Strategic Network Modelling for Passenger Transport Pricing

E.-S. Smits

Research output: ThesisDissertation (TU Delft)

12 Downloads (Pure)

Abstract

In the last decade the Netherlands has experienced an economic recession. Now, in 2017, the economy is picking up again. This growth does not only come with advantages because economic growth demands more from the transport system. Congestion is increasing again, the capacity of the train system is now insufficient during peak hours, and the world faces environmental challenges that are partly due to emissions caused by travellers. These negative effects worsen as travellers make rational choices, which could be undesirable from a system, or social welfare, perspective. For example, car drivers do often not choose public transport options, because it costs them more effort; however, if they choose public transport options, then the system improves since congestion and emissions will reduce. Or another example, if travellers choose to avoid peak hours, they might not arrive at their desired time, but then they do not contribute to peak hour congestion or crowding. In addition, the capacity of the transport system is more effectively used if travellers spread out over the day.
Passenger transport pricing can be an incentive for travellers to change their choices, and can therefore be used to mitigate congestion, emissions, and other undesirable effects. Passenger transport pricing is the umbrella term for measures that make passengers pay for their travels. Traditional pricing measures are for example: fuel excise taxes, public transport fares, and periodical registration fees for vehicles. More innovative measures are cordon charges (e.g., in London, Stockholm, and Singapore), special tolling lanes, and peak avoidance projects. When such an innovative measure has different prices for times of the day, and for different locations (i.e., it is time- and space-differentiated), travellers’ choices related to route, mode and departure time can be influenced. By changing these choices, the overall performance of the transport system can improve. Travellers have differences regarding time valuation, preferred departure or arrival times, and car ownership. Therefore, a measure can become even more effective if it also allows to differentiate amongst characteristics of travellers.
However, innovative pricing measures have not been implemented widely across the globe, despite their potential to reduce congestion and emissions. This is primarily due to lack of public and political support. The Netherlands has experienced decades of political discourse and many failed proposals. Low public support did not contribute to (political) agreement either, because it has always fuelled the discussion with dissenting opinions. In the process of designing policies and making decisions, strategic planning models usually estimate (or forecast) the effects of the policy. The preferences of travellers and the transport system are captured by mathematical equations. Such models are always a simplified representation of reality. To apply them to asses pricing measures, they should capture the underlying mechanisms that are important for transport pricing as realistic as possible.
This dissertation identifies disadvantages of current strategic network models for passenger transport pricing and provides methodological advances to resolve them. This is done with a holistic approach that combines game theory, discrete choice analysis, traffic flow theory, and tranport economics into one modelling framework. This framework has many sub-models and provides a toolbox for analysts to determine the effects of innovative pricing schemes. The basic principle for each tool is to make them realistic (so that the results are credible for decision makers), and computationally efficient. The latter means that many different pricing schemes can be computed within reasonable time. By providing the methodological advances, that are briefly discussed in the next sections, this dissertation aims to improve public and political support. For example, the preferences of multiple stakeholders can be considered, the possible conflicts between them can be identified, and solutions based on concepts that aim to resolve these conflicts can be computed.
Original languageEnglish
QualificationDoctor of Philosophy
Supervisors/Advisors
  • van Arem, B., Supervisor
  • Bliemer, Michiel, Supervisor
  • Pel, A.J., Advisor
Thesis sponsors
Award date16 May 2017
Publisher
Print ISBNs978-90-5584-222-3
DOIs
Publication statusPublished - 2017

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