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Characterizing multicity urban traffic conditions using crowdsourced data

PloS one, 2019-03, Vol.14 (3), p.e0212845-e0212845 [Peer Reviewed Journal]

COPYRIGHT 2019 Public Library of Science ;COPYRIGHT 2019 Public Library of Science ;2019 Nair et al 2019 Nair et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0212845 ;PMID: 30861011

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Title:
Characterizing multicity urban traffic conditions using crowdsourced data
Author: Nair, Divya Jayakumar ; Gilles, Flavien ; Chand, Sai ; Saxena, Neeraj ; Dixit, Vinayak
Wang, Yinhai
Subjects: Analysis ; Benchmarking ; Biology and Life Sciences ; Cities - statistics & numerical data ; Cities and towns ; City Planning - methods ; Computer and Information Sciences ; Crowdsourcing ; Digital map services ; Earth Sciences ; Engineering and Technology ; Human acts ; Human behavior ; Humans ; Management ; Models, Theoretical ; Motor vehicle drivers ; Physical Sciences ; Population density ; Reproducibility of Results ; Social Sciences ; Spatio-Temporal Analysis ; Strategic planning (Business) ; Traffic congestion ; Traffic engineering ; Transportation - statistics & numerical data ; Travelers
Is Part Of: PloS one, 2019-03, Vol.14 (3), p.e0212845-e0212845
Description: Road traffic congestion continues to manifest and propagate in cities around the world. The recent technological advancements in intelligent traveler information have a strong influence on the route choice behavior of drivers by enabling them to be more flexible in selecting their routes. Measuring traffic congestion in a city, understanding its spatial dispersion, and investigating whether the congestion patterns are stable (temporally, such as on a day-to-day basis) are critical to developing effective traffic management strategies. In this study, with the help of Google Maps API, we gather traffic speed data of 29 cities across the world over a 40-day period. We present generalized congestion and network stability metrics to compare congestion levels between these cities. We find that (a) traffic congestion is related to macroeconomic characteristics such as per capita income and population density of these cities, (b) congestion patterns are mostly stable on a day-to-day basis, and (c) the rate of spatial dispersion of congestion is smaller in congested cities, i.e. the spatial heterogeneity is less sensitive to increase in delays. This study compares the traffic conditions across global cities on a common datum using crowdsourced data which is becoming readily available for research purposes. This information can potentially assist practitioners to tailor macroscopic network congestion and reliability management policies. The comparison of different cities can also lead to benchmarking and standardization of the policies that have been used to date.
Publisher: United States: Public Library of Science
Language: English
Identifier: ISSN: 1932-6203
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0212845
PMID: 30861011
Source: Geneva Foundation Free Medical Journals at publisher websites
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Characterizing multicity urban traffic conditions using crowdsourced data

COPYRIGHT 2019 Public Library of Science ;COPYRIGHT 2019 Public Library of Science ;2019 Nair et al 2019 Nair et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0212845 ;PMID: 30861011

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