open access publication

Article, 2023

Bi-objective optimization of last-train timetabling with multimodal coordination in urban transportation

Transportation Research Part C Emerging Technologies, ISSN 0968-090X, 1879-2359, Volume 154, Page 104260, 10.1016/j.trc.2023.104260

Contributors

Ning, Jia [1] Peng, Qi-Yuan [1] Zhu, Yongqiu 0000-0002-7454-8664 (Corresponding author) [2] Xing, Xinjie 0000-0001-6277-5045 [3] Nielsen, Otto Anker 0000-0001-7901-7021 [4]

Affiliations

  1. [1] Southwest Jiaotong University
    2. [NORA names: China; Asia, East];
  2. [2] ETH Zurich
    3. [NORA names: Switzerland; Europe, Non-EU; OECD];
  3. [3] University of Liverpool
    4. [NORA names: United Kingdom; Europe, Non-EU; OECD];
  4. [4] Technical University of Denmark
    5. [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

When urban rail transit (URT) does not provide 24-hour services, passengers who travel at late night may not be able to reach their destinations with only URT trains. As a result, passengers have to find alternative transport means, or combine URT trains with other transport services to fulfill their journeys. This paper investigates the integrated optimization of last train timetabling and bridging service design with consideration of passenger path choices. Two bridging services are considered: taxis and buses. Based on pre-constructed path sets, a bi-objective mixed-integer nonlinear programming (MINLP) model is developed, aiming at minimizing total passenger travel time and total passenger travel cost. To reduce the model scale and improve solution efficiency, three path dominance principles are proposed to remove redundant passenger paths without loss of optimality. An adaptive iterative algorithm is designed to obtain the Pareto frontier curve. The proposed model and solution methods are demonstrated on the Chengdu URT network. Results indicate that passenger travel costs and travel times can be significantly reduced by the integrated optimization. It also provides passengers with a safer night travel environment due to the reduction in passenger travel times in taxis.

Keywords

Chengdu, Pareto, Pareto frontier curve, adaptive iterative algorithm, algorithm, alternative transport means, bi-objective mixed-integer nonlinear programming, bi-objective optimization, bridge, bridging services, bus, choice, coordination, cost, curves, design, destination, dominance principle, efficiency, environment, improve solution efficiency, integrated optimization, iterative algorithm, journey, last-train timetable, loss, loss of optimality, mean, method, mixed-integer nonlinear programming, model, model scale, multimodal coordination, network, night, nonlinear programming, optimization, passenger, passenger path choices, passenger paths, passenger travel cost, passenger travel time, path, path choice, principles, program, rail transit, reduction, results, scale, service design, services, solution, solution efficiency, solution method, taxes, time, timetable, train timetable, training, transition, transport, transport means, transport services, travel cost, travel environment, travel time, urban rail transit, urban rail transit trains, urban transport

Funders

  • National Natural Science Foundation of China
  • China Scholarship Council
  • Ministry of Science and Technology of the People's Republic of China
  • ETH Zurich

Data Provider: Digital Science

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