Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32586
Research on Morning Commuting Behavior under Autonomous Vehicle Environment Based on Activity Method

Authors: Qing Dai, Zhengkui Lin, Jiajia Zhang, Yi Qu


Based on activity method, this paper focuses on morning commuting behavior when commuters travel with autonomous vehicles (AVs). Firstly, a net utility function of commuters is constructed by the activity utility of commuters at home, in car and at workplace, and the disutility of travel time cost and that of schedule delay cost. Then, this net utility function is applied to build an equilibrium model. Finally, under the assumption of constant marginal activity utility, the properties of equilibrium are analyzed. The results show that, in autonomous driving, the starting and ending time of morning peak and the number of commuters who arrive early and late at workplace are the same as those in manual driving. In automatic driving, however, the departure rate of arriving early at workplace is higher than that of manual driving, while the departure rate of arriving late is just the opposite. In addition, compared with manual driving, the departure time of arriving at workplace on time is earlier and the number of people queuing at the bottleneck is larger in automatic driving. However, the net utility of commuters and the total net utility of system in automatic driving are greater than those in manual driving.

Keywords: Autonomous cars, bottleneck model, activity utility, user equilibrium.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 488


[1] W. S. Vickrey, “Congestion theory and transport investment,” American Economic Review, vol. 59, no. 2, pp. 251–260, 1969.
[2] D. Acemoglu, A. Makhdoumi, A. Malekian, and A. Ozdaglar, “Informational Braess’ Paradox : The Effect of Information on Traffic,” Operations Research, vol. 66, no. 4, pp. 893–917, 2018.
[3] Z. Khan and S. Amin, “Bottleneck model with heterogeneous information,” Transportation Research Part B: Methodological, vol. 112, pp. 157–190, 2018.
[4] X. Guo and H. J. Sun, “Modeling the morning commuter problem with heterogeneous travelers based on bottleneck model,” Systems Engineering-Theory & Practice, vol. 38, no. 4, pp. 1003–1012, 2018.
[5] L. L. Xiao, H. J. Huang, and L. J. Tian, “Stochastic bottleneck model with heterogeneous travelers,” Journal of Transportation Systems Engineering and Information Technology, vol. 14, no. 4, pp. 93–98, 2014.
[6] R. Arnott, A. De Palma, and R. Lindsey, “Information and time-of-usage decisions in the bottleneck model with stochastic capacity and demand,” European Economic Review, vol. 43, no. 3, pp. 525–548, 1999.
[7] M. Fosgerau, “On the relation between the mean and variance of delay in dynamic queues with random capacity and demand,” Journal of Economic Dynamics and Control, vol. 34, no. 4, pp. 598–603, 2010.
[8] F. Zhang, W. Liu, X. Wang, and H. Yang, “A new look at the morning commute with household shared-ride: How does school location play a role?,” Transportation Research Part E: Logistics and Transportation Review, vol. 103, pp. 198–217, 2017.
[9] L. L. Xiao, T. L. Liu, and H. J. Huang, “On the morning commute problem with carpooling behavior under parking space constraint,” Transportation Research Part B: Methodological, vol. 91, pp. 383–407, 2016.
[10] W. Liu, F. Zhang, and H. Yang, “Modeling and managing morning commute with both household and individual travels,” Transportation Research Part B: Methodological, vol. 103, pp. 227–247, 2017.
[11] Z. C. Li, W. H. K. Lam, S. C. Wong, and A. Sumalee, “An activity-based approach for scheduling multimodal transit services,” Transportation, vol. 37, no. 5, pp. 751–774, 2010.
[12] X. Zhang, H. Yang, H. J. Huang, and H. M. Zhang, “Integrated scheduling of daily work activities and morning-evening commutes with bottleneck congestion,” Transportation Research Part A: Policy and Practice, vol. 39, no. 1, pp. 41–60, 2005.
[13] Z. C. Li, W. H. K. Lam, and S. C. Wong, “Step tolling in an activity-based bottleneck model,” Transportation Research Part B: Methodological, vol. 101, pp. 306–334, 2017.
[14] J. Kim and M. P. Kwan, “Beyond commuting: Ignoring individuals’ activity-travel patterns may lead to inaccurate assessments of their exposure to traffic congestion,” International journal of environmental research and public health, vol. 16, no. 1. p. 89, 2019.
[15] W. Liu, “An equilibrium analysis of commuter parking in the era of autonomous vehicles,” Transportation Research Part C: Emerging Technologies, vol. 92, pp. 191–207, 2018.
[16] V. A. C. van den Berg and E. T. Verhoef, “Autonomous cars and dynamic bottleneck congestion: The effects on capacity, value of time and preference heterogeneity,” Transportation Research Part B: Methodological, vol. 94, pp. 43–60, 2016.
[17] X. Yu, V. A. C. van den Berg, and E. Verhoef, “Autonomous Cars and Dynamic Bottleneck Congestion Revisited: How In-Vehicle Activities Determine Aggregate Travel Patterns,” SSRN Electronic Journal, 2019.
[18] K. A. Small, “The scheduling of consumer activities: Work trips,” American Economic Review, vol. 72, no. 72, pp. 467–479, 1982.
[19] Z. C. Li, W. H. K. Lam, and S. C. Wong, “Bottleneck model revisited: An activity-based perspective,” Transportation Research Part B: Methodological, vol. 68, pp. 262–287, 2014.