portal with worldwide accessibility and allows logistics

providers and customers to track all assets from their various devices in real-time (DHL 2016).

IoT in last-mile delivery: More dynamic and customized services. IoT in the last mile can connect

the logistics provider with the end recipient in exciting ways as it drives dynamic new business models

(Macaulay, Buckalew, and Chung 2015). Creation of more dynamic and customized delivery services for

customers (DHL 2016).

Optimized collection from mailboxes. One IoT-enabled use case for the last mile creates

optimized collection from mail boxes. Sensors placed inside the box detect whether it is empty and,

if so, transmits a signal that is then processed in real time. The delivery person can then skip that

box for collection, thereby optimizing daily collection routes. Start-ups such as Postybell have

created proximity sensors that detect when mail has been placed in a private mailbox and can also

monitor the wetness inside the mailbox. A delivery then triggers an alert to the recipient’s phone

via GSM. They can, for example, be reminded to check their mailbox or keep track of it while they

are on holiday. The same principle could be applied to the DHL Paketkasten or Parcelbox, which

are solutions to accommodate the e-commerce boom – users can install a personal parcel locker at

their front door. This is currently being launched in Germany. In the future, temperature-controlled

smart lockers eventually replace traditional mailboxes and ensure first-time every-time delivery of

parcels, groceries, and other environmentally sensitive goods (Macaulay, Buckalew, and Chung

2015).

Flexible delivery address. Another IoT use case arising from the proliferation of smart devices

and home products is the flexible delivery address. Today, most online consumers have the

choice of giving one preferred delivery address or selecting an alternative means of delivery, such

as to a parcel station. Many experiments have been conducted to provide more flexible delivery,

but one of the key issues has been in matching real-time delivery to the given addresses and time

slots in a cost-effective manner for the logistics provider. With IoT-enabled solutions, tagged

parcels offer more visibility to the recipient on when their parcel is expected to arrive and whether

a change in address is required — for example, if they are at work. If a delivery is planned during

the day, a customer could change the address to that of a neighbor who is home or at a workplace

29

in the vicinity. If it is unclear what a recipient’s schedule will be, smart-home products with

proximity sensors (e.g., smart lights) could sense if the recipient is at home and communicate to

the delivery person ahead of time if a delivery is possible. A flexible delivery address service could

also be initiated by the logistics provider. Applying predictive analytics to the recipient’s historical

mobile device location data (with the recipient’s opt-in to the service), the provider could request

confirmation of the expected delivery window and location (Macaulay, Buckalew, and Chung

2015).

IoT-enabled locking mechanism. IoT also introduces many improvements in physical security.

August Smart Lock, a popular consumer application of IoT (smartphone-controlled security), allows

users to provide access to their house to trusted parties remotely, using a smartphone app and an

IoT-enabled locking mechanism on their front door. This could also provide new delivery options

in last-mile logistics (Macaulay, Buckalew, and Chung 2015).

Automating business process and decision making. Automate business processes to eliminate

manual interventions, improve quality and predictability, and lower costs (Macaulay, Buckalew, and

Chung 2015). Higher operational efficiency and cost reduction due to automating decision making in

complex environments (DHL 2016).

Optimized people, systems, asset coordination. Optimize how people, systems, and assets work

together, and coordinate their activities (Macaulay, Buckalew, and Chung 2015).

� IoT applications in warehousing operations. In the warehouse, the widespread adoption of pallet or

item-level tagging — using low-cost, miniscule identification devices such as RFID — will pave the way for

IoT-driven smart-inventory management (Macaulay, Buckalew, and Chung 2015).

30

Source: Macaulay, Buckalew, and Chung (2015)

� Challenges. Until now, only a few IoT applications in logistics have had substantial business impact, due

to security concerns, an absence of standards in the fragmented logistics industry, and the consumer

market focus of recent IoT innovations. Looking ahead, large-scale IoT deployments will require new

‘logistics-ready’ solutions that ensure security and common connection standards (DHL 2016).

High levels of fragmentation within the logistics industry requires the development of a logistics IoT

standard (DHL 2016). Logistics is a typically low-margin and fragmented industry, especially in road

freight where there are tens of thousands of different suppliers with varying operating standards for

local, domestic, and international operations (Macaulay, Buckalew, and Chung 2015).

Data and security issues and concerns in the IoT-powered supply chain (DHL 2016). Connecting what

has been previously unconnected may, in some circumstances, highlight new security vulnerabilities.

As we interconnect IT and OT, for example, there may be new points of ingress for hackers,

cybercriminals, terrorists, mischief-makers and others who wish to do harm (Macaulay, Buckalew, and

Chung 2015).

IoT hardware needs to be further ruggedized for large deployments in logistics, especially in terms of

robustness and battery life (DHL 2016).

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� IoT and M2M communication in transport infrastructure. Traffic management systems are currently

capable of aggregating data from infrastructure-based devices to identify and measure traffic speed and

volume on city roads, providing real-time data on traffic conditions and assisting in incident response

activities. The IoT could change how transportation agencies manage their roadside assets, such as street

lights and intersection signals, by allowing the real-time monitoring and control of these assets from

remote locations (Baker et al. 2016).

Smart road

SmartAmerica Smart Roads. Smart Transportation Systems (STS) rely not only on improved

sensors and control devices and clever methods for managing the complex traffic flows but also on

computer networks and similar ‘cyber’ elements. The Smart Roads Cyber-Physical System

demonstration project demonstration will play out a scenario that shows how the collection of real-

time traffic enables the control the traffic flow on a segment of an interstate, near a major city.

When the system detects a trend towards congestion it starts metering the access ramps thus

improving the flow. The Smart Roads testbed integrates real-life data with high-fidelity simulations

of the road system and the networks. UC Berkeley and Vanderbilt University are participating

(SmartAmerica n.d.).4

US Connected Vehicle Program. Connected vehicle technologies provide the opportunity to

create an interconnected network of moving vehicular units and stationary infrastructure units, in

which individual vehicles can communicate with other vehicles (i.e. Vehicle-to-Vehicle, or V2V

communication) and other agents (e.g., a centralized traffic management center through Vehicle-to-

Infrastructure, or V2I, communication) in a collaborative and meaningful manner. The real-time

information provided by V2V and V2I improves drivers’ situational awareness and enhances safety

and efficiency of operating vehicles. It also improves the reliability of the traffic system through

online monitoring and dynamic management while providing data for both on-line operations

management and off-line planning applications. From a traffic operations perspective, a key focus

of connected vehicle systems is to enable coordinated strategies that improve the quality of flow

along highways and at intersections, including speed harmonization, coordinated cruise control

and queue warning. In general, the more vehicles are connected together, the greater the

opportunity for coordinated interventions to improve the quality and reliability of flow. In an urban

setting, connected vehicles technology enables more responsive operation of traffic controls,

especially traffic signals, and more efficient sharing of right of way by different types of vehicles,

including transit vehicles along priority corridors. As envisioned by the U.S. Department of

Transportation Connected Vehicles program, this connected environment serves three main

purposes: improving safety, enhancing mobility, and reducing emissions (Mahmasani 2016).

Smart road sign in EU. Car manufacturers such as Volvo and Ford have already got vehicles in

production that are capable of reading the new smart road signs, and the road signs are already in used

4 In December of 2013 the SmartAmerica Challenge was launched by Geoff Mulligan and Sokwoo Rhee, two White House

Presidential Innovation Fellows. The SmartAmerica Challenge is a White House Presidential Innovation Fellow project

with the goal to bring together research in Cyber-Physical Systems (CPS) and to combine test-beds, projects and

activities from different sectors, such as Smart Manufacturing, Healthcare, Smart Energy, Intelligent Transportation and

Disaster Response, to show tangible and measurable benefits to the US economy and the daily lives of American

citizens (SmartAmerica n.d.).

32

in France and Germany. The European Road Assessment Program said the new scheme could reduce

the number of accidents and deaths on EU roads. However there has been resistance from some EU

countries including the UK who’s Department for Transport insisted that the changes will not be

implemented due to the substantial cost involved (Flood 2013).

Traffic data for dynamic message signs. Transportation agencies and private partners using data

collected from sensors on smartphones to generate traffic data that can be relayed to dynamic

message signs (Baker et al. 2016).

Transport system and control devices monitoring. Transportation agencies using remote sensors to

monitor the health of traffic control devices, such as stop lights, and monitoring other aspects of the

transportation system such as road conditions (Baker et al. 2016).

3D Printing

� New market segments for logistics service providers. 3D printing creates new market segments and

value creation opportunities (e.g., digital warehouses, trusted service provision of 3D data hosting and

exchange). In addition, B2B 3D printing services can enable new logistics services especially in aftermarket

supply chains (the warehousing and distribution of spare parts). Instead of managing multiple

warehouses stacked with spare parts that are often rarely ordered, logistics providers can set up a global

3D printing infrastructure coupled with a software database of digital models. Spare parts can then be

printed only on-demand at the nearest 3D printing facility (e.g., a hub or airport) and be delivered to the

right location. This would reduce lead times and cut inventory costs (DHL 2016).

Example: 3D Printing on the Fly from Amazon. Amazon has patented the concept of mobile 3D

printing delivery trucks to deliver products even faster to the consumer. When a shopper orders a

selected product from Amazon, this triggers the nearest truck to 3D print and deliver the product to

the consumer, effectively removing the need for any storage (DHL 2016).

Challenges. (1) Restrictions on materials and speed of 3D printing could delay full adoption of this

technology. (2) Authors of digital design templates could be targeted by hackers and incur copyright

infringement. (3) Need to solve questions of liability in the event of faulty 3D printed products (DHL

2016).

Deep-learning/Self-learning/Machine-learning Systems

� Anticipatory self-optimization in last-mile delivery. Anticipatory self-optimization of processes can be

applied in last-mile delivery. Self-learning systems can monitor each step of the process to deliver

dynamic route planning tailored to each recipient’s daily routine (DHL 2016).

� Deciphering and correcting logistics data. Deciphering and correcting logistics data will become an

essential application field for self-learning systems in logistics. Intelligent systems can be trained to

decode written and spoken text (such as shipment information and the addresses on letters and parcels).

They can also recognize and memorize frequently occurring fault and correction patterns, as well as style

33

characteristics such as handwriting to decrease the amount of time spent on quality checks and manual

analyses (DHL 2016).

� Challenges

Algorithms are highly complex and require substantial future research (DHL 2016)

Requires large amounts of relevant data and massive computing power to create a machine learning

system (DHL 2016)

High setup costs may deter early adoption in logistics (DHL 2016)

CLOUD-BASED TECHNOLOGY

� Cloud Logistics. In recent years, logistics providers have begun to embrace cloud logistics as it enables

rapid, efficient, and flexible access to IT services for innovative supply chain solutions. Already today,

companies use cloud computing to gain ad-hoc access to local logistics IT specialists who, in turn, benefit

from easier access to global markets when their services run on cloud platforms. In future, the key focus

will be on ‘cloud readiness’, especially in terms of security as well as the technological performance of

cloud in real-time, large-scale operations (DHL 2016).

Modular cloud logistics platforms: Logistics-as-a-Service (LaaS). Ideal for complex, volatile

environments, cloud computing enables new ‘logistics-as-a-service’ (LaaS)-based business models.

Logistics providers can activate and deactivate customizable, modular cloud services on demand using

a pay-per-use approach. Modular cloud logistics platforms offer open, web-based access to a choice of

flexible, configurable on-demand logistics-related IT services that can be easily integrated into supply

chain processes (e.g., orders, billing, and track & trace services). Pay-per-use models allow small and

medium-sized logistics providers as well as larger companies to react more flexibly to market volatility,

paying only for the services they actually need and use, instead of having to invest in a fixed-capacity IT

infrastructure. Companies using cloud-based solutions can budget for them as operating expenditure

(DHL 2016).

Cloud-enabled transportation: Transportation control tower. Cloud computing improves ability to

control supply chain processes through digitized processes and easily shared real-time data. Cloud-

powered global supply chains virtualize information and material flows by moving all supply chain

processes into the cloud. Operating parts of complex, fragmented global supply chains, logistics

providers often deal with a variety of transactions taking place between multiple parties using different

warehouse and transport management systems. The cloud’s ability to coordinate this information into

one integrated view is a key enabler of a ‘control tower’ which coordinates and orchestrates the supply

chain and provides 360-degree management dashboards (DHL 2016).

One-stop B2B logistics brokerage platforms. One-stop B2B logistics platforms are two-sided online

marketplaces that match the demand for and supply of logistics services through digital interfaces. All

processes are centrally managed by the brokerage platform, are highly automated, and have a relatively

self-service character. Customers now have the opportunity to find the right carrier for their logistics

34

requirements by choosing from a wide range of large and small service providers; they profit from

better comparability and transparency of proposals, optimized price/performance ratios, and high

security through member certification and rating systems. Logistics providers can react by actively

driving or participating in these platforms, ensuring their services remain price competitive and as

flexible as possible (DHL 2016).

Example: Transporeon. Transporeon Cloud-based logistics platform for tendering, assigning

orders, booking time slots, tracking & tracing, and more. The platform simplifies transparency and

communication between all parties and reduces wait times and empty trips to streamline the

overall supply chain. More than 1,000 shippers, 55,000 carriers, and 150,000 users in over 100

countries are currently connected to the platform (DHL 2016).

Direct logistics marketplace. Cloud-based technology will transform the way the freight industry

works. Not only will freight carriers publish dynamic pricing for contractual business, they will be able to

quickly adjust to market demands and utilize yield management practices that airlines use. For the

past 20 years, third-party intermediaries [e.g. brokerage platforms] have grown exponentially, and in

the next two years, direct marketplaces will prosper and eliminate the need for those intermediaries.

Freight carriers will then publish capacity and match this data to shipments and vice versa. Even more

powerful will be the ability for freight carriers to use push technology to alert shippers in advance

where they have capacity and where they are offering incentives for shipments (Zintro 2013).

Next-gen freight exchange platforms. In logistics, freight exchange platforms already exist; however

in future these B2B marketplaces will greatly evolve to potentially take over the traditional tendering

and contract process for logistics services. Next-generation freight exchange platforms utilize the

latest connection technologies and interfaces to provide real-time interaction between small to even

the largest of logistics players. These platforms offer the chance for logistics providers to find

additional shipments to reduce empty running, and find fast and efficient additional cargo capacity.

They also improve collaboration between logistics companies (DHL 2016).

AUGMENTED REALITY

� Augmented reality (AR). AR enables the user to intelligently understand their surroundings by

integrating contextual information into their field of view through smart glasses. Now with the latest

developments in contextual computing, AR is continuing to emerge as an important logistics asset

capable of increasing process efficiency and quality, reducing risk, and lowering the stress of manual

handling (DHL 2016). AR for navigation and driver-assistance systems. Safer and smarter driving can be achieved for

vehicle operators by utilizing AR as the next generation of navigation and driver-assistance systems.

Windshields can be used as heads-up displays to project virtual layers of navigation information as if

this data is overlaid on the real environment. AR can also be used to highlight road hazards to the

driver (DHL 2016). For transportation, many vehicle AR technologies include software and sensors that

can aid in recognizing listed speed limits, identifying lane position, and assessing distance between

35

vehicles. Heads-up displays would project information to the driver via the windshield of the vehicle,

reducing the need to take one’s eyes off the road while driving. A significant issue with [heads-up

display] applications is distracted driving (Baker et al. 2016). AR for intelligent last-mile operations. Intelligent last-mile operations can use smart glasses for the

entire delivery process. Workers equipped with smart glasses can conduct completeness checks of

each shipment using object-recognition technology. AR can also be used to virtually highlight inside a

vehicle the optimal loading sequence of each shipment (taking account of route, weight, fragility, etc.).

On delivery, AR can be used for last-meter navigation to correctly locate entrances (DHL 2016). Vehicle AR technology for autonomous vehicles. The use of AR in vehicles may also increase the

amount of new technology and sensors used in cars. This could potentially speed the development

and availability of automated vehicles. AR systems (e.g. software and sensors that can aid in

recognizing listed speed limits, identifying lane position, and assessing distance between vehicles)

provide an avenue for auto manufacturers to introduce many of the required sensors and background

programs required for autonomous vehicle implementation, helping adoption rates as autonomous

vehicles make strides toward a consumer-grade product (Baker et al. 2016).

TRANSPORTATION MANAGEMENT SYSTEM

� Key features of TMS systems

Source: Robinson (2015b)

Optimization

36

Carrier contract management. The right TMS will simplify carrier contract management. When

you digitize contracts, all your team members can easily compare contracts and costs. Look for a

TMS that can: (1) Track all your individual terms and carrier agreements in real-time. (2) Alert you

when a contract is up for renewal, and include proper discounts in new contracts. (3) Display the

total costs, including accessorial charges, so you can easily select the lowest cost carrier. (4) Help

establish pricing. A TMS can calculate the exact cost of delivery, so you know what to charge your

customer (Supply Chain Digital 2014).

Execution

Integrated EDI. Integration of EDI within TMS Systems provide high end accuracy in data and

speed up critical information exchange between the businesses and at the same time contributes

greatly to create a green supply chain (Robinson 2015b).

Integrated audit and payment module. Auditing and payment systems allow a TMS to calculate

the freight charges, evaluate the service options and identify the areas of improvement. This

enables prompt acknowledgement and resolution of overcharge related issues with the carriers

and customers alike. Also links to ERP or any financial system allow efficient handling of payment

procedures (Robinson 2015b).

Integrated warehouse management system. Integrated WMS module within existing a

transportation management system provides real time information about warehouse facility such

as in and out inventories movement, material tracking, dispatching the shipments and many such

key performance indicators. Access to this real-time information improves the process decision

making (Robinson 2015b).

International logistics functionality. Look for these features: Multi-language interface screens;

Help selecting air or ocean carriers; Supports foreign currencies; Manages commercial invoices,

SEDs, NAFTA paperwork and other necessary shipping papers for international deliveries; and

Calculates value-added taxes, cross-border fees, and freight forwarding charges. A good TMS

keeps up with ever-changing trade agreements and embargoes. It can perform system checks,

assign export control classification numbers and complete other functions to assist with trade

management. By performing an embargoed countries check and restricted party screening, a TMS

can help protect you from inadvertently violating export control regulations (Supply Chain Digital

2014).

Performance management

Visibility. Visibility features within TMS systems, provide a detailed view of every step of the

transportation process thereby making them easier to manage (Robinson 2015b). Your TMS

should offer: Auto pick up; Integration via EDI with a carrier; Automatic notifications; and

Exceptions alerts (Supply Chain Digital 2014).

Mobile technology-enabled visibility in TMS. By merging and synchronizing mobile

technologies (communications, vehicle telematics, geographic information systems (GIS) data,

dynamic content, and mobile computing) into a single freight and mobile asset management

system, an enterprise now has unprecedented visibility into their shipping activities

(Manhattan Associates n.d.; Robinson 2015a).

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Track & Trace. This feature allows real-time exchange of shipment information between carrier,

distributor, and customer. Regular and competent sharing of shipment information across the

organization through web-based access, increases visibility, accuracy rate of tracking and

monitoring, and efficient management & reporting (Robinson 2015b).

Business Intelligence and Analytics. This feature involves proficient use of data warehousing,

dashboard functionality and report generation in standard or custom formats. It aims at

collecting, analyzing & summarizing supply chain and transportation metrics and data to utilize

them resourcefully for effective decision making, identifying needs & key areas of functional

enhancement, and evaluating the effectiveness of existing strategies (Robinson 2015b). Your TMS

should be able to capture transactions and data from multiple sources, and use analytics and big

data to: Minimize logistical costs; Reduce shipment delivery times; Develop performance metrics

and key performance indicators (KPIs); and Create computer models to predict supply chain issues

(Supply Chain Digital 2014). Enterprises need to be able to effectively manage vast amounts of

disparate data that are now flowing into the enterprise. Key capabilities required by an

orchestration and analysis layer are (Manhattan Associates n.d.; Robinson 2015a):

Exception Management. The ability of the application to identify exceptions as they occur and

initiate the appropriate issue resolution steps (Manhattan Associates n.d.; Robinson 2015a)

Data Accessibility. The ability for stakeholders to gain real-time, on-demand access to data

(Manhattan Associates n.d.; Robinson 2015a)

Planning. The ability to use historic data as a means to influence future planning (Manhattan

Associates n.d.; Robinson 2015a)

Reporting/Analytics. The ability to find insights that can be used to drive operational or strategic

change (Manhattan Associates n.d.; Robinson 2015a)

Mobile TMS

� TMS mobile applications

Carrier-centric solutions. Transportation Management System (TMS) Carrier Mobile Applications,

already available for the Apple iPhone and iPad, are giving carriers the ability to communicate directly

with a shipper’s TMS, even from the cab of their truck, to respond to shipment tender requests and to

report shipment status in real-time. This remote interface capability is especially important to shippers

needing to communicate with smaller carriers who do not have EDI systems, including for those

owner/operators whose dispatch offices ride in the passenger seats of their tractors. Once trained on

the iPhone application, shippers can contact carriers online who can immediately respond to tender

requests. Once booked on a load, carriers can interface through the TMS application to provide pickup

and delivery status updates. Tying this information to their own networks, shippers can feed real-time

data directly into their TMS systems. In addition to finding out immediately if carriers can accept their

loads, shippers gain greater visibility to in-transit inventory status from pickup to delivery. It’s a win-

win situation, as carriers are able to respond to tender requests in real time, and shippers can provide

enhanced communications to customers for improved service—resulting in fewer calls from customers

asking where shipments are (Skinner n.d.).

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Example: LeanLogistics. LeanLogistics, has introduced LeanTMS Mobile, a dedicated mobile

access platform that provides a driver-centric interface through which drivers provide visibility on

their activity within the LeanLogistics network, a global SaaS network of shippers, carriers and

trade partners. This streamlines the communication process, reducing administrative tasks for

carrier companies by enabling the driver to update the shipper directly. Through LeanTMS,

LeanLogistics is supporting mobile connectivity for its users and their carrier partners, allowing

updates to come directly from the source that is performing the activities and removing several

layers that are present in EDI or manual processes (MH&L 2015).

Shipper-centric solutions

Example: Ceresis mobile TMS. Wireless solutions are packaged as downloadable apps that let

users track and trace shipments, get rate quotes, and receive shipment notifications. This is true in

Cerasis mobile Transportation Management System (TMS) (Robinson 2015a). With Mobile TMS

version of the Cerasis Rater, you have at your fingertips to do the following from your smartphone

by logging in: (1) Rate a less-than-truckload freight shipment; (2) Track your freight shipment for

real time visibility, with the ability to scan barcodes for easy use; (3) View your last 7 days at a

glance, or drill down to view all shipments of both less-than-truckload and small package freight;

(4) View your dedicated Cerasis Account Team contact information allowing you to get in touch

with us at a moment’s notice; (5) Read all pertinent announcements concerning the Cerasis Rater

in the “Rater News” section; (6) One click access to Cerasis.com where you can keep up to date on

Freight, Transportation, Logistics, Manufacturing, and Supply Chain best practices and news

(Cerasis.com).

Source: Cerasis Mobile TMS Application

(http://cerasis.com/transportation-technology/mobile-tms-application/)

IoT connected TMS

� IoT connected TMS: GPS location data & Automated check call process. There is an increasing interest

in tying networks of sensors on trucks to TMS solutions. The ability to use a single TMS as an information

39

hub, rather than having to log in to multiple sites to access a widening array of information options is one

of the technology’s major benefits. TMS solutions are connecting with modes of transportation via

sensors fitted to freight vehicles, cargo, and even drivers. The potential for using this connectivity to

increase network efficiency, lower costs, and improve safety is staggering. Application area that offers

huge potential is the use of GPS location data to better manage freight transportation. Some shippers are

transmitting location data from truck-mounted sensors to our TMS. We [C.H. Robinson] have built logic

into the TMS that uses this data to monitor the location of vehicles. If a truck is running behind schedule,

the system can automatically create an alert and notify relevant parties of the potential delay. Automating

the check call process in this way means that shippers don’t have to employ a person to track multiple

carriers and call drivers when vehicles are behind schedule. Moreover, the automated process eliminates

human errors such as the keying in of inaccurate address information (Noll 2015).

Cloud-based TMS

� Cloud-based TMS: Pros and Cons. The cloud is gaining popularity as a platform for transportation

management systems that offers market-leading capabilities for optimizing supply chain operations while

reducing the implementation and maintenance costs typically associated with a TMS solution. In many

cases, a cloud-based TMS offers all of the same functionality as an on-premise version. The key difference

is: the cloud version offers a TMS application on a recurring subscription basis while the on-premise

solution is typically sold as a perpetual license that requires a large one-time upfront cost (Levin 2014).

Pros

Ease of Ownership. Cloud-computing takes some of the burden of TMS ownership off of the user

because the software provider is responsible for provisioning, hosting, maintaining the underlying

infrastructure and ensuring the reliability, availability and security of the application. In most

cases, disaster recovery and additional data security measures are offered and managed by the

TMS provider. With an on-premise TMS, a company can quickly overburden its internal IT staff in

supporting the application. Data backup and infrastructure security also can be expensive and

cumbersome (Levin 2014).

More Predictable Costs. As companies essentially pay rent for the opportunity to utilize the

functionality inherent in a cloud TMS, upfront capital expenditures related to hardware and

software are eliminated. Businesses also gain a better understanding of the costs associated with

utilizing an application while eliminating the need to employ an internal IT staff dedicated to

maintaining and troubleshooting the tool (Levin 2014).

Ease of upgrades. As a cloud TMS is enhanced with new features and functionality, these

updates become readily available to users through application upgrades, offering subscribers the

opportunity to adopt new innovative functionality should it provide value to one’s operation.

Companies with on-premise TMS deployments tend to upgrade the application less frequently

because of the time and costs associated with installing and testing the new version (Levin 2014).

Cons

Security. Even as many companies heighten the need for data security, many cloud TMS

applications utilize a multitenant architecture where a single version of the software runs on a

40

server that is shared by multiple customers. This type of architecture opens the possibility for

users to view another subscriber’s data, jeopardizing not only data security, but a business’

competitive position in the market. For instance, if one shipper gained access to the carrier rates

of another shipper, that information could be used to negotiate for needed capacity or better

rates with those carriers. Along similar lines, a TMS solution layered with technology (hardware,

software, servers) purchased from different 3rd party vendors could mean that multiple handlers

are involved in supporting the infrastructure. The more parties having access to the technology

increases the security risk (Levin 2014).

Minimal System Flexibility. Should a cloud TMS provider only offer a cloud deployment option,

a subscriber is locked into the cloud and will not have an opportunity to transition to an on-

premise version of the application if the need becomes apparent. Beware of cloud TMS solutions

that offer limited configurability or require configuration that can only be completed by the

vendor itself. Vendor configurations could come with a price tag. Also, it is important to

understand if one’s requirements can be met within the boundaries of the application’s out-of-box

functionality. Due to the access restrictions inherent in cloud-based solutions, customizations and

extensions to the base product most likely will not be an option (Levin 2014).

Forced Upgrades. While upgrades are an important element of innovation, many cloud TMS

providers force upgrades that can stifle an operation if imposed at the wrong time, such as during

a seasonal business high. TMS users also must stay abreast of the new content within the software

upgrade with the goal of capitalizing on the specific features that apply to one’s business process

(Levin 2014).

Layers of Risk. When discussing risk, most companies think about data security first, but vendor

viability is important. Will the TMS provider be around in the future or will another company buy

them out? Is this a new venture for them or do they have established technology? Look at the

history of the company and how long its products have been used. Does the provider have a solid

portfolio? See if you will be in good company with other customers. Also determine if the

company owns the technology or if it is purchased from someone else. A TMS infrastructure

layered with technology from different vendors is at risk to performing sub-optimally if the

provider does not fully understand or cannot support the technology on their own. The type of

hardware supporting the TMS application can also determine the performance of the applications.

Should a provider invest in commodity hardware instead of a strong infrastructure that offers high

processing speeds right out of the gate, companies may find that they need to upgrade hardware

at their own cost to achieve more acceptable results (Levin 2014).

� Emerging edge/fog computing for TMS. Current TMS solutions provide high levels of connectivity,

supply chain visibility, and fast data transfers by leveraging the power of cloud computing. The cloud

essentially removes latency issues caused by hard drive processing and raises the central system to a place

where each user can interact with it directly. In an industry with millions of moving parts, this presents a

major advantage. Shippers and carriers can communicate, track transportation vehicles, and anticipate

regulations at every point along the way. The internet of things (IoT) has created a constant push toward

even greater speed and efficiency has led to the rise of edge computing – also called fog computing – it

exists beyond the cloud (Endsley 2016).

41

Edge computing fueled by the rise of IoT. Cloud computing employs a centralized system but

relocates it for faster data processing. Edge computing, however, moves the active processing

components further from the center. Fueled by the rise of the IoT, it leverages nodes on the outskirts of

a network to manage data. Rather than working from a central system, it can perform on its own,

processing and sending data as appropriate. Without the need to connect to center, latency in

transactions is reduced (Endsley 2016).

Edge computing in TMS: Still work in progress. Edge computing in TMS would leverage smart

shipping containers and vessels. Some of these data points are valuable in streamlining shipping and

logistics. However, the sheer density of information is too much to transfer quickly. By moving this

processing to the edge, it would function at top speed, sending only necessary data back to centralized

systems. Employing new data makes predictive analytics more relevant. Edge processing allows

machines to respond to situations that arise and provides analysts with high-quality data for future

planning. Many think this would also represent a decreased security risk. By completing data

processing in closed devices, the chances of being hacked are less extreme than in open-air transfers.

The edge is still a work in progress. Many analysts believe a middle point will arise – a cloud-edge hybrid

system that makes the most of each. Some edge computing solutions will likely find their way into TMS

as one part of a complex system (Endsley 2016).

42

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