research-article

AntMapper: An Ant Colony-Based Map Matching Approach for Trajectory-Based Applications

Authors:

Jun ZhangAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 19, Issue 2

Pages 390 - 401

https://doi.org/10.1109/TITS.2017.2697439

Published: 01 February 2018 Publication History

Abstract

Many trajectory-based applications require an essential step of mapping raw GPS trajectories onto the digital road network accurately. This task, commonly referred to as map matching, is challenging due to the measurement error of GPS devices in critical environment and the sampling error caused by long sampling intervals. Traditional algorithms focus on either a local or a global perspective to deal with the problem. To further improve the performance, this paper develops a novel map matching model that considers local geometric/topological information and a global similarity measure simultaneously. To accomplish the optimization goal in this complex model, we adopt an ant colony optimization algorithm that mimics the path finding process of ants transporting food in nature. The algorithm utilizes both local heuristic and global fitness to search the global optimum of the model. Experimental results verify that the proposed algorithm is able to provide accurate map matching results within a relatively short execution time.

References

[1]

W. Luo, H. Tan, L. Chen, and L. M. Ni, “ Finding time period-based most frequent path in big trajectory data,” in Proc. ACM Int. Conf. Manage. Data (SIGMOD), 2013, pp. 713–724.

Digital Library

[2]

Y. Wang, Y. Zheng, and Y. Xue, “ Travel time estimation of a path using sparse trajectories,” in Proc. 20th ACM Int. Conf. Knowl. Discovery Data Mining (SIGKDD), 2014, pp. 25–34.

Digital Library

[3]

A. Hofleitner, R. Herring, P. Abbeel, and A. Bayen, “ Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network,” IEEE Trans. Intell. Transp. Syst., vol. Volume 13, no. Issue 4, pp. 1679–1693, 2012.

Digital Library

[4]

S. Liu, L. M. Ni, and R. Krishnan, “ Fraud detection from taxis' driving behaviors,” IEEE Trans. Veh. Technol., vol. Volume 63, no. Issue 1, pp. 464–472, 2014.

[5]

Y. Zheng, Y. Liu, J. Yuan, and X. Xie, “ Urban computing with taxicabs,” in Proc. 13th ACM Int. Conf. Ubiquitous Comput. (UbiComp), 2011, pp. 89–98.

Digital Library

[6]

M. Hashemi and H. A. Karimi, “ A critical review of real-time map-matching algorithms: Current issues and future directions,” Comput. Environ. Urban Syst., vol. Volume 48, pp. 153–165, 2014.

[7]

C. E. White, D. Bernstein, and A. L. Kornhauser, “ Some map matching algorithms for personal navigation assistants,” Transp. Res. C, Emerg. Technol., vol. Volume 8, no. Issue 1, pp. 91–108, 2000.

[8]

S. Brakatsoulas, D. Pfoser, R. Salas, and C. Wenk, “ On map-matching vehicle tracking data,” in Proc. 31st Int. Conf. Very Large Data Bases (VLDB), 2005, pp. 853–864.

Digital Library

[9]

N. R. Velaga, M. A. Quddus, and A. L. Bristow, “ Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems,” Transp. Res. C, Emerg. Technol., vol. Volume 17, no. Issue 6, pp. 672–683, 2009.

[10]

H. Wei, Y. Wang, G. Forman, Y. Zhu, and H. Guan, “ Fast Viterbi map matching with tunable weight functions,” in Proc. 20th ACM Int. Conf. Adv. Geograph. Inf. Syst. (SIGSPACIAL), 2012, pp. 613–616.

Digital Library

[11]

M. Quddus and S. Washington, “ Shortest path and vehicle trajectory aided map-matching for low frequency GPS data,” Transp. Res. C, Emerg. Technol., vol. Volume 55, pp. 328–339, 2015.

[12]

M. Hashemi and H. A. Karimi, “ A weight-based map-matching algorithm for vehicle navigation in complex urban networks,” J. Intell. Transp. Syst., vol. Volume 20, no. Issue 6, pp. 573–590, 2016.

[13]

Y. Lou, C. Zhang, Y. Zheng, X. Xie, W. Wang, and Y. Huang, “ Map-matching for low-sampling-rate GPS trajectories,” in Proc. 17th ACM Int. Conf. Adv. Geograph. Inf. Syst. (SIGSPATIAL), 2009, pp. 352–361.

Digital Library

[14]

P. Newson and J. Krumm, “ Hidden Markov map matching through noise and sparseness,” in Proc. 17th ACM Int. Conf. Adv. Geograph. Inf. Syst. (SIGSPATIAL), 2009, pp. 336–343.

Digital Library

[15]

G. Wang and R. Zimmermann, “ Eddy: An error-bounded delay-bounded real-time map matching algorithm using HMM and online Viterbi decoder,” in Proc. 22nd ACM Int. Conf. Adv. Geograph. Inf. Syst. (SIGSPATIAL), 2014, pp. 33–42.

Digital Library

[16]

C. Fouque and P. Bonnifait, “ Matching raw GPS measurements on a navigable map without computing a global position,” IEEE Trans. Intell. Transp. Syst., vol. Volume 13, no. Issue 2, pp. 887–898, 2012.

Digital Library

[17]

T. Hunter, P. Abbeel, and A. Bayen, “ The path inference filter: Model-based low-latency map matching of probe vehicle data,” IEEE Trans. Intell. Transp. Syst., vol. Volume 15, no. Issue 2, pp. 507–529, 2014.

[18]

F. Abdallah, G. Nassreddine, and T. Denoeux, “ A multiple-hypothesis map-matching method suitable for weighted and box-shaped state estimation for localization,” IEEE Trans. Intell. Transp. Syst., vol. Volume 12, no. Issue 4, pp. 1495–1510, 2011.

Digital Library

[19]

M. A. Quddus, R. B. Noland, and W. Y. Ochieng, “ A high accuracy fuzzy logic based map matching algorithm for road transport,” J. Intell. Transp. Syst., vol. Volume 10, no. Issue 3, pp. 103–115, 2006.

[20]

S. Syed and M. Cannon, “ Fuzzy logic-based map matching algorithm for vehicle navigation system in urban canyons,” in Proc. ION Nat. Tech. Meeting, vol. Volume 1 . San Diego, CA, USA, 2004, pp. 26–28.

[21]

M. Ren and H. A. Karimi, “ A fuzzy logic map matching for wheelchair navigation,” GPS Solutions, vol. Volume 16, no. Issue 3, pp. 273–282, 2012.

[22]

M. Dorigo and L. M. Gambardella, “ Ant colony system: A cooperative learning approach to the traveling salesman problem,” IEEE Trans. Evol. Comput., vol. Volume 1, no. Issue 1, pp. 53–66, 1997.

Digital Library

[23]

E. Bonabeau, M. Dorigo, and G. Theraulaz, “ Inspiration for optimization from social insect behaviour,” Nature, vol. Volume 406, no. Issue 6791, pp. 39–42, 2000.

[24]

M. A. Quddus, W. Y. Ochieng, and R. B. Noland, “ Current map-matching algorithms for transport applications: State-of-the art and future research directions,” Transp. Res. C, Emerg. Technol., vol. Volume 15, no. Issue 5, pp. 312–328, 2007.

[25]

G. Taylor, G. Blewitt, D. Steup, S. Corbett, and A. Car, “ Road reduction filtering for GPS-GIS navigation,” Trans. GIS, vol. Volume 5, no. Issue 3, pp. 193–207, 2001.

[26]

H. Wu, W. Sun, and B. Zheng, “ Is only one GPS position sufficient to locate you to the road network accurately?” in Proc. ACM Int. Joint Conf. Pervasive Ubiquitous Comput. (UbiComp), 2016, pp. 740–751.

Digital Library

[27]

R. R. Joshi, “ A new approach to map matching for in-vehicle navigation systems: The rotational variation metric,” in Proc. IEEE Intell. Transp. Syst., Aug. 2001, pp. 33–38.

[28]

M. Xu, Y. Du, J. Wu, and Y. Zhou, “ Map matching based on conditional random fields and route preference mining for uncertain trajectories,” Math. Problems Eng., vol. Volume 2015, pp. 1–13, 2015.

[29]

M. Rohani, D. Gingras, and D. Gruyer, “ A novel approach for improved vehicular positioning using cooperative map matching and dynamic base station DGPS concept,” IEEE Trans. Intell. Transp. Syst., vol. Volume 17, no. Issue 1, pp. 230–239, 2016.

Digital Library

[30]

M. Rohani, D. Gingras, and D. Gruyer, “ A new decentralized Bayesian approach for cooperative vehicle localization based on fusion of GPS and inter-vehicle distance measurements,” IEEE Intell. Transp. Syst. Mag., vol. Volume 7, no. Issue 2, pp. 85–95, 2015.

[31]

A. A. Goshtasby, “<chapter-title>Similarity and dissimilarity measures</chapter-title>,” in Image Registration . London, U.K.: Springer, 2012, pp. 7–66.

[32]

S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th ed. Orlando, FL, USA: Academic, 2009.

Digital Library

[33]

B. Yu and Z. Z. Yang, “ An ant colony optimization model: The period vehicle routing problem with time windows,” Transp. Res. E, Logistics Transp. Rev., vol. Volume 47, no. Issue 2, pp. 166–181, 2011.

[34]

G. Kim, Y. S. Ong, C. K. Heng, P. S. Tan, and N. A. Zhang, “ City vehicle routing problem (city VRP): A review,” IEEE Trans. Intell. Transp. Syst., vol. Volume 16, no. Issue 4, pp. 1654–1666, 2015.

[35]

J. Cheng, J. Cheng, M. Zhou, F. Liu, S. Gao, and C. Liu, “ Routing in Internet of vehicles: A review,” IEEE Trans. Intell. Transp. Syst., vol. Volume 16, no. Issue 5, pp. 2339–2352, 2015.

[36]

W. Fang, S. Yang, and X. Yao, “ A survey on problem models and solution approaches to rescheduling in railway networks,” IEEE Trans. Intell. Transp. Syst., vol. Volume 16, no. Issue 6, pp. 2997–3016, 2015.

[37]

Y.-J. Zheng, M.-X. Zhang, H.-F. Ling, and S.-Y. Chen, “ Emergency railway transportation planning using a hyper-heuristic approach,” IEEE Trans. Intell. Transp. Syst., vol. Volume 16, no. Issue 1, pp. 321–329, 2015.

[38]

S. Su, T. Tang, and C. Roberts, “ A cooperative train control model for energy saving,” IEEE Trans. Intell. Transp. Syst., vol. Volume 16, no. Issue 2, pp. 622–631, 2015.

[39]

J. B. Wang, M. Chen, X. Wan, and C. Wei, “ Ant-colony-optimization-based scheduling algorithm for uplink CDMA nonreal-time data,” IEEE Trans. Veh. Technol., vol. Volume 58, no. Issue 1, pp. 231–241, 2009.

[40]

W.-N. Chen and J. Zhang, “ Ant colony optimization for software project scheduling and staffing with an event-based scheduler,” IEEE Trans. Softw. Eng., vol. Volume 39, no. Issue 1, pp. 1–17, 2013.

Digital Library

[41]

Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs . Berlin, Germany: Springer-Verlag, 1996.

Digital Library

[42]

A. Lipowski and D. Lipowska, “ Roulette-wheel selection via stochastic acceptance,” Phys. A, Statist. Mech. Appl., vol. Volume 391, no. Issue 6, pp. 2193–2196, 2012.

[43]

M. Dorigo and T. Stützle, Ant Colony Optimization . Cambridge, MA, USA: MIT Press, 2004.

Digital Library

[44]

A. Pasternack, “<chapter-title>If you're a foreigner using GPS in China, you could a spy</chapter-title>,” Vice Media, New York, NY, USA, 2013.

[45]

Restrictions on Geographic Data in China Wikipedia, 2016. {Online}. Available: https://en.wikipedia.org/wiki/Restrictions_on_geographic_data_in_China

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 19, Issue 2

February 2018

328 pages

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Copyright © 2018.

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IEEE Press

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Published: 01 February 2018

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https://dl.acm.org/doi/10.1109/TMC.2024.3413589
Haoyan WYuangang LShaohua LBo LZongyi H(2023)A Path Increment Map Matching Method for High-Frequency TrajectoryIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.328141824:10(10948-10962)Online publication date: 1-Oct-2023
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Soares de Sousa RBoukerche ALoureiro A(2023)A Novel Scalable Framework to Reconstruct Vehicular Trajectories From Unreliable GPS DatasetsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.328011424:9(9658-9669)Online publication date: 1-Sep-2023
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Dogramadzi MKhan A(2022)Accelerated Map Matching for GPS TrajectoriesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.304637523:5(4593-4602)Online publication date: 1-May-2022
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Peng JCao YDing ZYan J(2019)A Fast Real-time Map-Matching for Unstable Sampling-rate GPS TrajectoriesProceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City10.1145/3377170.3377195(253-258)Online publication date: 20-Dec-2019
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