Understanding Congestion Risk and Emissions of Various Travel Behavior Patterns Based on License Plate Recognition Data

Yuting Wang, Zhaocheng He (), Wangyong Xing and Chengchuang Lin
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Yuting Wang: School of Intelligent Engineering, Sun Yat-sen University, Shenzhen 528406, China
Zhaocheng He: School of Intelligent Engineering, Sun Yat-sen University, Shenzhen 528406, China
Wangyong Xing: Guangdong Leatop Technology Investment Co., Ltd., Guangzhou 510663, China
Chengchuang Lin: Guangdong Leatop Technology Investment Co., Ltd., Guangzhou 510663, China

Sustainability, 2025, vol. 17, issue 2, 1-22

Abstract: Understanding vehicle travel behavior patterns is crucial for effectively managing urban traffic congestion and mitigating the associated risks and excessive emissions. Existing research predominantly focuses on commuting patterns, with limited attention given to the spatiotemporal characteristics of other travel behaviors, and sparse investigation into the congestion risks and emissions associated with these patterns. To address this gap, the present study examines various travel behavior patterns and their associated congestion risks and emissions, using one week of License Plate Recognition (LPR) data from the megacity expressway network. First, we classify vehicles into different travel modes based on spatiotemporal features extracted from the LPR data and propose a scalable mode recognition method suitable for large-scale applications. We then assess the congestion risks associated with each mode and estimate the excessive emissions resulting from congestion. The findings reveal notable differences in congestion risks among travel modes, with a bimodal distribution influenced by the temporal rhythm of traffic flow. Furthermore, although commercial vehicles constitute only one-third of the total vehicle population, the excessive emissions attributed to congestion from commercial vehicles are comparable to those from privately owned vehicles. This suggests that focusing exclusively on commuting patterns may underestimate both the congestion risks and excessive emissions. The results of this study not only deepen our understanding of the relationship between individual travel behavior and traffic congestion but also support the optimization of personal travel time and health management, providing a foundation for the development of personalized and proactive traffic demand management strategies.

Keywords: spatiotemporal characteristics of travel; pattern recognition; traffic congestion; carbon emissions; sustainable development (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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