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DigitalizationPaul Davidsson (Professor, Computer Science, Malmö University)
Jan Persson (Associate Prof., Computer Science, Malmö University)
Andreas Tapani (Research Director, Traffic analysis and logistics, VTI)
Challenge and Approach
Major challenge: optimize the use of the available resources to meet the goals of society, including– providing the best possible service to the potential travellers– economical, environmental and social sustainability
Approach: make use of modern information technology to collect and analyse information and then make recommendations to – travellers– public transport operators– transport analysts and planners
On-‐going research – Digitalization
• K2-‐funded research projects– Agent-‐based Simulation of Passenger Transport in Urban Areas– Efficient Interchange Stations– Information-‐based Disturbance Management for Public Transport
• Examples of affiliated projects– Sustainable Traveling Through Dynamic Public Transportation
(Vinnova)– Data Innovation Arena: Using open data to develop innovative
services for people in motion (Vinnova)– National Postgraduate School for Intelligent Transport Systems
(Vinnova and Swedish Transport Administration)
Agent-‐based Simulation of Passenger Transport in Urban Areas – Problem
Implementation
Instruments• Different taxes, restrictions on vehicle use,
ticket prices, investments in infrastructure, bus routes and frequency, etc.
Transportation system issues• Congestion, emission, service quality,
coverage, etc.
Impact assessment of changes in policies and infrastructure
investments
Actual tests• Expensive• Time-consuming• Sometimes infeasible
Traditional models• Limited number of factors• Oversimplification • Neglecting interaction effects
Agent-‐based Simulation of Passenger Transport in Urban Areas – Approach
Decision making module
Cost Travel time
Social normsConvenience
Age Gender
Income Work&homeaddresses
Car / bike ownership
An agent-based modelwhere every traveler
is individually modeled
Choice of travel(mode, time, route, etc.)
Traveler’s characteristics
Cost Time
CO2 emission
Number of interchange
Walking/Cycling distance
Available travel options
Skåne-‐trafikenOpenAPI
Google maps
Agent-‐based Simulation of Passenger Transport in Urban Areas – Results
• A state-‐of-‐the-‐art analysis of relevant models• A questionnaire survey of the needs of the relevant stakeholders • A first version of the simulation model • An initial case study concerning public transport pricing and
commuting in the Malmö-‐Lund area (16 persons)
Three scenarios:• Half the price• Current prices• Double the price
Agent-‐based Simulation of Passenger Transport in Urban Areas – The future
• Model development: The agent-‐based model will be further developed based on the experience of the initial experiment and the results of the survey of the needs.
• Validation: A larger simulation study regarding commuting in the Malmö-‐Lund region. Possibly also case studies in other urban regions in Sweden.– Difficult to get actual data with sufficient precision, e.g., the
home and working addresses (from RVU). Maybe necessary to generate artificial populations.
• Long-‐term: A follow-‐up project to validate and apply the simulation model in different European urban regions is considered, possibly funded by H2020.
Efficient Interchange Stations – Problem
• Interchange stations are important in a high quality public transport system– Connects different modes and routes– High impact on passenger satisfaction
• How should the bus and tram terminal at interchange stations be designed?– High capacity to avoid congestion and queues ® Large terminal– Commercially valuable land– Short walking distances
• Need of good tools to evaluate this trade-‐off
® Small terminal
Wikipedia/Ilya Plekhanov
Efficient Interchange Stations – Approach
• A simulation model of combined bus and tram terminals– Discrete Event Simulation
• The model will evaluate a given layout in terms of its capacity• Applied to Norrköping’s new interchange station• A first step: A two-‐berth, off-‐street combined bus and tram
stop at Norrköping interchange station
Event 1
Event 2 Event 4 Event 7
Event 5
Event 3 Event 6
Efficient Interchange Stations – Results
• Two lanes, buses canovertake at departure– Performs marginally better at
the present situation– Greater differences at higher
inflow of vehicles, but toolong queues to be feasible
• One lane, no overtaking– Present design
• A simulation model capable of comparing two stop designs
Efficient Interchange Stations – The future
• The stop model will be further developed and expanded into a model of a bus and tram terminal
• Application of the model to Norrköping’s new interchange station• Evaluation of dynamic platform allocation• Long term: Optimize platform allocation, interaction between
station and surroundings, effects of/for different passenger groups, the station in the lager public transport system, innovative solutions for future public transport systems at the station…
Information-‐based Disturbance Management for Public Transport – Problem
Jan Persson and Åse Jevinger
Situation: Given disturbances in the public transport system, there are a number of potential actions that can be taken by multiple actors (including travellers)
Challenge:• Relevant actors need to have information related to the
disturbance when deciding what actions to take• Actors often need information about actions taken by other actors• Include the view of the traveller as an actor (i.e. being a decision
maker and a potential provider of information)• Adopt to the new law of travellers rights.
Information-‐based Disturbance Management for Public Transport – Approach
• Identifying missing information by different actors in situations of disturbances – Focus on short to mid range journeys– Multiple transport modes and operators typically involved– Focus on information needed for taking actions (e.g. re-‐planning
resource, take another route etc.)– Also include operators not directly involved (e.g. taxi and timetabled
substitute transports)• Mapping information needs to information availability• Interviews, participating in workshops and literature/report reviews
Information-‐based Disturbance Management for Public Transport – Results
• A map of the available information, including the information flows to/from the traveler and between different public transport actors
• Identified a rather large set of missing information (and problems) from the perspectives of the actors and the travelers
• The effects of the new law are also considered• Suggested concepts that may support the situation
– Check-‐in system (including intended end destination)
– Open information system• Suggested some rather straight forward solutions
to some problems
Travelers directed to other train
Information-‐based Disturbance Management for Public Transport – The future
• Further investigations of the suggested concepts• Further investigations of the effects of the new law• Traveller opinions• Information need of operators not directly involved in the
disturbance• Need for information processing• Information reliability
Some Publications – Digitalization
• Banafsheh Hajinasab, Paul Davidsson, Jan A. Persson, Johan Holmgren (2016) The requirements on transport models for estimating the effects of policies and infrastructure investments: A questionnaire study, 5th Nationella Transportforskningskonferensen.
• Banafsheh Hajinasab, Paul Davidsson, Jan A. Persson, Johan Holmgren (2016) Towards an Agent-‐based Model of Passenger Transportation. Multi Agent Based Simulation XVI, Lecture Notes in Computer Science, Vol. 9568, Springer.
• Banafsheh Hajinasab, Paul Davidsson, Jan A. Persson, Johan Holmgren (2016) Using on-‐line services for outsourcing the computations in transport simulation, International Symposia of Transport Simulation and the International Workshop on Traffic Data Collection and its Standardisation
• Banafsheh Hajinasab, Paul Davidsson, Jan A. Persson, Johan Holmgren (2015) Agent-‐based simulation of passenger transport in urban areas, 4th Nationella Transportforsknings-‐konferensen.
• Banafsheh Hajinasab, Paul Davidsson, Jan A. Persson (2015) Att beräkna styrmedelseffekter, K2 Working Papers 2015:4.
• Åse Jevinger and Jan A. Persson Information-‐based Disturbance Management for Public Transport -‐ Project report 1. K2 Working Papers 2016:15.