Advertisement

Genetic-Inspired Map Matching Algorithm for Real-Time GPS Trajectories

  • Saravjeet Singh
  • Jaiteg SinghEmail author
  • Sukhjit Singh Sehra
RESEARCH ARTICLE - SPECIAL ISSUE - INTELLIGENT COMPUTING and INTERDISCIPLINARY APPLICATIONS
  • 25 Downloads

Abstract

Complex road networks, inaccurate GPS receiver output, low sampling rate and many other associated issues pose real challenges for map matching process. Genetic algorithms have recently been trialed for rendering GPS fix on digital maps. This manuscript introduces an improvised genetic algorithm named as post-processing genetic-inspired map matching (GiMM) algorithm. The proposed GiMM intends to mitigate inherent challenges associated with originally proposed genetic algorithm for map matching. The fitness function used by GiMM makes use of Bucket Dijkstra’s and fast dynamic time wrapping (FDTW) algorithms to render GPS information on digital maps. Bucket Dijkstra’s suggests the shortest path available in between two points, and FDTW is responsible for comparing two data series. Unlike traditional genetic algorithm for map matching, GiMM was evaluated on sparse as well as dense GPS data. The performance of the GiMM algorithm was evaluated in real time using OpenStreetMap data and GPS dataset mapped onto a road network of 82 km. GiMM uses population size, generation count, accuracy and execution time as input parameters. A maximum accuracy of 99.4% with root-mean-square error 0.06 was observed, whereas a minimum of 60% accuracy was observed at 0.47 root-mean-square error. Number of iterations and population size were concluded to be the most influential parameters for the performance of genetic algorithms for map matching.

Keywords

Genetic algorithm Intelligent transport system Navigation system Crowdsourced dataset 

References

  1. 1.
    Scott, C.A.; Drane, C.: Increased accuracy of motor vehicle position estimation by utilising map data: vehicle dynamics, and other information sources. In: Vehicle Navigation and Information Systems Conference, 1994. Proceedings., 1994, pp. 585–590. IEEE (1994)Google Scholar
  2. 2.
    Skog, I.; Handel, P.: State-of-the-art in-car navigation: an overview. In: Eskandarian, A. (ed.) Handbook of Intelligent Vehicles, pp. 435–462. Springer, London (2012) CrossRefGoogle Scholar
  3. 3.
    Zhang, T.; Yang, D.G.; Li, J.T.; Lian, X.M.: A trajectory-based map-matching system for the driving road identification in vehicle navigation systems. J. Intell. Transp. Syst. 20(2), 162–177 (2016)CrossRefGoogle Scholar
  4. 4.
    Greenfeld, J.S.: Matching GPS observations to locations on a digital map. In: Transportation Research Board 81st Annual Meeting (2002)Google Scholar
  5. 5.
    Quddus, M.A.; Ochieng, W.Y.; Noland, R.B.: Current map-matching algorithms for transport applications: state-of-the art and future research directions. Transp. Res. Part C Emerg. Technol. 15(5), 312–328 (2007)CrossRefGoogle Scholar
  6. 6.
    Singh, J.; Singh, S.; Singh, S.; Singh, H.: Evaluating the performance of map matching algorithms for navigation systems: an empirical study. Spatial Inf. Res. 27(1), 63–74 (2019)CrossRefGoogle Scholar
  7. 7.
    Yin, Y.; Shah, R.R.; Zimmermann, R.: A general feature-based map matching framework with trajectory simplification. In: Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming, p. 7. ACM (2016)Google Scholar
  8. 8.
    Smaili, C.; El Najjar, M.E.B.; Charpillet, F.: A hybrid Bayesian framework for map matching: formulation using switching Kalman filter. J. Intell. Robot. Syst. 74(3–4), 725–743 (2014)CrossRefGoogle Scholar
  9. 9.
    Gong, Y.J.; Chen, E.; Zhang, X.; Ni, L.M.; Zhang, J.: AntMapper: an ant colony-based map matching approach for trajectory-based applications. IEEE Trans. Intell. Transp. Syst. 19(2), 390–401 (2018)CrossRefGoogle Scholar
  10. 10.
    Yang, C.; Gidofalvi, G.: Fast map matching, an algorithm integrating hidden Markov model with precomputation. Int. J. Geogr. Inf. Sci. 32(3), 547–570 (2018)CrossRefGoogle Scholar
  11. 11.
    Hou, X.; Luo, L.; Cai, W.; Hanai, M.: Fast online map matching for recovering travelling routes from low-sampling GPS data. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 917–924. IEEE (2018)Google Scholar
  12. 12.
    Shigezumi, J.; Asai, T.; Morikawa, H.; Inakoshi, H.: A fast algorithm for matching planar maps with minimum Frechet distances. In: Proceedings of the 4th International ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data, pp. 25–34. ACM (2015)Google Scholar
  13. 13.
    Atia, M.M.; Hilal, A.R.; Stellings, C.; Hartwell, E.; Toonstra, J.; Miners, W.B.; Basir, O.A.: A low-cost lane-determination system using GNSS/IMU fusion and HMM-based multistage map matching. IEEE Trans. Intell. Transp. Syst. 18(11), 3027–3037 (2017)CrossRefGoogle Scholar
  14. 14.
    Gkonos, C.; Giannopoulos, I.; Raubal, M.: Maps, vibration or gaze? Comparison of novel navigation assistance in indoor and outdoor environments. J. Locat. Based Serv. 11(1), 29–49 (2017) CrossRefGoogle Scholar
  15. 15.
    Taguchi, S.; Koide, S.; Yoshimura, T.: Online map matching with route prediction. IEEE Trans. Intell. Transp. Syst. 20(1), 338–347 (2018)CrossRefGoogle Scholar
  16. 16.
    Hashemi, M.; Karimi, H.A.: A critical review of real-time map-matching algorithms: current issues and future directions. Comput. Environ. Urban Syst. 48, 153–165 (2014)CrossRefGoogle Scholar
  17. 17.
    Quddus, M.A.; Ochieng, W.Y.; Zhao, L.; Noland, R.B.: A general map matching algorithm for transport telematics applications. GPS Solut. 7(3), 157–167 (2003)CrossRefGoogle Scholar
  18. 18.
    Sharath, M.; Velaga, N.R.; Quddus, M.A.: A dynamic two-dimensional (D2D) weight-based map-matching algorithm. Transp. Res. Part C Emerg. Technol. 98, 409–432 (2019)CrossRefGoogle Scholar
  19. 19.
    Yang, H.; Cheng, S.; Jiang, H.; An, S.: An enhanced weight-based topological map matching algorithm for intricate urban road network. Procedia Soc. Behav. Sci. 96, 1670–1678 (2013)CrossRefGoogle Scholar
  20. 20.
    Velaga, N.R.; Quddus, M.A.; Bristow, A.L.: Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems. Transp. Res. Part C Emerg. Technol. 17(6), 672–683 (2009)CrossRefGoogle Scholar
  21. 21.
    White, C.E.; Bernstein, D.; Kornhauser, A.L.: Some map matching algorithms for personal navigation assistants. Transp. Res. Part C Emerg. Technol. 8(1), 91–108 (2000)CrossRefGoogle Scholar
  22. 22.
    Mohammed Quddus, S.W.: Shortest path and vehicle trajectory aided map-matching for low frequency GPS data. Transp. Res. Part C Emerg. Technol. 55, 328–339 (2015)CrossRefGoogle Scholar
  23. 23.
    Zhao, X.; Cheng, X.; Zhou, J.; Xu, Z.; Dey, N.; Ashour, A.S.; Satapathy, S.C.: Advanced topological map matching algorithm based on DS theory. Arab. J. Sci. Eng. 43(8), 3863–3874 (2018)CrossRefGoogle Scholar
  24. 24.
    Jagadeesh, G.R.; Srikanthan, T.: Online map-matching of noisy and sparse location data with hidden Markov and route choice models. IEEE Trans. Intell. Transp. Syst. 18(9), 2423–2434 (2017)CrossRefGoogle Scholar
  25. 25.
    Goh, C.Y.; Dauwels, J.; Mitrovic, N.; Asif, M.T.; Oran, A.; Jaillet, P.: Online map-matching based on hidden markov model for real-time traffic sensing applications. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 776–781. IEEE (2012)Google Scholar
  26. 26.
    Shahidi, S.; Valaee, S.: Hidden markov model based graph matching for calibration of localization maps. In: 2015 IEEE International Conference on Communications (ICC), pp. 4606–4611. IEEE (2015)Google Scholar
  27. 27.
    Newson, P.; Krumm, J.: Hidden markov map matching through noise and sparseness. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 336–343. ACM (2009)Google Scholar
  28. 28.
    Chen, D.; Driemel, A.; Guibas, L.J.; Nguyen, A.; Wenk, C.: Approximate map matching with respect to the frechet distance. In: 2011 Proceedings of the Thirteenth Workshop on Algorithm Engineering and Experiments (ALENEX), pp. 75–83. SIAM (2011)CrossRefGoogle Scholar
  29. 29.
    Xu, Z.; Li, Y.; Rizos, C.; Xu, X.: Novel hybrid of LS-SVM and Kalman filter for GPS/INS integration. J. Navig. 63(02), 289–299 (2010)CrossRefGoogle Scholar
  30. 30.
    Krakiwsky, E.J.; Harris, C.B.; Wong, R.V.: A kalman filter for integrating dead reckoning, map matching and gps positioning. In: Position Location and Navigation Symposium, 1988. Record. Navigation into the 21st Century. IEEE PLANS’88., IEEE, pp. 39–46. IEEE (1988)Google Scholar
  31. 31.
    Liu, X.; Liu, K.; Li, M.; Lu, F.: A ST-CRF map-matching method for low-frequency floating car data. IEEE Trans. Intell. Transp. Syst. 18(5), 1241–1254 (2017)CrossRefGoogle Scholar
  32. 32.
    Lou, Y.; Zhang, C.; Zheng, Y.; Xie, X.; Wang, W.; Huang, Y.: Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 352–361. ACM (2009)Google Scholar
  33. 33.
    Bernstein, D.; Kornhauser, A.: An introduction to map matching for personal navigation assistants. Geographic information system New Jersey TIDE Center (1998). https://rosap.ntl.bts.gov/view/dot/38257
  34. 34.
    Kim, J.: Node based map matching algorithm for car navigation system. In: International Symposium on Automotive Technology & Automation (29th: 1996: Florence, Italy). Global deployment of advanced transportation telematics/ITS (1996)Google Scholar
  35. 35.
    Kim, S.; Kim, J.H.: Q-factor map matching method using adaptive fuzzy network. In: Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE’99. 1999 IEEE International, vol. 2, pp. 628–633. IEEE (1999)Google Scholar
  36. 36.
    Bouju, A.; Stockus, A.; Bertrand, F.; Boursier, P.: Location-based spatial data management in navigation systems. In: Intelligent Vehicle Symposium, 2002. IEEE, vol. 1, pp. 172–177. IEEE (2002)Google Scholar
  37. 37.
    El Najjar, M.E.; Bonnifait, P.: A road-matching method for precise vehicle localization using belief theory and Kalman filtering. Auton. Robot. 19(2), 173–191 (2005)CrossRefGoogle Scholar
  38. 38.
    Ochieng, W.Y.; Quddus, M.A.; Noland, R.B.: Map-matching in complex urban road networks. Braz. J. Cartography 55(2), 1–14 (2003)Google Scholar
  39. 39.
    Srinivasan, D.; Cheu, R.L.; Tan, C.W.: Development of an improved ERP system using GPS and AI techniques. In: Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE, vol. 1, pp. 554–559. IEEE (2003)Google Scholar
  40. 40.
    Taylor, G.; Blewitt, G.; Steup, D.; Corbett, S.; Car, A.: Road reduction filtering for GPS–GIS navigation. Trans. GIS 5(3), 193–207 (2001)CrossRefGoogle Scholar
  41. 41.
    Fu, M.; Li, J.; Wang, M.: A hybrid map matching algorithm based on fuzzy comprehensive judgment. In: International IEEE Conference on Intelligent Transportation Systems, pp. 613–617 (2003)Google Scholar
  42. 42.
    Meng, Y.; Chen, W.; Li, Z.; Chen, Y.; Chao, J.C.: A simplified map-matching algorithm for in-vehicle navigation unit. Geogr. Inf. Sci. 8(1), 24–30 (2002)Google Scholar
  43. 43.
    Sakic, E.: Map-matching algorithms for android applications. Bachelor thesis, Department of Electrical Engineering and Information Technology (2012)Google Scholar
  44. 44.
    Xu, A.; Yang, D.; Cao, F.; Xiao, W.; Law, C.; Ling, K.; Chua, H.: Prototype design and implementation for urban area in-car navigation system. In: The IEEE 5th International Conference on Intelligent Transportation Systems, 2002. Proceedings, pp. 517–521. IEEE (2002)Google Scholar
  45. 45.
    Yang, D.; Cai, B.; Yuan, Y.: An improved map-matching algorithm used in vehicle navigation system. In: Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE, vol. 2, pp. 1246–1250. IEEE (2003)Google Scholar
  46. 46.
    Syed, S.; Cannon, M.E.: Fuzzy logic-based map matching algorithm for vehicle navigation system in urban canyons. ION National Technical Meeting, San Diego, CA, vol. 1, pp. 26–28 (2004)Google Scholar
  47. 47.
    Pereira, F.C.; Costa, H.; Pereira, N.M.: An off-line map-matching algorithm for incomplete map databases. Eur. Transp. Res. Rev. 1(3), 107–124 (2009)MathSciNetCrossRefGoogle Scholar
  48. 48.
    Toledo-Moreo, R.; Bétaille, D.; Peyret, F.: Lane-level integrity provision for navigation and map matching with GNSS, dead reckoning, and enhanced maps. IEEE Trans. Intell. Transp. Syst. 11(1), 100–112 (2010)CrossRefGoogle Scholar
  49. 49.
    Li, L.; Quddus, M.; Zhao, L.: High accuracy tightly-coupled integrity monitoring algorithm for map-matching. Transp. Res. Part C Emerg. Technol. 36, 13–26 (2013)CrossRefGoogle Scholar
  50. 50.
    Li, Y.; Huang, Q.; Kerber, M.; Zhang, L.; Guibas, L.: Large-scale joint map matching of GPS traces. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 214–223. ACM (2013)Google Scholar
  51. 51.
    Yanagisawa, H.: An offline map matching via integer programming. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 4206–4209. IEEE (2010)Google Scholar
  52. 52.
    Miler, M.; Todić, F.; Ševrović, M.: Extracting accurate location information from a highly inaccurate traffic accident dataset: a methodology based on a string matching technique. Transp. Res. Part C Emerg. Technol. 68, 185–193 (2016)CrossRefGoogle Scholar
  53. 53.
    Nikoli, M.; Jovi, J.: Implementation of generic algorithm in map-matching model. Expert Syst. Appl. 72, 283–292 (2017)CrossRefGoogle Scholar
  54. 54.
    Naumann, S.; Kovalyov, M.Y.: Pedestrian route search based on OpenStreetMap. In: Intelligent Transport Systems and Travel Behaviour, pp. 87–96. Springer, Cham (2017) Google Scholar
  55. 55.
    Mohamed, R.; Aly, H.; Youssef, M.: Accurate real-time map matching for challenging environments. IEEE Trans. Intell. Transp. Syst. 18(4), 847–857 (2017)CrossRefGoogle Scholar
  56. 56.
    Qi, H.; Di, X.; Li, J.: Map-matching algorithm based on the junction decision domain and the hidden Markov model. PloS ONE 14(5), e0216476 (2019)CrossRefGoogle Scholar
  57. 57.
    Song, C.; Yan, X.; Stephen, N.; Khan, A.A.: Hidden markov model and driver path preference for floating car trajectory map matching. IET Intell. Transp. Syst. 12(10), 1433–1441 (2018)CrossRefGoogle Scholar
  58. 58.
    Bast, H.; Brosi, P.: Sparse map-matching in public transit networks with turn restrictions. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 480–483. ACM (2018)Google Scholar
  59. 59.
    Rappos, E.; Robert, S.; Cudre Mauroux, P.: A force-directed approach for offline GPS trajectory map matching. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 319–328. ACM (2018)Google Scholar
  60. 60.
    Loomis, P.V.W.: Vehicle navigation by dead reckoning and GNSS-aided map-matching. US Patent App. 15/270,299 (2018)Google Scholar
  61. 61.
    Ta, N.; Wang, J.; Li, G.: Map matching algorithms: an experimental evaluation. In: Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, pp. 182–198. Springer (2018)Google Scholar
  62. 62.
    Cao, K.; Wang, L.; Zuo, Z.; Sun, X.: A map matching algorithm combining twice gridding and weighting factors methods. In: International Symposium for Intelligent Transportation and Smart City, pp. 63–73. Springer (2019)Google Scholar
  63. 63.
    Chao, P.; Hua, W.; Zhou, X.: An iterative map-trajectory co-optimisation framework based on map-matching and map update. In: International Conference on Database Systems for Advanced Applications, pp. 305–309. Springer (2019)Google Scholar
  64. 64.
    Laboudi, Z.; Chikhi, S.: Comparison of genetic algorithm and quantum genetic algorithm. Int. Arab. J. Inf. Technol. 9(3), 243–249 (2012)Google Scholar
  65. 65.
    Salvador, S.; Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)CrossRefGoogle Scholar
  66. 66.
    Zhan, F.B.; Noon, C.E.: Shortest path algorithms: an evaluation using real road networks. Transp. Sci. 32(1), 65–73 (1998)CrossRefGoogle Scholar
  67. 67.
    Zhao, Y.: Vehicle Location and Navigation Systems. The Artech House ITS Series. Artech House, Boston (1997). http://www.worldcat.org/title/vehicle-location-and-navigationsystems/oclc/917742102
  68. 68.
    Sehra, S.S.; Singh, J.; Rai, H.S.: Assessing openstreetmap data using intrinsic quality indicators: an extension to the QGIS processing toolbox. Fut. Internet 9(2), 15 (2017)CrossRefGoogle Scholar
  69. 69.
    Puschner, P.; Koza, C.: Calculating the maximum execution time of real-time programs. Real-Time Syst. 1(2), 159–176 (1989)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpuraIndia
  2. 2.Elocity Technologies Inc.TorontoCanada

Personalised recommendations