Exploring the nonlinear relationships between human travel and road traffic congestions using taxi trajectory data

Yan Shi (), Da Wang (), Baoju Liu (), Min Deng () and Bingrong Chen ()
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Yan Shi: Central South University
Da Wang: Central South University
Baoju Liu: Central South University
Min Deng: Central South University
Bingrong Chen: Central South University

Transportation, 2025, vol. 52, issue 5, No 2, 1827-1856

Abstract: Abstract Urban road traffic congestion remains challenging due to global urbanisation and has caused travel delays, energy consumption, and detrimental emissions. Therefore, exploring the potential dominant factors associated with traffic congestion generation is necessary to mitigate traffic congestion. The built environment around congested areas is the core factor in the generation of traffic congestion, however, only a few considered the impact of human travel features on congested roads. We divided human travel factors into purpose- and movement-related factors and explored the nonlinear relationship between human travel factors and traffic congestion. The results from taxi travel in Wuhan show that travel purpose factors mostly impact traffic congestion on low-grade inner-city short roads, while movement factors mainly impact the periphery ring or high-grade long roads. Movement-dominant congestions are widespread but not severe. Severe traffic congestion occurs mainly due to purpose-dominant travel. For purpose-dominant congestions, all excessive POI visits may worsen traffic congestion, and higher POI mixing degree has a positive effect on reducing congestion. For movement-dominant congestions, the detour rate and congestion level show a positive dependence, and the whole travel distance and travel accomplished rate indicate a U-shaped nonlinear relationship with congestion. This study provides detailed partial dependence plots of how congestion varies with human travel factors, providing insights and locational indications for traffic participants and urban designers to reduce congestion and improve urban mobility.

Keywords: Human travel; Traffic congestion; Nonlinear relationship; Threshold effect; Taxi trajectory data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11116-024-10476-7

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