Exploring the Spatially Heterogeneous Effects of Urban Built Environment on Road Travel Time Variability

Shaopeng Zhong () and Daniel (Jian) Sun ()
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Shaopeng Zhong: Dalian University of Technology
Daniel (Jian) Sun: Chang’an University

Chapter Chapter 7 in Logic-Driven Traffic Big Data Analytics, 2022, pp 141-165 from Springer

Abstract: Abstract Most studies of road travel time estimation have been based on traffic flow theory or data-driven methods and generally neglect the influence of urban built environment on road travel time. A global regression model and a geographically weighted regression model were thus established to analyse the spatial heterogeneity of the effects of urban built environment on road travel time. The estimated results of the global regression model indicate that the occupancy rate of taxis, the distance from the nearest intersection, and the speed limit show positive correlations with a road’s travel speed, whilst the number of bus stops and the distance from the nearest school show negative associations with the travel speed of the road. Furthermore, based on the results of the geographically weighted regression model, the spatially varying relationships between urban built environment and road travel time can be established, thus providing important information for decision-makers to reduce road travel time by adjusting the attributes of urban built environment.

Keywords: Urban built environment; Road travel time variability; Spatial heterogeneity; GWR model; Floating car data (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-981-16-8016-8_7

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