Abstract
To reduce the pollution and noise in the cities, the authorities encourage intermodality, notably through private cars and public transport combinations. The application of dissuasive measures such as urban tolls is an increasingly investigated solution. This paper proposes an agent-based simulation to assess the impact of an urban toll on intermodal trip behaviors (private car + public transport) in a city. The impact of the urban toll is modeled through a multinomial logit (MNL) model, which is used to estimate the modal choice for each agent. To avoid paying the toll tax, people (agents in our simulation) prefer to combine different modes of transportation, e.g., their private cars and public transport, by parking their vehicles in the park and ride facilities at the entrance to the city. Our experiments based on the MATSim platform infer that with 20 euros of toll tax, it is possible to reduce by \(20\%\) the use of the private car in the European Metropolis of Lille (MEL, France).
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Diallo, A.O., Lozenguez, G., Doniec, A., Mandiau, R. (2022). Agent-Based Intermodal Behavior for Urban Toll. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_33
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