|Table of Contents|

[1] Sun Chao, Cheng Lin, Li Dawei, Ma Jie, et al. Stochastic network equilibrium modelwith reliable travel time confidence level [J]. Journal of Southeast University (English Edition), 2017, 33 (3): 330-334. [doi:10.3969/j.issn.1003-7985.2017.03.012]
Copy

Stochastic network equilibrium modelwith reliable travel time confidence level()
可靠旅行时间置信水平下随机网络均衡模型
Share：

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

Volumn:
33
Issue:
2017 3
Page:
330-334
Research Field:
Traffic and Transportation Engineering
Publishing date:
2017-09-30

Info

Title:
Stochastic network equilibrium modelwith reliable travel time confidence level
可靠旅行时间置信水平下随机网络均衡模型
Author(s):
Sun Chao;  Cheng Lin;  Li Dawei;  Ma Jie;  Tu Qiang
School of Transportation, Southeast University, Nanjing 210096, China
孙超;  程琳;  李大韦;  马捷;  凃强
东南大学交通学院, 南京 210096
Keywords:
user equilibrium;  reliable travel time;  acceptable travel time difference;  confidence level;  quasi method of successive average
用户均衡;  可靠旅行时间;  可接受旅行时间偏差;  置信水平;  拟相继平均算法
PACS:
U491
DOI:
10.3969/j.issn.1003-7985.2017.03.012
Abstract:
In order to ensure on-time arrival when travelers make their trips, the stochastic network assignment model under uncertainty of travel time is investigated. First, based on travelers’ route choice behavior, the reliable travel time confidence level(RTTCL), which is the probability that a trip arrives within the shortest average travel time plus the acceptable travel time difference, is defined. Then, a reliability-based user equilibrium(RUE)model, which hypothesizes that for each OD pair no traveler can improve his/her RTTCL by unilaterally changing routes, is built. Since the traditional traffic assignment algorithms are not feasible to solve the RUE model, a quasi method of successive average(QMSA)is developed. Using Nguyen-Dupuis and Sioux Falls networks, the model and the algorithm are tested. The results show that the QMSA algorithm can rapidly converge to a high accuracy for solving the proposed RUE model, and the RUE model can provide a good response to travelers’ behavior in the stochastic network.
为了让交通出行者能够准时到达目的地, 对出行时间不确定性下的随机网络分配模型进行了研究.首先根据出行者出行行为, 定义了可靠旅行时间置信水平(RTTCL), 即出行者在可靠旅行时间(最短旅行时间加上可接受的旅行时间偏差)范围内到达目的地的概率;然后建立了基于可靠性的用户均衡(RUE)模型, 即没有出行者可以通过单方面改变自己的出行路径来提高自己的RTTCL.由于传统的交通分配算法无法求解建立的RUE模型, 因此设计了一种拟相继平均算法(QMSA).将建立的模型和算法分别在Nguyen-Dupuis和Sioux Falls网络上进行了测试, 结果表明, 算法能够很快收敛到较高精度, 随机交通网络中人们的出行行为与建立的RUE模型一致.

References:

[1] Abdel-Aty M A, Kitamura R, Jovanis P P. Exploring route choice behavior using geographic information system-based alternative routes and hypothetical travel time information input [J]. Transportation Research Record, 1995, 1493: 74-80.
[2] Lam T C, Small K A. The value of time and reliability: measurement from a value pricing experiment[J].Transportation Research Part E: Logistics and Transportation Review, 2001, 37(2): 231-251. DOI:10.1016/s1366-5545(00)00016-8.
[3] Franklin J, Karlstrom A. Travel time reliability for Stockholm roadways: Modeling mean lateness factor [J]. Transportation Research Record: Journal of the Transportation Research Board, 2009, 2134: 106-113. DOI:10.3141/2134-13.
[4] Bell M G H, Cassir C. Risk-averse user equilibrium traffic assignment: An application of game theory[J]. Transportation Research Part B: Methodological, 2002, 36(8): 671-681. DOI:10.1016/s0191-2615(01)00022-4.
[5] Szeto W, O’Brien L, O’Mahony M. Generalisation of the risk-averse traffic assignment [J]. Transportation and Traffic Theory, 2007, 17: 127-153.
[6] Xu H, Zhou J, Xu W. A decision-making rule for modeling travelers’ route choice behavior based on cumulative prospect theory[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(2): 218-228. DOI:10.1016/j.trc.2010.05.009.
[7] Lo H K, Luo X W, Siu B W Y. Degradable transport network: Travel time budget of travelers with heterogeneous risk aversion[J]. Transportation Research Part B: Methodological, 2006, 40(9): 792-806. DOI:10.1016/j.trb.2005.10.003.
[8] Wu X. Study on mean-standard deviation shortest path problem in stochastic and time-dependent networks: A stochastic dominance based approach[J]. Transportation Research Part B: Methodological, 2015, 80: 275-290. DOI:10.1016/j.trb.2015.07.009.
[9] Nie Y M. Multi-class percentile user equilibrium with flow-dependent stochasticity [J]. Transportation Research Part B: Methodological, 2011, 45(10): 1641-1659. DOI:10.1016/j.trb.2011.06.001.
[10] Chen A, Zhou Z. The α-reliable mean-excess traffic equilibrium model with stochastic travel times[J]. Transportation Research Part B: Methodological, 2010, 44(4): 493-513. DOI:10.1016/j.trb.2009.11.003.
[11] Xu X, Chen A, Cheng L, et al. A link-based mean-excess traffic equilibrium model under uncertainty[J]. Transportation Research Part B: Methodological, 2017, 95: 53-75. DOI:10.1016/j.trb.2016.10.018.
[12] Szeto W Y, O’Brien L, O’Mahony M. Risk-averse traffic assignment with elastic demands: NCP formulation and solution method for assessing performance reliability [J]. Networks and Spatial Economics, 2006, 6(3/4): 313-332. DOI:10.1007/s11067-006-9286-7.
[13] Watling D. User equilibrium traffic network assignment with stochastic travel times and late arrival penalty[J]. European Journal of Operational Research, 2006, 175(3): 1539-1556. DOI:10.1016/j.ejor.2005.02.039.
[14] Wang J Y T, Ehrgott M, Chen A. A bi-objective user equilibrium model of travel time reliability in a road network[J]. Transportation Research Part B: Methodological, 2014, 66: 4-15. DOI:10.1016/j.trb.2013.10.007.
[15] Sun C, Cheng L, Zhu S, et al. Multi-criteria user equilibrium model considering travel time, travel time reliability and distance [J/OL]. Transportation Research Part D: Transport and Environment, 2017. http://www.sciencedirect.com/science/article/pii/S1361920916304928. DOI:10.1016/j.trd.2017.03.002.
[16] Ji X, Ban X J, Li M, et al. Non-expected route choice model under risk on stochastic traffic networks [J/OL]. Networks and Spatial Economics, 2017. https://link.springer.com/journal/11067. DOI:10.1007/s11067-017-9344-3.
[17] Nguyen S, Dupuis C. An efficient method for computing traffic equilibria in networks with asymmetrictransportation costs [J]. Transportation Science, 1984, 18(2): 185-202. DOI:10.1287/trsc.18.2.185.
[18] National Research Council. Highway capacity manual[M]. Washington, DC, USA: Transportation Research Board, 2000.

Memo

Memo:
Biographies: Sun Chao(1990—), male, graduate; Cheng Lin(corresponding author), male, doctor, professor, gist@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No. 51608115, 51578150, 51378119), the Natural Science Foundation of Jiangsu Province(No. BK20150613), the Scientific Research Foundation of Graduate School of Southeast University(No. YBJJ1679), the Scientific Innovation Research of College Graduates in Jiangsu Province(No. KYLX15_0150), the China Scholarship Council(CSC)Program.
Citation: Sun Chao, Cheng Lin, Li Dawei, et al. Stochastic network equilibrium model with reliable travel time confidence level[J].Journal of Southeast University(English Edition), 2017, 33(3):330-334.DOI:10.3969/j.issn.1003-7985.2017.03.012.
Last Update: 2017-09-20