TY - JOUR
T1 - Urban path travel time estimation using GPS trajectories from high-sampling-rate ridesourcing services
AU - Correa, Diego
AU - Ozbay, Kaan
N1 - Funding Information:
The authors thank Dr. Jingqin Gao, Fan Zuo and Yubin Shen for helping during the analysis. This work was supported by the Connected Cities for Smart Mobility towards Accessible and Resilient Transportation (C2SMART) Center, a Tier 1 University Center awarded by U.S. Department of Transportation (DOT) under the University Transportation Centers Program. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This work is funded, partially or entirely, by a grant from the U.S. DOT's University Transportation Centers Program. However, the U.S. Government assumes no liability for the contents or use thereof.
Publisher Copyright:
© 2022 Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - Link-Travel-Time (LTT) estimation is essential for the planning and operations of a variety of transportation services. Given the random sampling of a very large number of GPS-points over a highly complex urban network, the task of organizing these individual GPS readings to estimate LTTs requires the development and implementation of a novel comprehensive data processing and path-finding methodology which is described in detail in this paper. As part of this novel methodology, an innovative data-driven matching-algorithm to estimate urban LTT from high-sampling-rate GPS data projected onto the Open-Street-Map network is developed and implemented. Then, using these LTTs, we construct Path-Travel-Time (PTT) between major origin-destination pairs. PTT of Actual-Paths (AP) followed by GPS-enabled vehicles are compared with k-Shortest-Paths (SP), allowing us to better understand route-choice behavior and overall traffic conditions. We compare PTT from observed-trips (OD-trips), map-matched AP, and SP paths with Free-Flow (FF). Results show that OD-trips, AP, and SP exceed FF by 15%, 41%, and 15%, respectively. The difference in PTT between OD-AP is ∼5%, which means the map-matching process works well and does not create bias in our analysis. People using the shortest-path varies with the distance; for ∼3-mile-paths, 50% of users do not use it. For ∼6-mile-paths, the percentage reduces to 35%, and for ∼9-mile, the percentage is 25%. A relatively high number of trips spend more time than the average and much longer than the shortest PTT.
AB - Link-Travel-Time (LTT) estimation is essential for the planning and operations of a variety of transportation services. Given the random sampling of a very large number of GPS-points over a highly complex urban network, the task of organizing these individual GPS readings to estimate LTTs requires the development and implementation of a novel comprehensive data processing and path-finding methodology which is described in detail in this paper. As part of this novel methodology, an innovative data-driven matching-algorithm to estimate urban LTT from high-sampling-rate GPS data projected onto the Open-Street-Map network is developed and implemented. Then, using these LTTs, we construct Path-Travel-Time (PTT) between major origin-destination pairs. PTT of Actual-Paths (AP) followed by GPS-enabled vehicles are compared with k-Shortest-Paths (SP), allowing us to better understand route-choice behavior and overall traffic conditions. We compare PTT from observed-trips (OD-trips), map-matched AP, and SP paths with Free-Flow (FF). Results show that OD-trips, AP, and SP exceed FF by 15%, 41%, and 15%, respectively. The difference in PTT between OD-AP is ∼5%, which means the map-matching process works well and does not create bias in our analysis. People using the shortest-path varies with the distance; for ∼3-mile-paths, 50% of users do not use it. For ∼6-mile-paths, the percentage reduces to 35%, and for ∼9-mile, the percentage is 25%. A relatively high number of trips spend more time than the average and much longer than the shortest PTT.
KW - large scale GPS data analysis
KW - link travel time
KW - ridesourcing
KW - shortest-path
KW - urban networks
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U2 - 10.1080/15472450.2022.2124867
DO - 10.1080/15472450.2022.2124867
M3 - Review article
AN - SCOPUS:85139095260
SN - 1547-2450
JO - Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
JF - Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
ER -