A Spatio-temporal Distribution Model for Determining Origin–Destination Demand from Multisource 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 2 in Logic-Driven Traffic Big Data Analytics, 2022, pp 33-52 from Springer

Abstract: Abstract A scientific understanding of the spatio-temporal distribution of road travel demand is a prerequisite for formulating effective countermeasures to traffic congestion. Accordingly, this chapter analyzes the relationship between urban built environment attributes and origin–destination (OD) demand in the specific spatial structure of a city, thereby guiding decision-makers on how to solve traffic congestion problems. Multisource data and a Dirichlet multinomial regression model are used to reveal the functional zones and spatial structure of a city. A spatial autoregressive model is then applied to reveal the relationship between urban built environment attributes and the spatio-temporal distribution of OD demand. Finally, data from the downtown area of Chengdu (China) are used to validate the model and method and analyze their performance.

Keywords: Spatio-temporal distribution of OD demand; Built environment; Dirichlet multinomial regression model; Spatial autoregressive model (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_2

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DOI: 10.1007/978-981-16-8016-8_2

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