Multi-modal dynamic traffic assignment model with the addition of electric vehicles

doi:10.3969/j.issn.1674-8484.2021.04.013

Abstract

Abstract:

This paper proposed a multi-modal dynamic traffic assignment model based on travel behavior analysis to distinguish the transportation network status and to analyze the dynamic evolutionary process with the addition of electric vehicles (EVs). Established several travel behavior models for different travel groups based on a discrete choice theory. Proposed a multi-modal dynamic traffic assignment model and a corresponding algorithm based on the point queuing model, considering some factors like the network capacity, the battery surplus, and the charging-station service-level dynamically with doing a numerical example for verification. The results show that the factors, which impact transportation system significantly, include the EV State of Charge (SOC), the charging service fee, and the EV charging-pile number. The proposed scheme for public charging- facility capacity-allocation reduces the users’ cost by 3.61%, reduces the EV charging pile acquisition cost by 15.86%, reduces the CO₂ emission from transportation-system by 0.76%, increases the EV charging station operation income by 14.66%, and increases the level of service by 77.09%.

Key words: traffic engineering, electric vehicles (EVs), dynamic traffic assignment, variational inequality, travel behavior analysis

CLC Number:

U491.13

ZHANG Rui, YAO Enjian, ZHANG Yongsheng. Multi-modal dynamic traffic assignment model with the addition of electric vehicles[J]. Journal of Automotive Safety and Energy, 2021, 12(4): 540-550.

Figures/Tables 13

References 20

[1]	WANG Judith Y T, Ehrgott M. Modelling route choice behaviour in a tolled road network with a time surplus maximisation bi-objective user equilibrium model[J]. *Trans Res Part B Methodological*, 2013, 57(57):342-360. doi: 10.1016/j.trb.2013.05.011 URL
[2]	Smart J G, Schey S. Battery electricle vehicle driving and charging behavior observed early in the EV project[J]. *SAE Int’l J Alter Powertrains*, 2012, 1(1):27-33.
[3]	YANG Yang, YAO Enjian, YANG Zhiqiang, et al. Modeling the charging and route choice behavior of BEV drivers[J]. *Transp Res Part C*, 2016, 65:190-204. doi: 10.1016/j.trc.2015.09.008 URL
[4]	XU Min, MENG Qiang, LIU Kai, et al. Joint charging mode and location choice model for battery electric vehicle users[J]. *Transp Res Part B Methodological, 2017, 103*:68-86. doi: 10.1016/j.trb.2017.03.004 URL
[5]	李宗华, 翟钧, 王贤军, 等. 基于使用行为的电动汽车驾驶员里程焦虑模型[J]. 汽车安全与节能学报, 2021, 12(2):226-231.
	LI Zonghua, ZHAI Jun, WANG Xianjun, et al. Electric vehicle driver’s range anxiety model based on use behavior[J]. J Auto Safe Energy, 2021, 12(2):226-231. (in Chinese)
[6]	HE Fang, YIN Yafeng, Lawphongpanich S. Network equilibrium models with battery electric vehicles[J]. *Transp Res Part B*, 2014, 67(3):306-319. doi: 10.1016/j.trb.2014.05.010 URL
[7]	JIANG Nan, XIE Chi. Computing and analyzing mixed equilibrium network flows with gasoline and electric vehicles[J]. *Comput-Aided Civ Inf*, 2014, 29(8):626-641.
[8]	JIANG Nan, XIE Chi, Duthie J C, et al. A network equilibrium analysis on destination, route and parking choices with mixed gasoline and electric vehicular flows[J]. *Euro J Transp Logistics*, 2014, 3(1):55-92. doi: 10.1007/s13676-013-0021-5 URL
[9]	杨扬, 姚恩建, 王梅英, 等. 电动汽车混入条件下的随机用户均衡分配模型[J]. 中国公路学报, 2015, 28(9):91-97.
	YANG Yang, YAO Enjian, WANG Meiying, et al. Stochastic user equilibrium assignment model for electric vehicle under hybrid traffic condition[J]. Chin J Highway Transp, 2015, 28(9):91-97. (in Chinese)
[10]	WANG Hua, MENG Qiang, WANG Jing, et al. An electric-vehicle corridor model in a dense city with applications to charging location and traffic management[J]. *Transp Res Part B Methodological, 2021, 149*:79-99. doi: 10.1016/j.trb.2021.04.015 URL
[11]	XI Haoning, HE Liu, ZHANG Yi, et al. Differentiable road pricing for environment-oriented electric vehicle and gasoline vehicle users in the bi-objective transportation network[J/OL]. *Transp Lett*, 2021. (2021-05-05), https://doi.org/10.1080/19427867.2021.1919468.
[12]	郇宁, 姚恩建, 杨扬, 等. 电动汽车混入条件下随机动态用户均衡分配模型[J]. 交通运输工程学报, 2019, 19(5):150-161.
	HUAN Ning, YAO Enjian, YANG Yang, et al. Stochastic dynamic user equilibrium assignment model considering penetration of electric vehicles[J]. J Traffic Transp Engineering, 2019, 19(5):150-161. (in Chinese)
[13]	ZHOU Zhe, ZHANG Xuan, GUO Qinglai, et al. Analyzing power and dynamic traffic flows in coupled power and transportation networks[J]. *Renew Susta Energy Rev, 2021, 135*:(No. 110083).
[14]	ZHANG Rui, YAO Enjian. Mesoscopic model framework for estimating electric vehicles’ energy consumption[J]. *Susta Cities Society*, 2019, 47:1-7.
[15]	YAO Enjian, SONG Yuanyuan. Study on Eco-route planning algorithm and environmental impact assessment[J]. *J Intel Transp Syst*, 2013, 17(1):42-53. doi: 10.1080/15472450.2013.747822 URL
[16]	ZHANG Rui, YAO Enjian. Electric vehicles’ energy consumption estimation with real driving condition data[J]. *Transp Research, Part D Transp Envir*, 2015, 41:177-187. doi: 10.1016/j.trd.2015.10.010 URL
[17]	HAN Sangjin. Dynamic traffic modelling and dynamic stochastic user equilibrium assignment for general road networks[J]. *Transp Res Part B Methodological*, 2003, 37(3):225-249. doi: 10.1016/S0191-2615(02)00009-7 URL
[18]	杨励雅, 李娟. 居民出行链、出行方式与出发时间联合选择的交叉巢式Logit模型[J]. 北京大学学报(自然科学版), 2017, 53(4):722-730.
	YANG Liya, LI Juan. Cross-nested logit model for the joint choice of residential location, travel mode and departure time[J]. Acta Sci Natu Univ Pekinensis, 2017, 53(4):722-730. (in Chinese)
[19]	秦焕美, 关宏志, 殷焕焕. 停车收费价格对居民出行方式选择行为的影响研究:以北京市居民小汽车、公交、出租车选择行为为例[J]. 土木工程学报, 2008, 41(8):93-98.
	QIN Huanmei, GUAN Hongzhi, YIN Huanhuan. A study of the effect of parking price on the mode of inhabitant trip behavior:With the cars, public transit and taxi in Beijing as an example[J]. Chin Civil Eng J, 2008, 41(8):93-98. (in Chinese)
[20]	YANG Liu, WANG Yuanqing, HAN Sunsheng, et al. Urban transport carbon dioxide (CO₂) emissions by commuters in rapidly developing cities: The comparative study of Beijing and Xi’an in China[J]. *Transp Res Part D Transp Envir*, 2019, 68:65-83. doi: 10.1016/j.trd.2017.04.026 URL

路段编号	自由流时间 / s	通行能力/ (辆·h^-1)
1	126	3 838
2	153	2 731
3	68	3 735
4	60	4 498
5	90	3 838
6	78	3 598
7	102	3 598
8	86	3 735
9	66	3 598
10	99	3 838
11	117	3 838
12	153	3 838
13	72	3 735
14	114	4 498
15	216	3 838
16	105	3 598
17	900	40
18	900	52
19	900	16
20	21 600	10

路段编号	自由流时间 / s	通行能力/ (辆·h^-1)
1	126	3 838
2	153	2 731
3	68	3 735
4	60	4 498
5	90	3 838
6	78	3 598
7	102	3 598
8	86	3 735
9	66	3 598
10	99	3 838
11	117	3 838
12	153	3 838
13	72	3 735
14	114	4 498
15	216	3 838
16	105	3 598
17	900	40
18	900	52
19	900	16
20	21 600	10

起终点	路段构成
(O1, D)	路径1：3-6-9-12-15 路径2：3-8-13-16 路径3：1-4-9-14
(O2, D)	路径4：4-9-12-15 路径5：4-9-14
(O3, D)	路径6：5-10-15

起终点	路段构成
(O1, D)	路径1：3-6-9-12-15 路径2：3-8-13-16 路径3：1-4-9-14
(O2, D)	路径4：4-9-12-15 路径5：4-9-14
(O3, D)	路径6：5-10-15

出行方式	变量	参数估计
出行方式	变量	人群1	人群2	人群3	人群4
地铁	出行时间 / min	-0.086^**	-0.149^**	-0.066^**	-0.085^**
地铁	票价 / 元	-0.456^**	-0.342^**	-0.443^**	-0.207^**
公交车	出行时间 / min	-0.086^**	-0.149^**	-0.066^**	-0.085^**
公交车	票价 / 元	-1.230^**	-0.451^**	-1.406^**	-0.927^**
燃油汽车	出行时间 / min		-0.149^**		-0.085^**
	费用 / 元		-0.165^**		-0.089^**
	常量				-0.506^**
电动汽车在途充电	出行时间 / min			-0.066^**	-0.085^**
	费用 / 元			-0.195^**	-0.329^**
	剩余电量 / %			1.360	1.490^**
	消耗电量 / %			-0.141^**	-0.070^**
电动汽车终点充电	出行时间 / min			-0.066^**	-0.085^**
	费用 / 元			-0.195^**	-0.329^**
	剩余电量 / %			3.860^**	4.540^**
	消耗电量 / %			-0.141^**	-0.070^**