Bus Service Effectiveness and the Built Environment: A Spatial Cost-Effectiveness Analysis with Smart Card Data

Abstract Compared with conventional evaluations that focus on bus service demand, bus service effectiveness (SEV) emphasizes both the demand and supply dimensions of service. However, knowledge on spatial cost-effectiveness analyses of SEV and its relationship with spatial factors such as the built environment remains limited. Using smart card data from Shanghai, this study investigates how the built environment affects bus SEV using a slack-based efficiency measure and machine learning models. The results reveal that less bus ridership does not necessarily equate to lower bus SEV. Population and employment density show distinct effects on bus SEV, with employment density exhibiting a threshold effect at 5,000 jobs / km 2 . These findings offer insights for planners to design targeted built environment interventions that enhance SEV and underscore the importance of adopting a more spatially cost-effective evaluation framework that goes beyond bus ridership alone.

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Publication Details

Journal
Journal of Transportation Engineering Part A Systems
Published
2026-10-26
DOI
https://doi.org/10.1061/jtepbs.teeng-9898
Primary Topic
Transportation Planning and Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Bus Service Effectiveness and the Built Environment: A Spatial Cost-Effectiveness Analysis with Smart Card Data

Jiaorong Wu, 王鸿远, Zili Tian, Xiang Liu et al.
Journal of Transportation Engineering Part A Systems
Transportation Planning and Optimization
article

Bus Service Effectiveness and the Built Environment: A Spatial Cost-Effectiveness Analysis with Smart Card Data

Jiaorong Wu, 王鸿远, Zili Tian, Xiang Liu, Yongqi Deng
article en

Abstract

Abstract Compared with conventional evaluations that focus on bus service demand, bus service effectiveness (SEV) emphasizes both the demand and supply dimensions of service. However, knowledge on spatial cost-effectiveness analyses of SEV and its relationship with spatial factors such as the built environment remains limited. Using smart card data from Shanghai, this study investigates how the built environment affects bus SEV using a slack-based efficiency measure and machine learning models. The results reveal that less bus ridership does not necessarily equate to lower bus SEV. Population and employment density show distinct effects on bus SEV, with employment density exhibiting a threshold effect at 5,000 jobs / km 2 . These findings offer insights for planners to design targeted built environment interventions that enhance SEV and underscore the importance of adopting a more spatially cost-effective evaluation framework that goes beyond bus ridership alone.

Journal of Transportation Engineering Part A SystemsVol. 153(1)
Tongji University (CN), Hong Kong Polytechnic University (HK), Iowa State University (US)
Openalex Percentile: Top 11%
Transportation Planning and Optimization
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