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Personalized Travel Time Prediction Using a Small Number of Probe Vehicles

Authors:

Dimitrios Gunopulos,

Leonidas J. GuibasAuthors Info & Claims

ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 5, Issue 1

Article No.: 4, Pages 1 - 27

https://doi.org/10.1145/3317663

Published: 21 May 2019 Publication History

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Abstract

Predicting the travel time of a path is an important task in route planning and navigation applications. As more GPS probe data has been collected to monitor urban traffic, GPS trajectories of the probe vehicles have been frequently used to predict path travel time. However, most trajectory-based methods rely on deploying GPS devices and collect real-time data on a large taxi fleet, which can be expensive and unreliable in smaller cities. This work deals with the problem of predicting path travel time when only a small number of cars are available. We propose an algorithm that learns local congestion patterns of a compact set of frequently shared paths from historical data. Given a travel time prediction query, we identify the current congestion patterns around the query path from recent trajectories, then infer its travel time in the near future. Driver identities are also used in predicting personalized travel time. Experimental results using 10--25 taxis in urban areas of Shenzhen, China, show that personal prediction has on average 3.4mins of error on trips of duration 10--75mins. This result improves the baseline approach of using purely historical trajectories by 16.8% on average, over four regions with various degrees of path regularity. It also outperforms a state-of-the-art travel time prediction method that uses both historical trajectories and real-time trajectories.

References

[1]

OpenStreetMap contributors. 2015. Planet Dump. Retrieved from https://planet.osm.org. https://www.openstreetmap.org.

[2]

John E. Angus. 1994. The probability integral transform and related results. SIAM Rev. 36, 4 (1994), 652--654.

Digital Library

[3]

Chen Chen, Hao Su, Qixing Huang, Lin Zhang, and Leonidas Guibas. 2013b. Pathlet learning for compressing and planning trajectories. In Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 392--395.

Digital Library

[4]

Hao Chen, Hesham A. Rakha, and Catherine C. McGhee. 2013a. Dynamic travel time prediction using pattern recognition. In Proceedings of the ITS World Congress 2013.

[5]

Mei Chen and Steven Chien. 2001. Dynamic freeway travel-time prediction with probe vehicle data: Link based versus path based. Transport. Res. Rec.: J. Transport. Res. Board 1768 (2001), 157--161.

[6]

Jocelyn Chi, Eric Chi, and Richard Baraniuk. 2016. k-POD: A method for k-means clustering of missing data. Amer. Stat. 70, 1 (2016), 91--99.

[7]

Corrado De Fabritiis, Roberto Ragona, and Gaetano Valenti. 2008. Traffic estimation and prediction based on real time floating car data. In Proceedings of the International Conference on Intelligent Transportation Systems (ITSC’08). IEEE, 197--203.

[8]

Jianhe Du and Lisa Aultman-Hall. 2007. Increasing the accuracy of trip rate information from passive multi-day {GPS} travel datasets: Automatic trip end identification issues. Transport. Res. Part A: Policy Pract. 41, 3 (2007), 220--232.

[9]

Greg Hamerly and Charles Elkan. 2003. Learning the K in K-means. In Neural Information Processing Systems. MIT Press, 2003.

Digital Library

[10]

Aude Hofleitner and Alexandre Bayen. 2011. Optimal decomposition of travel times measured by probe vehicles using a statistical traffic flow model. In Proceedings of the International Conference on Intelligent Transportation Systems (ITSC’11). IEEE, 815--821.

[11]

Aude Hofleitner, Ryan Herring, Pieter Abbeel, and Alexandre Bayen. 2012. Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network. IEEE Trans. Intell. Transport. Syst. 13, 4 (2012), 1679--1693.

Digital Library

[12]

Yijuan Jiang and Xiang Li. 2013. Travel time prediction based on historical trajectory data. Ann. GIS 19, 1 (2013), 27--35.

[13]

Gurunath Kedar-Dongarkar and Manohar Das. 2012. Driver classification for optimization of energy usage in a vehicle. Procedia Comput. Sci. 8 (2012), 388--393.

[14]

Georgios Kellaris, Nikos Pelekis, and Yannis Theodoridis. 2013. Map-matched trajectory compression. J. Syst. Softw. 86, 6 (2013), 1566--1579.

Digital Library

[15]

Yang Li, Dimitrios Gunopulos, Cewu Lu, and Leonidas Guibas. 2017. Urban travel time prediction using a small number of GPS floating cars. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL’17). ACM, New York, NY, Article 3, 10 pages.

Digital Library

[16]

John-Michael McNew. 2012. Predicting cruising speed through data-driven driver modeling. In Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems (ITSC’12). IEEE, 1789--1796.

[17]

Xiaoguang Niu, Ying Zhu, and Xining Zhang. 2014. DeepSense: A novel learning mechanism for traffic prediction with taxi GPS traces. In Proceedings of the IEEE Global Communications Conference. IEEE, 2745--2750.

[18]

Mahmood Rahmani, Erik Jenelius, and Haris N. Koutsopoulos. 2013. Route travel time estimation using low-frequency floating car data. In Proceedings of the International Conference on Intelligent Transportation Systems. IEEE, 2292--2297.

[19]

Gilles Reymond, Andras Kemeny, Jacques Droulez, and Alain Berthoz. 2001. Role of lateral acceleration in curve driving: Driver model and experiments on a real vehicle and a driving simulator. Human Factors 43, 3 (2001), 483--495.

[20]

Bin Shi, Li Xu, Jie Hu, Yun Tang, Hong Jiang, Wuqiang Meng, and Hui Liu. 2015. Evaluating driving styles by normalizing driving behavior based on personalized driver modeling. IEEE Trans. Syst., Man, Cyber.: Syst. 45, 12 (2015), 1502--1508.

[21]

Penghui Sun, Shixiong Xia, Guan Yuan, and Daxing Li. 2016. An overview of moving object trajectory compression algorithms. Mathematical Problems in Engineering 2016, Algorithms for Compressive Sensing Signal Reconstruction with Applications (Special Issue), Article 6587309 (2016).

[22]

Hongjian Wang, Yu-Hsuan Kuo, Daniel Kifer, and Zhenhui Li. 2016. A simple baseline for travel time estimation using large-scale trip data. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 61.

Digital Library

[23]

Wenshuo Wang, Junqiang Xi, and Huiyan Chen. 2014a. Modeling and recognizing driver behavior based on driving data: A survey. Mathematical Problems in Engineering 2014, Mathematical Modeling, Analysis, and Advanced Control of Complex Dynamical Systems (Special Issue), Article 245641 (2014).

[24]

Yilun Wang, Yu Zheng, and Yexiang Xue. 2014b. Travel time estimation of a path using sparse trajectories. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 25--34.

Digital Library

[25]

Chun-Hsin Wu, Jan-Ming Ho, and Der-Tsai Lee. 2004. Travel-time prediction with support vector regression. IEEE Trans. Intell. Transport. Syst. 5, 4 (2004), 276--281.

Digital Library

[26]

Longgang Xiang, Meng Gao, and Tao Wu. 2016. Extracting stops from noisy trajectories: A sequence oriented clustering approach. ISPRS Int. J. Geo-Inform. 5, 3 (2016), 29.

[27]

Tao Xu, Xiang Li, and Christophe Claramunt. 2017. Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories. Frontiers of Earth Science 12, 2 (2017), 1--11.

[28]

Xiangxiang Xu, Pei Zhang, and Lin Zhang. 2014. Gotcha: A mobile urban sensing system. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’14). ACM, 316--317.

Digital Library

[29]

M. Kerber, L. Zhang, Y. Li, Q. Huang, and L. Guibas. 2013. Large-scale joint map matching of GPS traces. In Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL’13).

Digital Library

[30]

Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, and Yan Huang. 2010. T-drive: Driving directions based on taxi trajectories. In Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL’10). ACM, 99--108.

Digital Library

[31]

Xianyuan Zhan, Samiul Hasan, Satish V. Ukkusuri, and Camille Kamga. 2013. Urban link travel time estimation using large-scale taxi data with partial information. Trans. Res. Part C: Emerg. Technol. 33, 8 (2013), 37--49.

[32]

Xianyuan Zhan, Yu Zheng, Xiuwen Yi, and Satish V. Ukkusuri. 2017. Citywide traffic volume estimation using trajectory data. IEEE Trans. Knowl. Data Eng. 29, 2 (Feb. 2017), 272--285.

Digital Library

[33]

Faming Zhang, Xinyan Zhu, Tao Hu, Wei Guo, Chen Chen, and Lingjia Liu. 2016. Urban link travel time prediction based on a gradient boosting method considering spatiotemporal correlations. ISPRS Int. J. Geo-Inform. 5, 11 (2016), 201.

[34]

Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the Conference on World Wide Web. ACM, 791--800.

Digital Library

Cited By

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
Alix GPapagelis MRenz MNascimento M(2023)PathletRL: Trajectory Pathlet Dictionary Construction using Reinforcement LearningProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625622(1-12)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625622
Chughtai JHaq IIslam SGani A(2022)A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector RegressionSensors10.3390/s2224973522:24(9735)Online publication date: 12-Dec-2022
https://doi.org/10.3390/s22249735
Show More Cited By

Index Terms

Personalized Travel Time Prediction Using a Small Number of Probe Vehicles
1. Applied computing
  1. Operations research
    1. Transportation
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems
      1. Data streaming
      2. Global positioning systems

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Published In

cover image ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems Volume 5, Issue 1

Special Issue on SIGSPATIAL 2017

March 2019

146 pages

ISSN:2374-0353

EISSN:2374-0361

DOI:10.1145/3336122

Editor:
Walid G. Aref
Purdue University, USA

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 May 2019

Accepted: 01 January 2019

Revised: 01 January 2019

Received: 01 May 2018

Published in TSAS Volume 5, Issue 1

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Cited By

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
Alix GPapagelis MRenz MNascimento M(2023)PathletRL: Trajectory Pathlet Dictionary Construction using Reinforcement LearningProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625622(1-12)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625622
Chughtai JHaq IIslam SGani A(2022)A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector RegressionSensors10.3390/s2224973522:24(9735)Online publication date: 12-Dec-2022
https://doi.org/10.3390/s22249735
Chughtai JHaq IShafiq OMuneeb M(2022)Travel Time Prediction Using Hybridized Deep Feature Space and Machine Learning Based Heterogeneous EnsembleIEEE Access10.1109/ACCESS.2022.320638410(98127-98139)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3206384
Roy BRout D(2022)Predicting Taxi Travel Time Using Machine Learning Techniques Considering Weekend and HolidaysProceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021)10.1007/978-3-030-96302-6_24(258-267)Online publication date: 22-Feb-2022
https://doi.org/10.1007/978-3-030-96302-6_24

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