research-article

SharedEdge: GPS-Free Fine-Grained Travel Time Estimation in State-Level Highway Systems

Authors:

Desheng ZhangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 1

Article No.: 48, Pages 1 - 26

https://doi.org/10.1145/3191780

Published: 26 March 2018 Publication History

Abstract

Estimating travel time on the highway in real time is of great importance for transportation services. Previous work has been mainly focusing on the city scale for a particular transportation system, e.g., taxi, bus, and metro. Little research has been conducted to estimate fine-grained real-time travel time in state-level highway systems. This is because the traditional solutions based on probe vehicles or loop sensors cannot scale to state-level highway systems due to their large spatial coverage. Recently, the adoption of Electric Toll Collection (ETC) systems (e.g. EZ-pass) brings a new opportunity to estimate the real-time travel time in the highway systems with little marginal cost. However, the key challenge is that ETC data only record the coarse-grained total travel time between a pair of toll stations rather than fine-grained travel time in each individual highway edge. To address this challenge, we design SharedEdge to estimate the fine-grained edge travel time with large-scale streaming ETC data. The key novelty is that we estimate real-time fine-grained travel time (i.e., edge travel time) without using fine-grained data (i.e. GPS trajectories or loop sensor data), by a few techniques based on Bayesian Graphical models and Expectation Maximization. More importantly, we implement our SharedEdge in the Guangdong Province, China with an ETC system covering 69 highways and 773 toll stations with a length of 7, 000 km. Based on this implementation, we evaluate SharedEdge in details by comparing it with some baselines and the state-of-the-art models. The evaluation results show that SharedEdge outperforms other methods in terms of travel time estimation accuracy when compared with the ground truth obtained by 114 thousand GPS-equipped vehicles.

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Cited By

Musleh MMokbel M(2024)Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis TasksACM Transactions on Spatial Algorithms and Systems10.1145/365647010:2(1-25)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656470
Mokbel MSakr MXiong LZüfle AAlmeida JAnderson TAref WAndrienko GAndrienko NCao YChawla SCheng RChrysanthis PFei XGhinita GGraser AGunopulos DJensen CKim JKim KKröger PKrumm JLauer JMagdy ANascimento MRavada SRenz MSacharidis DSalim FSarwat MSchoemans MShahabi CSpeckmann BTanin ETeng XTheodoridis YTorp KTrajcevski Gvan Kreveld MWenk CWerner MWong RWu SXu JYoussef MZeinalipour DZhang MZimányi E(2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/365215810:2(1-35)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3652158
Mostafa AMokbel MDamiani MRenz MEldawy AKröger PNascimento M(2023)Towards Open-Source Maps MetadataProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625576(1-4)Online publication date: 13-Nov-2023
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Index Terms

SharedEdge: GPS-Free Fine-Grained Travel Time Estimation in State-Level Highway Systems
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Location based services
2. Networks
  1. Network types
    1. Cyber-physical networks
      1. Sensor networks

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 1

March 2018

1370 pages

EISSN:2474-9567

DOI:10.1145/3200905

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Copyright © 2018 ACM.

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Publication History

Published: 26 March 2018

Accepted: 01 January 2018

Revised: 01 November 2017

Received: 01 August 2017

Published in IMWUT Volume 2, Issue 1

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Rutgers Global Center
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National Natural Science Foundation of China

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Cited By

Musleh MMokbel M(2024)Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis TasksACM Transactions on Spatial Algorithms and Systems10.1145/365647010:2(1-25)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656470
Mokbel MSakr MXiong LZüfle AAlmeida JAnderson TAref WAndrienko GAndrienko NCao YChawla SCheng RChrysanthis PFei XGhinita GGraser AGunopulos DJensen CKim JKim KKröger PKrumm JLauer JMagdy ANascimento MRavada SRenz MSacharidis DSalim FSarwat MSchoemans MShahabi CSpeckmann BTanin ETeng XTheodoridis YTorp KTrajcevski Gvan Kreveld MWenk CWerner MWong RWu SXu JYoussef MZeinalipour DZhang MZimányi E(2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/365215810:2(1-35)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3652158
Mostafa AMokbel MDamiani MRenz MEldawy AKröger PNascimento M(2023)Towards Open-Source Maps MetadataProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625576(1-4)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625576
Yan HWang HZhang DYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Identifying Regional Driving Risks via Transductive Cross-City Transfer Learning Under Negative TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614924(2877-2886)Online publication date: 21-Oct-2023
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Wang HZhang ZFan ZChen JZhang LShibasaki RSong X(2023)Multi-Task Weakly Supervised Learning for Origin–Destination Travel Time EstimationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.323606035:11(11628-11641)Online publication date: 25-Jan-2023
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