Let's Connect
Follow Us
Watch Us
(+385) 1 2380 262
journal.prometfpz.unizg.hr
Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

PUBLISHED
21.12.2017
LICENSE
Copyright (c) 2024 Zhongzhen Yang, Jiannan Cheng

Optimization of Trip-end Networks and Ride Price for Express Coach Systems in the High-speed Rail Era

Authors:Zhongzhen Yang, Jiannan Cheng

Abstract

Express coach (EC) lost a considerable share of passengers after high-speed rail (HSR) was implemented. This paper proposes a door-to-door operation mode for the EC system and builds a model to design an EC trip-end network in the origin city with the aim of maximizing the EC’s daily operating profit. A case study is undertaken, and the results show that the operating profit of the EC system first increases and then decreases with the growth of the trip-end routes. In the HSR era, door-to-door operation can effectively guarantee the market share and operating profits of the EC.

Keywords:express coach, high-speed rail, door-to-door mode, trip-end network, ticket price, operating prof-it,

References

  1. Lampkim S. The design of routes, undertaking: a case study. Transp Res. 1967;34(2): 123-127.

    Mandl CE. Evaluation and optimization of urban public transportation networks. Eur J Oper Res. 1980;5(6): 396-404.

    Van Nes R, Bovy PHL. The importance of objectives in urban transit network design. Transp Res Rec. 2000;1735: 50-57.

    Chakroborty P, Wivedi T. Optimal route network design for transit systems using genetic algorithms. Eng Optim. 2002;34(1):83-100.

    Ceder A, Wilson NHM. Bus network design. Transp Res B Methodol. 1986;20(4): 331-344.

    Baaj MH, Mahmassani HS. An AI-based approach for transit route system planning and design. J Adv Transp. 1991;25(2): 187-209.

    Baaj MH, Mahmassani HS. Hybrid route generation heuristic algorithm for the design of transit networks. Transp Res C Emerg Technol. 1995;3(1): 31-50.

    Ceder A, Israeli Y. User and operator perspectives in transit network design. Transp Res Rec. 1998;1623: 3-7.

    Pattnaik SB, Mohan S, Tom VM. route network design using genetic algorithm. J Transp Eng. 1998;124(4): 368-375.

    Tom VM, Mohan S. Transit route network design using frequency coded genetic algo-rithm. J Transp Eng. 2003;129(2): 186-195.

    Bielli M, Caramia M, Carotenuto P. Genetic algorithms in bus network optimization. Transp Res C Emerg Technol. 2002;10(1): 19-34.

    Ngamchai S, Lovell DJ. Optimal time transfer in bus transit route network design using a genetic algorithm. J Transp Eng. 2003;129(5): 510-521.

    Agrawal J, Mathew TV. Transit route network design using parallel genetic algorithm. J Comput Civ Eng. 2004;18(3): 248-256.

    Lee YJ, Vuchic VR. Transit network design with variable demand. J Transp Eng. 2005;131(1): 1-10.

    dell’Olio L, Moura J, Ibeas A. Bi-level mathematical programming model for locating bus stops and optimizing frequencies. Transp Res Rec. 2006;1971(1): 23-31.

    Fan W, Machemehl RB. Optimal transit route network design problem with variable transit demand: genetic algorithm approach. J Transp Eng. 2006;132(1): 40-51.

    Fan W, Machemehl RB. Using a simulated annealing algorithm to solve the transit route network design problem. J Transp Eng. 2006;132(2): 122-132.

    Szeto WY, Wu Y. A simultaneous bus route design and frequency setting problem for tin Shui Wai, Hong Kong. Eur J Oper Res. 2011;209(2): 141-155.

    Afandizadeh S, Khaksar H, Kalantari N. Bus fleet optimization using genetic algorithm a case study of Mashhad. Int J Civ Eng. 2013;11(1): 43-52.

    Szeto WY, Jiang Y. Transit route and frequency design: bi-level modeling and hybrid arti-ficial bee colony algorithm approach. Transp Res B Methodol. 2014;67(9): 235-263.

    Arbex RO, da Cunha CB. Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm. Transp Res B Methodol. 2015;81: 355-376.

    Zhang Y, Wu L, Wei G, Wang S. A novel algorithm for all pairs shortest path problem based on matrix multiplication and pulse coupled neural network. Digit Signal Process. 2011;21(4): 517-521.

    Ma JQ, Bai Y, Han BM. Characteristic analysis of basic unit and complex network for ur-ban rail transit. J Traffic Transp Eng. 2010;4.

    Dai T, Jin F. Spatial interaction and network structure evolvement of cities in terms of Chi-na’s rail passenger flows. Chin Geogr Sci. 2008;18(3): 206-213.

    Zhao F. Large-scale transit network optimization by minimizing user cost and transfers. J Public Transp. 2006;9(2): 107-129.

    Wang S, Yang J, Liu G, Du S, Yan J. Multi-objective path finding in stochastic networks using a biogeography-based optimization method. Simulation. 2016;92(7): 637-647.

    Zhang Y, Wu L, Wang S. UCAV path planning by fitness-scaling adaptive chaotic particle swarm optimization. Math Probl Eng. 2013;2013(2013): 705238.

Show more


Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal