Manage morning commute problem for household travellers with stochastic bottleneck capacity

被引:0
|
作者
Lin, Boyu [1 ,2 ]
Liu, Qiumin [3 ]
Jiang, Rui [1 ,2 ]
Li, Xingang [1 ,2 ]
机构
[1] Beijing Jiaotong Univ, Sch Syst Sci, Beijing, Peoples R China
[2] Beijing Jiaotong Univ, Hebei Key Lab Future Urban Intelligent Traff Manag, Beijing, Peoples R China
[3] Beijing Transport Inst, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Stochastic bottleneck model; household travellers; staggering school and work start time; departure time choices; TIME VARIABILITY; CONGESTION; MODEL; CHOICE; EQUILIBRIUM; INFORMATION; ECONOMICS; SCHEDULE; LOCATION; BEHAVIOR;
D O I
10.1080/23249935.2025.2478305
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
This paper investigates the morning commute problem with household travellers under stochastic bottleneck capacity. Adults drop children at school before going to work, considering preferred arrival times and uncertainty. Their departure time choices follow the Wardrop's first principle to minimise the expected travel cost. All possible equilibrium departure patterns, and the boundary conditions are derived analytically. The impacts of the school-work start time difference, the degradation ratio of capacity and the degradation probability on the expected total travel cost (TTC) and on the expected total queueing cost (TQC) are analysed, respectively. Three optimal solutions for school-work start time difference are proposed. Policymakers can adjust the school-work start time difference to balance TTC and TQC. The results of the stochastic and deterministic models are also compared. Ignoring uncertainty always underestimates TTC, but TQC might be overestimated. This study enhances our understanding of the morning commute problem with household travellers under uncertainty.
引用
收藏
页数:45
相关论文
共 50 条
  • [1] Stochastic bottleneck capacity, merging traffic and morning commute
    Xiao, Ling-Ling
    Liu, Ronghui
    Huang, Hai-Jun
    TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2014, 64 : 48 - 70
  • [2] The Morning Commute Problem with Ridesharing When Meet Stochastic Bottleneck
    Zhang, Zipeng
    Zhang, Ning
    SUSTAINABILITY, 2021, 13 (11)
  • [3] Morning commute problem with queue-length-dependent bottleneck capacity
    Chen, Jin-Yong
    Jiang, Rui
    Li, Xin-Gang
    Hu, Mao-Bin
    Jia, Bin
    Gao, Zi-You
    TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2019, 121 : 184 - 215
  • [4] Modeling the Morning Commute Problem With Stochastic Travel Time in a Bottleneck Model
    Guo, Xiao
    Zhang, Aomuhan
    Tian, Lu
    Li, Tongfei
    IEEE ACCESS, 2020, 8 (08): : 149468 - 149475
  • [5] On the morning commute problem with mixed autonomous and human-driven traffic under stochastic bottleneck capacity
    Liu, Qiumin
    Liu, Wei
    Jiang, Rui
    Han, Xiao
    TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2025, 195
  • [6] Manage morning commute for household travels with parking space constraints
    Zhang, Yuan
    Zhao, Hui
    Jiang, Rui
    TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2024, 185
  • [7] On the morning commute problem with bottleneck congestion and parking space constraints
    Yang, Hai
    Liu, Wei
    Wang, Xiaolei
    Zhang, Xiaoning
    TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2013, 58 : 106 - 118
  • [8] Departure Time and Route Choices With Accurate Information Under Binary Stochastic Bottleneck Capacity in the Morning Commute
    Yu, Yun
    Han, Xiao
    Jiang, Rui
    Darr, Justin
    Jia, Bin
    IEEE ACCESS, 2020, 8 : 225551 - 225565
  • [9] Experimental study of departure time choice behavior in commute problem with stochastic bottleneck capacity
    Lu, Dongxu
    Jiang, Rui
    Han, Xiao
    Yang, Yong
    Liu, Qiumin
    2019 5TH INTERNATIONAL CONFERENCE ON TRANSPORTATION INFORMATION AND SAFETY (ICTIS 2019), 2019, : 1146 - 1151
  • [10] Manage morning commute problem of household travels under single-step toll: A comparison study
    Zhang, Yuan
    Zhao, Hui
    Jiang, Rui
    Shang, Ying
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2025, 174