Time Organization for Urban Mobility Decongestion: A Methodology for People’s Profile Identification

Authors: Yassamina Berkane, Leïla Kloul, Yoann Demoli

Abstract:

Quality of life, environmental impact, congestion of mobility means, and infrastructures remain significant challenges for urban mobility. Solutions like car sharing, spatial redesign, eCommerce, and autonomous vehicles will likely increase the unit veh-km and the density of cars in urban traffic, thus reducing congestion. However, the impact of such solutions is not clear for researchers. Congestion arises from growing populations that must travel greater distances to arrive at similar locations (e.g., workplaces, schools) during the same time frame (e.g., rush hours). This paper first reviews the research and application cases of urban congestion methods through recent years. Rethinking the question of time, it then investigates people’s willingness and flexibility to adapt their arrival and departure times from workplaces. We use neural networks and methods of supervised learning to apply a methodology for predicting peoples’ intentions from their responses in a questionnaire. We created and distributed a questionnaire to more than 50 companies in the Paris suburb. Obtained results illustrate that our methodology can predict peoples’ intentions to reschedule their activities (work, study, commerce, etc.).

Keywords: Urban mobility, decongestion, machine learning, neural network.

References:

[1] T. Oguchi, “Redesign of transport systems on highways, streets and avenues,” IATSS Research, vol. 32, no. 1, pp. 6–13, 2008.
[Online]. Available: https://www.sciencedirect.com/science/article/pii/S0386111214601953
[2] D. Metz, “Developing policy for urban autonomous vehicles: Impact on congestion,” Urban Science, vol. 2, no. 2, 2018.
[Online]. Available: https://www.mdpi.com/2413-8851/2/2/33
[3] A. Samaha and H. Mostofi, “Predicting the likelihood of using car-sharing in the greater cairo metropolitan area,” Urban Science, vol. 4, no. 4, 2020.
[Online]. Available: https://www.mdpi.com/2413-8851/4/4/61
[4] T. Litman, “Evaluating carsharing benefits,” Transportation Research Record, vol. 1702, pp. 31–35, 01 2000.
[5] E. Uhlemann, “Introducing connected vehicles
[connected vehicles],” Vehicular Technology Magazine, IEEE, vol. 10, pp. 23–31, 03 2015.
[6] K. A. Marczuk, H. Soh, C. L. Azevedo, D.-H. Lee, and E. Frazzoli, “Simulation framework for rebalancing of autonomous mobility on demand systems,” 2016.
[7] M. Pavone, Autonomous Mobility-on-Demand Systems for Future Urban Mobility. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015, pp. 399–416.
[Online]. Available: https://doi.org/10.1007/978-3-662-45854-9 19
[8] L. J. Basso, C. A. Guevara, A. Gschwender, and M. Fuster, “Congestion pricing, transit subsidies and dedicated bus lanes: Efficient and practical solutions to congestion,” Transport Policy, vol. 18, no. 5, pp. 676–684, 2011.
[Online]. Available: https://www.sciencedirect.com/science/article/pii/S0967070X1100014X
[9] E. G¨uresen and G. Kayakutlu, “Definition of artificial neural networks with comparison to other networks,” in WCIT, 2011.
[10] E. G¨ures¸en and G. Kayakutlu, “Definition of artificial neural networks with comparison to other networks,” Procedia CS, vol. 3, pp. 426–433, 12 2011.
[11] G. Guo, H. Wang, D. Bell, and Y. Bi, “Knn model-based approach in classification,” 08 2004.