TY - GEN
T1 - An ϵ-Constraint Multi-objective Algorithm for Transit Route Design with Subsidy Consideration
AU - Chen, Li Wen
AU - Hu, Ta Yin
AU - Shih, Le Chi
AU - Liao, Tsai Yun
N1 - Funding Information:
This paper is based on work supported by the Ministry of Science and Technology, Taiwan, R.O.C. The authors of course remain responsible for the content of this paper.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/28
Y1 - 2018/9/28
N2 - Smart City has been proposed to be a total solution for cities around the world. Public transportation, as one of the basic element in a Smart City, provides shared transport service, such as bus, light rail transit (LRT), and mass rapid transit (MRT), to save energy, reduce air pollution and relieve congestion. For transit operators, how to provide efficient and effective service in traffic networks with limited budget is an important issue. As more public transportation is deployed under the same budget, how to balance bus route and subsidy becomes a new issue. The research proposes a multi-objective formulation to design the optimal bus routes under three conflicting objectives, including travel cost, demand, and subsidy. The solution algorithm is constructed based on the ϵ-constraint method to solve the problem. Numerical experiments based on a realistic network in Chiayi (Taiwan) are conducted to illustrate the proposed algorithm.
AB - Smart City has been proposed to be a total solution for cities around the world. Public transportation, as one of the basic element in a Smart City, provides shared transport service, such as bus, light rail transit (LRT), and mass rapid transit (MRT), to save energy, reduce air pollution and relieve congestion. For transit operators, how to provide efficient and effective service in traffic networks with limited budget is an important issue. As more public transportation is deployed under the same budget, how to balance bus route and subsidy becomes a new issue. The research proposes a multi-objective formulation to design the optimal bus routes under three conflicting objectives, including travel cost, demand, and subsidy. The solution algorithm is constructed based on the ϵ-constraint method to solve the problem. Numerical experiments based on a realistic network in Chiayi (Taiwan) are conducted to illustrate the proposed algorithm.
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U2 - 10.1109/SOLI.2018.8476739
DO - 10.1109/SOLI.2018.8476739
M3 - Conference contribution
AN - SCOPUS:85055640804
T3 - Proceedings of the 2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018
SP - 163
EP - 168
BT - Proceedings of the 2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018
Y2 - 31 July 2018 through 2 August 2018
ER -