A Scheduling-Optimization Model with Multi-Objective Constraints for Low-Carbon Urban Rail Transit Considering the Built Environment and Travel Demand: A Case Study of Hangzhou

Jinrui Zang, Yuan Liu, Kun Qie, Yue Chen, Suli Wang and Xu Sun ()
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Jinrui Zang: Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Yuan Liu: Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Kun Qie: Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Yue Chen: College of Civil and Transportation Engineering, Guangzhou University, Guangzhou 510006, China
Suli Wang: Beijing CSTJ Metro Investment and Development Co., Ltd., Beijing 100070, China
Xu Sun: Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Sustainability, 2025, vol. 17, issue 11, 1-19

Abstract: Urban rail transit, a crucial component of urban public transportation, often experiences increased operational costs and carbon emissions due to low-load operations being conducted during off-peak passenger flow periods. This study aims to develop an optimization method for the daily scheduling of rail train operations with the goal of carbon emission reduction, while comprehensively considering the built environment and travel demand. Firstly, the influence of the urban built environment on residents’ travel demand is analyzed using an XGBoost model. Secondly, a time convolutional travel demand prediction model, Built Environment-Weighted Temporal Convolutional Network (BE-TCN), weighted by built environment factors, is constructed. Finally, an optimization method for rail train operation schedules based on the built environment and travel demand is proposed, with the objective of carbon emission reduction. A case study is conducted using the Hangzhou urban rail transit system as an example. The results indicate that the optimization method proposed in this study can achieve monthly carbon emission reductions of 1524.58 tons, 1181.94 tons, and 520.84 tons for Lines 1, 2, and 4 of the Hangzhou urban rail transit system, respectively. The research findings contribute to enhancing the economic efficiency and environmental sustainability of urban rail transit systems.

Keywords: urban rail transit; carbon emission reduction; train operation planning optimization; travel demand; built environment; spatiotemporal modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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