ANALYSIS TOOL TO PROCESS PASSIVELY-COLLECTED GPS DATA FOR

COMMERCIAL VEHICLE DEMAND MODELLING APPLICATIONS

Bryce Sharman &Matthew Roorda

University of Toronto

Presentation for the TRB - SHRP2 Symposium: Innovations in Freight DemandSeptember 15, 2010, Washington DC

Presentation Outline

1. Motivation2. Data3. Data Analysis Methods4. Preliminary Results5. Conclusions6. Future Work

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Shortcomings of Existing Commercial Vehicle Survey Data

• A freight data survey was conducted in 2006 by University of Toronto researchers

• Small sample size (n=600)• Survey limited to one suburban region outside of

Toronto• Low survey response rate (25%)• GPS add-on revealed differences between reported

and observed behavior • Single day observations only (practical limit for

response burden)• Cost of better data collection is very high

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Benefits of Supplementing Travel Survey Data Using GPS Data

• GPS data provide precise and continuous spatial and temporal information about a large number of vehicles for long periods of time.

• Many firms already subscribe to GPS tracking services to monitor their vehicle fleets.

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Research Goals

• Use GPS data to develop a model for forecasting urban commercial vehicle tours, incorporating dynamics of business operations over time.

• Develop analysis procedure and computer software to process GPS data such that it is suitable for developing a disaggregate travel demand model

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Provider: Xata Turnpike Global Technologies Inc.

• Provides fleet management services to > 300 firms, that own > 30,000 trucks

• GPS location tracking – routing, stop dwell time

• Engine diagnostics– speed, braking, fuel consumption, idling

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Database for this Study

• 77 Firms• 1618 Vehicles• 91 Days: April 1, 2009 – June 30, 2009• 147,238 vehicle days• ~ 7 million GPS motion points• 308,575 stops identified by GPS units

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Study Area

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

GPS Resolution

• Xata Turnpike is tracking vehicles for fleet monitoring, not travel demand surveys

• Data resolution– 500 m intervals between GPS points– Distance is extended to 1 or 2 miles as the

vehicle reaches freeway speeds ( > 60 mph)– Stop detection threshold: 5 minutes

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Internal/External Stops

• All GPS points are recorded within study area.

• When vehicle leaves study area, GPS points are recorded until first stop.

• When vehicle enters study area, GPS points are recorded after last stop prior to entering the area.

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Data Cleaning

• False-positive stop removal: – Infeasible that truck is making a delivery,

service or "other" stop. (E.g. bad congestion on freeways)

• False-negative stop addition: – Time interval between subsequent GPS

motion points shows that a stop must have occurred.

• Removal of uninteresting trips: (E.g. Repositioning truck within the depot)

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Identifying Repeat Destinations

• Why? – When GPS trip ends are linked, then repeated travel

behavior to locations can be analyzed• When commercial vehicles repeatedly make

deliveries to a customer, the GPS unit does not record exactly the same coordinates.

• Differences due to: – GPS error – Choice of loading bay or parking spot.

• Research – use spatial clustering techniques to best predict which GPS stops are for the same destinations

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Example Clustering -- One Firm in Toronto CBD (3 months, 7 trucks)

Driver logs were obtained from this firm to test the performance of various methods

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Clustering Method:

• Found issues due to very different scales of land parcel sizes

• Factories, warehouses and truck yards can occupy very large areas

• Testing different algorithms found that a two-step clustering approach worked the best.1. Cluster using Ward’s Hierarchical

Agglomerative Clustering method aiming to form reasonably compact clusters

2. Combine any two clusters whose median point lies within the same land parcel

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Identifying the Depot

• Firm identities and attributes not provided with GPS data

• Identification of depots is important to distinguish visits to a firm’s own location vs. visits to customers and suppliers

• Using the number of visits to the location and the average time spent as determining attributes

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Tour Creation

• Tours are automatically created when a vehicle visits a depot location or a location outside of the study region.

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Comparison of Vehicle Ownership

• Toronto region survey (2006)– Avg. of 4.4 vehicles per firm (single-unit

trucks and tractors)• GPS database (77 firms)

– Avg. of 21 vehicles per firm• This difference is expected since

transportation and larger retail firms are expected to show a preference for using fleet management services

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Comparison of Stop Dwell Times

0-4.99 5-9.99 10-19.99

20-29.99

30-44.99

45-59.99

60-119.99

120-179.99

180+0

5

10

15

20

25

30

35

40

45

50

Peel Region Survey (all vehi-cles)

Peel Region Sur-vey (Tractor with 1 and 2 Trailers)

Preliminary GPS Results

Stop Dwell Time (minutes)

Perc

enta

ge o

f Sto

ps

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Conclusions

• Research focused on creating a tool to analyze GPS data recorded for the customers of one fleet management company.

• Tasks include data cleaning, clustering stops into destinations, depot identification and tour creation

• Goal is to use this processed GPS data to develop commercial travel demand models.

Motivation

Data

Data Analysis Methods

Preliminary Results

Conclusions

Future Work

Envisioned Travel Demand Models and Analyses from GPS Data

1. Model of the dwell time at a stop2. Model of the number of days between visits

to the same destination3. Analysis of travel variability (how

representative is the travel on one day of other days)

4. Tour generation model – May use stochastic or deterministic (VRP) approaches. Ideally tour generation will also be specified over a multiple-day time period.