Travel Time Estimation Based on Built Environment Attributes and Low-Frequency Floating Car Data

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

Chapter Chapter 6 in Logic-Driven Traffic Big Data Analytics, 2022, pp 119-139 from Springer

Abstract: Abstract This chapter studies the effect of urban built environment attributes on the estimation of road travel time (hereafter denoted “travel time”) from low-frequency floating car data without complex global positioning system information, such as speed. In addition, a new method of estimating travel time distribution is developed, which uses the distribution of the number of vehicles on a road, rather than the road’s length, as the proportional coefficient of a travel time distribution. To verify the correctness of this novel method, the effect parameters of various built environment attributes on travel time are examined in an example, using the maximum likelihood estimation method. The results show that certain urban built environment attributes around a road will lead to a significant increase in travel time in certain periods. The effect time of schools is from 6:00 a.m. to 7:20 a.m., while that of hospitals and clinics is from 7:00 a.m. to 8:00 a.m.; in addition, a similar travel time increase is caused by intersections in all scenarios. Finally, the likelihood ratio test verifies the reliability of using built environment attribute variables as influencing factors of travel time.

Keywords: Urban traffic; Travel time estimation; Floating car data; Built environment; Maximum likelihood estimation; Travel time distribution (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-8016-8_6

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DOI: 10.1007/978-981-16-8016-8_6

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