Understanding vehicular routing behavior with location-based service data
dc.contributor.author | Xu, Y | |
dc.contributor.author | Clemente, RD | |
dc.contributor.author | González, MC | |
dc.date.accessioned | 2021-03-01T07:46:08Z | |
dc.date.issued | 2021-01-26 | |
dc.description.abstract | Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation.</jats:p> | en_GB |
dc.description.sponsorship | MIT Energy Initiative | en_GB |
dc.description.sponsorship | Berkeley Deep Drive consortium | en_GB |
dc.identifier.citation | Vol. 10, article 12 | en_GB |
dc.identifier.doi | 10.1140/epjds/s13688-021-00267-w | |
dc.identifier.uri | http://hdl.handle.net/10871/124947 | |
dc.language.iso | en | en_GB |
dc.publisher | SpringerOpen | en_GB |
dc.rights | © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ | en_GB |
dc.title | Understanding vehicular routing behavior with location-based service data | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2021-03-01T07:46:08Z | |
exeter.article-number | 12 | en_GB |
dc.description | This is the final published version, also available from Springer Nature via the DOI in this record. | en_GB |
dc.identifier.eissn | 2193-1127 | |
dc.identifier.journal | EPJ Data Science | en_GB |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2021-02-14 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2021-02-14 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2021-03-01T07:43:47Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2021-03-01T07:46:11Z | |
refterms.panel | B | en_GB |
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author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other
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to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/