Exploring the Spatio-Temporally Heterogeneous Impact of Traffic Network Structure on Ride-Hailing Emissions Using Shenzhen, China, as a Case Study

Wenyuan Gao, Chuyun Zhao (), Yu Zeng and Jinjun Tang ()
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Wenyuan Gao: Pollutant and Emergency Monitoring Department, Hunan Provincial Ecological Environment Monitoring Center, Changsha 410001, China
Chuyun Zhao: Smart Transport Key Laboratory of Hunan Province, School of Transport and Transportation Engineering, Central South University, Changsha 410075, China
Yu Zeng: Pollutant and Emergency Monitoring Department, Hunan Provincial Ecological Environment Monitoring Center, Changsha 410001, China
Jinjun Tang: Smart Transport Key Laboratory of Hunan Province, School of Transport and Transportation Engineering, Central South University, Changsha 410075, China

Sustainability, 2024, vol. 16, issue 11, 1-31

Abstract: The rise of ride-hailing services presents innovative solutions for curbing urban carbon emissions, yet poses challenges such as fostering fair competition and integrating with public transit. Analyzing the factors influencing ride-hailing emissions is crucial for understanding their relationship with other travel modes and devising policies aimed at steering individuals towards more environmentally sustainable travel options. Therefore, this study delves into factors impacting ride-hailing emissions, including travel demand, land use, demographics, and transportation networks. It highlights the interplay among urban structure, multi-modal travel, and emissions, focusing on network features such as betweenness centrality and accessibility. Employing the COPERT (Computer Programme to Calculate Emissions from Road Transport) model, ride-hailing emissions are calculated from vehicle trajectory data. To mitigate statistical errors from multicollinearity, variable selection involves tests and correlation analysis. Geographically and temporally weighted regression (GTWR) with an adaptive kernel function is designed to understand key influencing mechanisms, overcoming traditional GTWR limitations. It can dynamically adjust bandwidth based on the spatio-temporal distribution of data points. Experiments in Shenzhen validate this approach, showing a 9.8% and 10.8% increase in explanatory power for weekday and weekend emissions, respectively, compared to conventional GTWR. The discussion of findings provides insights for urban planning and low-carbon transport strategies.

Keywords: travel emissions; traffic network structure; spatio-temporal heterogeneity; geographically and temporally weighted regression; ride-hailing travel (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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