[1] Li Manman, , Lu Jian, et al. Network traffic flow evolution with battery electric vehiclesand conventional gasoline vehicles [J]. Journal of Southeast University (English Edition), 2019, 35 (2): 213-219. [doi:10.3969/j.issn.1003-7985.2019.02.011]

Copy

Network traffic flow evolution with battery electric vehiclesand conventional gasoline vehicles()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

35

Issue:

2019 2

Page:

213-219

Research Field:

Traffic and Transportation Engineering

Publishing date:

2019-06-30

Info

Title:

Network traffic flow evolution with battery electric vehiclesand conventional gasoline vehicles

Author(s):

Li Manman¹;  2;  3;  Lu Jian¹;  2;  3 ;  Sun Jiahui⁴;  Tu Qiang³

¹Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China
²Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China
³School of Transportation, Southeast University, Nanjing 210096, China
⁴Xi’an Aerospace Power Test Technology Institute, Xi’an 710100, China

Keywords:

battery electric vehicles;  constrained path;  reinforcement learning;  bounded rationality;  traffic dynamics

PACS:

U121

DOI:

10.3969/j.issn.1003-7985.2019.02.011

Abstract:

In order to investigate the effect of the use of battery electric vehicles on traffic dynamics, the valid paths of electric battery vehicles are defined and a check-based method is proposed to obtain them. Then, assuming that travelers only focus on their past travel experience, a day-to-day traffic assignment model is established based on reinforcement learning and bounded rationality. In the proposed model, the Bush-Mosteller model, a reinforcement learning model, is modified to calculate path choice probability according to bounded rationality. The modified model updates the path choice probability only if the gap between expected travel time and perceived travel time is beyond the cognitive threshold. Numerical experiments validate the effectiveness of the model and show that traffic flows can converge to the equilibrium in any case of cognitive thresholds and penetration rates of battery electric vehicles. The cognitive threshold has a positive influence on the variation of traffic flows while it has a negative influence on the differences between traffic flows. The adaptation of battery electric vehicles leads to the poor performance of the traffic system.

References:

[1] He X Z, Guo X L, Liu H X. A link-based day-to-day traffic assignment model[J].Transportation Research Part B: Methodological, 2010, 44(4): 597-608. DOI:10.1016/j.trb.2009.10.001.
[2] He F, Yin Y F, Lawphongpanich S. Network equilibrium models with battery electric vehicles[J]. Transportation Research Part B: Methodological, 2014, 67: 306-319. DOI:10.1016/j.trb.2014.05.010.
[3] Pearre N S, Kempton W, Guensler R L, et al. Electric vehicles: How much range is required for a day’s driving?[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(6): 1171-1184. DOI:10.1016/j.trc.2010.12.010.
[4] Smith M J. The stability of a dynamic model of traffic assignment: An application of a method of lyapunov[J]. Transportation Science, 1984, 18(3): 245-252. DOI:10.1287/trsc.18.3.245.
[5] Friesz T L, Bernstein D, Mehta N J, et al. Day-to-day dynamic network disequilibria and idealized traveler information systems[J]. Operations Research, 1994, 42(6): 1120-1136. DOI:10.1287/opre.42.6.1120.
[6] Zhang D, Nagurney A. On the local and global stability of a travel route choice adjustment process[J]. Transportation Research Part B: Methodological, 1996, 30(4): 245-262. DOI:10.1016/0191-2615(95)00034-8.
[7] Nakayama S, Kitamura R, Fujii S. Drivers’ learning and network behavior: dynamic analysis of the driver-network system as a complex system[J]. Transportation Research Record: Journal of the Transportation Research Board, 1999, 1676(1): 30-36. DOI:10.3141/1676-04.
[8] Zhang W, He R C. Dynamic route choice based on prospect theory[J]. Procedia—Social and Behavioral Sciences, 2014, 138: 159-167. DOI:10.1016/j.sbspro.2014.07.191.
[9] Rossetti R J F, Liu R H. An agent-based approach to assess drivers’ interaction with pre-trip information systems[J]. Journal of Intelligent Transportation Systems, 2005, 9(1): 1-10. DOI:10.1080/15472450590912529.
[10] Li X Y, Li X M, Yang L R, et al. Dynamic route and departure time choice model based on self-adaptive reference point and reinforcement learning[J]. Physica A: Statistical Mechanics and its Applications, 2018, 502: 77-92. DOI:10.1016/j.physa.2018.02.104.
[11] Wang W, Guo X C. Transportation engineering[M]. Nanjing: Southeast University Press, 2011.(in Chinese)
[12] Jiang N, Xie C, Waller S T. Path-constrained traffic assignment[J]. Transportation Research Record: Journal of the Transportation Research Board, 2012, 2283(1): 25-33. DOI:10.3141/2283-03.
[13] Wei F F, Ma S F, Jia N. A day-to-day route choice model based on reinforcement learning[J]. Mathematical Problems in Engineering, 2014, 2014: 1-19. DOI:10.1155/2014/646548.
[14] Di X, Liu H X. Boundedly rational route choice behavior: A review of models and methodologies[J]. Transportation Research Part B: Methodological, 2016, 85: 142-179. DOI:10.1016/j.trb.2016.01.002.
[15] Zhang R, Yao E J, Yang Y. Degradable transportation network with the addition of electric vehicles: Network equilibrium analysis[J].PLoS One, 2017, 12(9): e0184693. DOI:10.1371/journal.pone.0184693.

Memo

Memo:

Biographies: Li Manman(1991—), female, Ph.D. candidate; Lu Jian(corresponding author), male, doctor, professor, lujian_1972@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.51478110), Postgraduate Research & Practice Innovation Program of Jiangsu Province(No.KYCX18_0139).
Citation: Li Manman, Lu Jian, Sun Jiahui, et al. Network traffic flow evolution with battery electric vehicles and conventional gasoline vehicles[J].Journal of Southeast University(English Edition), 2019, 35(2):213-219.DOI:10.3969/j.issn.1003-7985.2019.02.011.

Common functions