Exploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data

This study is designed to leverage ubiquitous mobile computing techniques on exploring app-based taxi movement patterns in large cities. To study patterns at different scales, we comprehensively explore both occupied and unoccupied vehicle movement characteristics through not only individual trips b...

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Autores principales:	Wenbo Zhang, Chang Xu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	traditional street-hailing taxicabs emerging app-based taxi services spatiotemporal movement patterns occupied and unoccupied vehicle movements Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/c2be14198b3f4b13a38a61116a153b6c
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spelling	oai:doaj.org-article:c2be14198b3f4b13a38a61116a153b6c2021-11-25T17:52:58ZExploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data10.3390/ijgi101107512220-9964https://doaj.org/article/c2be14198b3f4b13a38a61116a153b6c2021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/751https://doaj.org/toc/2220-9964This study is designed to leverage ubiquitous mobile computing techniques on exploring app-based taxi movement patterns in large cities. To study patterns at different scales, we comprehensively explore both occupied and unoccupied vehicle movement characteristics through not only individual trips but also their aggregations. Moran’s <i>I</i> and its variations are applied to explore spatial autocorrelations among different rides. PageRank centrality is applied for a functional network representing traffic flows to discover places of interest. Gyration radius measures the scope of passenger mobility and driver passenger searching. Moreover, cumulative distribution and data visualization techniques are adopted for trip level characteristics and features analysis. The results indicate that the app-based taxi services are serving more neighborhoods other than downtown areas by taking large proportion of relatively shorter trips and contributing to net increase in total taxi ridership although net decrease in downtown areas. The spatial autocorrelations are significant not only within each service but also among services. With the smartphone-based applications, app-based taxi services are able to search passengers in a larger area and move more efficiently during both occupied and unoccupied periods. Mining from huge empty trip trajectory by app-based taxis, we also identify the existence of stationary/stops state and circulations.Wenbo ZhangChang XuMDPI AGarticletraditional street-hailing taxicabsemerging app-based taxi servicesspatiotemporal movement patternsoccupied and unoccupied vehicle movementsGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 751, p 751 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	traditional street-hailing taxicabs emerging app-based taxi services spatiotemporal movement patterns occupied and unoccupied vehicle movements Geography (General) G1-922
spellingShingle	traditional street-hailing taxicabs emerging app-based taxi services spatiotemporal movement patterns occupied and unoccupied vehicle movements Geography (General) G1-922 Wenbo Zhang Chang Xu Exploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data
description	This study is designed to leverage ubiquitous mobile computing techniques on exploring app-based taxi movement patterns in large cities. To study patterns at different scales, we comprehensively explore both occupied and unoccupied vehicle movement characteristics through not only individual trips but also their aggregations. Moran’s <i>I</i> and its variations are applied to explore spatial autocorrelations among different rides. PageRank centrality is applied for a functional network representing traffic flows to discover places of interest. Gyration radius measures the scope of passenger mobility and driver passenger searching. Moreover, cumulative distribution and data visualization techniques are adopted for trip level characteristics and features analysis. The results indicate that the app-based taxi services are serving more neighborhoods other than downtown areas by taking large proportion of relatively shorter trips and contributing to net increase in total taxi ridership although net decrease in downtown areas. The spatial autocorrelations are significant not only within each service but also among services. With the smartphone-based applications, app-based taxi services are able to search passengers in a larger area and move more efficiently during both occupied and unoccupied periods. Mining from huge empty trip trajectory by app-based taxis, we also identify the existence of stationary/stops state and circulations.
format	article
author	Wenbo Zhang Chang Xu
author_facet	Wenbo Zhang Chang Xu
author_sort	Wenbo Zhang
title	Exploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data
title_short	Exploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data
title_full	Exploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data
title_fullStr	Exploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data
title_full_unstemmed	Exploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data
title_sort	exploring app-based taxi movement patterns from large-scale geolocation data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c2be14198b3f4b13a38a61116a153b6c
work_keys_str_mv	AT wenbozhang exploringappbasedtaximovementpatternsfromlargescalegeolocationdata AT changxu exploringappbasedtaximovementpatternsfromlargescalegeolocationdata
_version_	1718411885815005184

Exploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data

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