Skip to main content
Log in

Analytical Review of Map Matching Algorithms: Analyzing the Performance and Efficiency Using Road Dataset of the Indian Subcontinent

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Precise position information of moving entities on digital road networks is a vital requirement of location-based applications. Location information received from Global Positioning System has some positional error and this inaccurate information generates errors in further processing of navigation and location-based applications. Map matching algorithms are responsible for the prediction of precise location by considering different parameters of the device. Many map matching algorithms were developed by the research community so as to improve performance and accuracy. These algorithms are categorized into different categories. This paper briefly explains the category-wise working of map matching algorithms and also provides analytically reviews of the performance of these algorithms. Five different algorithms from each category were considered in this experiment. The performance of five basic map matching algorithms was further evaluated on the digital road network of the Indian subcontinent. Six separate routes ranging in length from 0.2 to 55 km were used to analyze the efficiency of considered algorithms. This analytical review provides a performance and accuracy comparison of point to point, topological, Kalman filter-based, Hidden Markov Model-based, and Frechet distance-based map matching algorithms. This review concludes that for online map matching, Hidden Markov model-based map matching algorithm provides good accuracy in comparison to other considered algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. Scott CA, Drane C (1994) 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. IEEE, pp 585–590

  2. Zhao Y (1997) Vehicle location and navigation systems. Artech House Publishers, London

    MATH  Google Scholar 

  3. Mishra A, Lee S, Kim D, Kim S (2022) In-cabin monitoring system for autonomous vehicles. Sensors 22(12):4360

    Article  Google Scholar 

  4. Mishra A, Cha J, Kim S (2022) Privacy-preserved in-cabin monitoring system for autonomous vehicles. Comput Intell Neurosci. https://doi.org/10.1155/2022/5389359

    Article  Google Scholar 

  5. Dutta M, Gupta D, Sahu S, Limkar S, Singh P, Mishra A et al (2023) Evaluation of growth responses of lettuce and energy efficiency of the substrate and smart hydroponics cropping system. Sensors 23(4):1875

    Article  Google Scholar 

  6. Mishra A, Kim J, Cha J, Kim D, Kim S (2021) Authorized traffic controller hand gesture recognition for situation-aware autonomous driving. Sensors 21(23):7914

    Article  Google Scholar 

  7. Chu HJ, Tsai GJ, Chiang KW, Duong TT (2013) GPS/MEMS INS data fusion and map matching in urban areas. Sensors 13(9):11280–11288

    Article  Google Scholar 

  8. Jimenez F, Monzon S, Naranjo JE (2016) Definition of an enhanced map-matching algorithm for urban environments with poor GNSS signal quality. Sensors 16(2):193

    Article  Google Scholar 

  9. Meng J, Ren M, Wang P, Zhang J, Mou Y (2020) Improving positioning accuracy via map matching algorithm for visual-inertial odometer. Sensors 20(2):552

    Article  Google Scholar 

  10. Brakatsoulas S, Pfoser D, Salas R, Wenk C (2005) On map-matching vehicle tracking data. In: Proceedings of the 31st international conference on Very large data bases. pp 853–864

  11. Wenk C, Salas R, Pfoser D (2006) Addressing the need for map-matching speed: localizing global curve-matching algorithms. In: 18th international conference on scientific and statistical database management (SSDBM’06). IEEE, pp 379–388

  12. Karimi HA, Asavasuthirakul D (2014) A novel optimal routing for navigation systems/services based on global navigation satellite system quality of service. J Intell Transp Syst 18(3):286–298

    Article  Google Scholar 

  13. Quddus MA, Ochieng WY, Noland RB (2007) Current map-matching algorithms for transport applications: state-of-the art and future research directions. Transp Res C 15(5):312–328

    Article  Google Scholar 

  14. Singh S, Singh J, Sehra SS (2020) Genetic-inspired map matching algorithm for real-time GPS trajectories. Arab J Sci Eng 45(4):2587–2603

    Article  Google Scholar 

  15. Singh S, Singh J (2022) Map matching algorithm: empirical review based on Indian OpenStreetMap road network data. Int Arab J Inf Technol 19(2):143–149

    Google Scholar 

  16. Rahmani M, Koutsopoulos HN (2013) Path inference from sparse floating car data for urban networks. Transp Res C 30:41–54

    Article  Google Scholar 

  17. Rahmani M, Jenelius E, Koutsopoulos HN (2015) Non-parametric estimation of route travel time distributions from low-frequency floating car data. Transp Res C 58:343–362

    Article  Google Scholar 

  18. Singh S, Singh J (2020) Analysis of GPS trajectories mapping on shape files using spatial computing approaches. In: International conference on big data analytics. Springer, pp 91–100

  19. Lee YJ, Suhr JK, Jung HG (2021) Map matching based driving lane recognition for low-cost precise vehicle positioning on highways. IEEE Access 9:42192–42205

    Article  Google Scholar 

  20. Yumaganov A, Agafonov A, Myasnikov V (2021) An improved map matching algorithm based on dynamic programming approach. In: Information technology for management: towards business excellence: 15th conference, ISM 2020, and FedCSIS-IST 2020 track, held as part of FedCSIS, Sofia, Bulgaria, September 6–9, 2020, extended and revised selected papers 15. Springer International Publishing, pp 87–102

  21. Ptovsek V, Rapant L, Martinovivc J (2020) Floating car data map-matching utilizing the Dijkstra’s algorithm. In: Data management, analytics and innovation. Springer, pp 115–130

  22. Srinivasan D, Cheu RL, Tan CW (2003) Development of an improved ERP system using GPS and AI techniques. In: Intelligent transportation systems, 2003. Proceedings, vol 1. IEEE, pp 554–559

  23. Miwa T, Kiuchi D, Yamamoto T, Morikawa T (2012) Development of map matching algorithm for low frequency probe data. Transp Res C 22:132–145

    Article  Google Scholar 

  24. Zhao L, Ochieng WY, Quddus MA, Noland RB (2003) An extended Kalman filter algorithm for integrating GPS and low cost dead reckoning system data for vehicle performance and emissions monitoring. J Navig 56(2):257–275

    Article  Google Scholar 

  25. Jiang L, Chen C, Chen C, Huang H, Guo B (2022) From driving trajectories to driving paths: a survey on map-matching algorithms. CCF Trans Pervasive Comput Interact. https://doi.org/10.1007/s42486-022-00101-w

    Article  Google Scholar 

  26. Singh J, Singh S, Singh S, Singh H (2019) Evaluating the performance of map matching algorithms for navigation systems: an empirical study. Spat Inf Res 27(1):63–74

    Article  Google Scholar 

  27. Hashemi M, Karimi HA (2014) A critical review of real-time map-matching algorithms: current issues and future directions. Comput Environ Urban Syst 48:153–165

    Article  Google Scholar 

  28. Huang Z, Qiao S, Han N, Ca Yuan, Song X, Xiao Y (2021) Survey on vehicle map matching techniques. CAAI Trans Intell Technol 6(1):55–71

    Article  Google Scholar 

  29. Chao P, Xu Y, Hua W, Zhou X (2020) A survey on map-matching algorithms. In: Databases theory and applications: 31st Australasian Database Conference, ADC 2020, Melbourne, VIC, Australia, February 3–7, 2020, Proceedings 31. Springer, pp 121–133

  30. Choudhary S, Bhatia V, Ramkumar K (2020) IoT based navigation system for visually impaired people. In: 2020 8th international conference on reliability, Infocom technologies and optimization (trends and future directions) (ICRITO). IEEE, pp 521–525

  31. Krakiwsky EJ, Harris CB, Wong RV (1988) 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

  32. Iwaki F, Kakihara M, Sasaki M (1989) Recognition of vehicle’s location for navigation. In: Conference record of papers presented at the First Vehicle Navigation and Information Systems Conference (VNIS’89). IEEE, pp 131–138

  33. Zhang X, Wang Q, Wan D (2003) The relationship among vehicle positioning performance, map quality, and sensitivities and feasibilities of map-matching algorithms. In: IEEE IV2003 intelligent vehicles symposium. Proceedings (Cat. No. 03TH8683). IEEE, pp 468–473

  34. Greenfeld JS (2002) Matching GPS observations to locations on a digital map. In: Transportation research board 81st annual meeting, vol 22. pp 576–582

  35. Raanan MG, Shoval N (2014) Mental maps compared to actual spatial behavior using GPS data: a new method for investigating segregation in cities. Cities 36:28–40

    Article  Google Scholar 

  36. Koch T (2008) Maps: finding our place in the world. Cartogr Perspect 60(1):72–76

    Article  Google Scholar 

  37. Vinken R (1986) Digital geoscientific maps: a priority program of the German Society for the Advancement of Scientific Research. Math Geol 18(2):237–246

    Article  Google Scholar 

  38. Kamijo S, Okumura K, Kitamura A (1989) Digital road map database for vehicle navigation and road information systems. In: Conference record of papers presented at the First Vehicle Navigation and Information Systems Conference (VNIS89). IEEE, pp 319–323

  39. Goodwin CW, Lau JW (1993) Vehicle navigation and map quality. In: Proceedings of VNIS’93-vehicle navigation and information systems conference. IEEE, pp 17–20

  40. Getting IA (1993) Perspective/navigation—the global positioning system. IEEE Spectr 30(12):36–38

    Article  Google Scholar 

  41. Collier W (1990) In-vehicle route guidance systems using map-matched dead reckoning. In: IEEE symposium on position location and navigation. A decade of excellence in the navigation sciences. IEEE, pp 359–363

  42. Kim J (1996) 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. pp 121–126

  43. Mattos PG (1994) Integrated GPS and dead reckoning for low-cost vehicle navigation and tracking. In: Proceedings of VNIS’94-1994 vehicle navigation and information systems conference. IEEE, pp 569–574

  44. Jo T, Haseyama M, Kitajima H (1996) A map matching method with the innovation of the Kalman filtering. IEICE Trans Fundam Electron Commun Comput Sci 79(11):1853–1855

    Google Scholar 

  45. Bernstein D, Kornhauser A et al (1996) An introduction to map matching for personal navigation assistants. US Transportation Collection, pp 1–14

  46. Carstensen LW Jr (1998) GPS and GIS: enhanced accuracy in map matching through effective filtering of autonomous GPS points. Cartogr Geogr Inf Syst 25(1):51–62

    Google Scholar 

  47. Kim S, Kim JH (1999) Q-factor map matching method using adaptive fuzzy network. In: Fuzzy systems conference proceedings, 1999. FUZZ-IEEE’99. 1999 IEEE international, vol 2. IEEE, pp 628–633

  48. White CE, Bernstein D, Kornhauser AL (2000) Some map matching algorithms for personal navigation assistants. Transp Res C 8(1):91–108

    Article  Google Scholar 

  49. Ebner J (2001) Dead reckoning and estimated positions. Perform Res 6(3):3–7

    Article  Google Scholar 

  50. Witte T, Wilson A (2005) Accuracy of WAAS-enabled GPS for the determination of position and speed over ground. J Biomech 38(8):1717–1722

    Article  Google Scholar 

  51. Kim W, Jee GI, Lee J (2000) Efficient use of digital road map in various positioning for ITS. In: Position location and navigation symposium. IEEE, pp 170–176

  52. Taylor G, Blewitt G, Steup D, Corbett S, Car A (2001) Road reduction filtering for GPS-GIS navigation. Trans GIS 5(3):193–207

    Article  Google Scholar 

  53. Xu A, Yang D, Cao F, Xiao W, Law C, Ling K et al (2002) Prototype design and implementation for urban area in-car navigation system. In: Intelligent transportation systems, 2002. Proceedings. The IEEE 5th international conference on. IEEE, pp 517–521

  54. Bouju A, Stockus A, Bertrand F, Boursier P (2002) Location-based spatial data management in navigation systems. In: Intelligent vehicle symposium, 2002, vol 1. IEEE, pp 172–177

  55. Meng Y, Chen W, Li Z, Chen Y, Chao JC (2002) A simplified map-matching algorithm for in-vehicle navigation unit. Geogr Inf Sci 8(1):24–30

    Google Scholar 

  56. Yang D, Cai B, Yuan Y (2003) An improved map-matching algorithm used in vehicle navigation system. In: Intelligent transportation systems, 2003. Proceedings, vol 2. IEEE, pp 1246–1250

  57. Quddus MA, Ochieng WY, Zhao L, Noland RB (2003) A general map matching algorithm for transport telematics applications. GPS Solut 7(3):157–167

    Article  Google Scholar 

  58. Marchal F, Hackney J, Axhausen K (2005) Efficient map matching of large global positioning system data sets: tests on speed-monitoring experiment in Zürich. Transp Res Rec 1935(1):93–100

    Article  Google Scholar 

  59. Blazquez C, Vonderohe A (2005) Simple map-matching algorithm applied to intelligent winter maintenance vehicle data. Transp Res Rec 1935(1):68–76

    Article  Google Scholar 

  60. Wang Y, Xiong R, Tang P, Liu Y (2023) Fast and reliable map matching from large-scale noisy positioning records. J Comput Civ Eng 37(1):04022040

    Article  Google Scholar 

  61. Ochieng WY, Quddus MA, Noland RB (2003) Map-matching in complex urban road networks. J Cartogr 55(2):1–14

    Google Scholar 

  62. Fu M, Li J, Wang M (2003) A hybrid map matching algorithm based on fuzzy comprehensive judgment. In: International IEEE conference on intelligent transportation systems. pp 613–617

  63. Velaga NR, Quddus MA, Bristow AL (2009) Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems. Transp Res C 17(6):672–683

    Article  Google Scholar 

  64. Yang H, Cheng S, Jiang H, An S (2013) An enhanced weight-based topological map matching algorithm for intricate urban road network. Procedia Soc Behav Sci 96:1670–1678

    Article  Google Scholar 

  65. Li L, Quddus M, Zhao L (2013) High accuracy tightly-coupled integrity monitoring algorithm for map-matching. Transp Res C 36:13–26

    Article  Google Scholar 

  66. Li J, Taylor G, Kidner DB (2005) Accuracy and reliability of map-matched GPS coordinates: the dependence on terrain model resolution and interpolation algorithm. Comput Geosci 31(2):241–251

    Article  Google Scholar 

  67. Zhu Y, Jiang M, Yamamoto T (2022) Glocal map-matching algorithm for high-frequency and large-scale GPS data. J Intell Transport Syst. https://doi.org/10.1080/15472450.2022.2086805

    Article  Google Scholar 

  68. Chhabra R, Verma S, Krishna CR (2017) A survey on driver behavior detection techniques for intelligent transportation systems. In: 2017 7th international conference on cloud computing, data science & engineering-confluence. IEEE, pp 36–41

  69. Syed S, Cannon ME (2004) Fuzzy logic-based map matching algorithm for vehicle navigation system in urban canyons. In: ION national technical meeting, San Diego, CA, vol 1. pp 26–28

  70. Sakic E (2012) Map-matching algorithms for Android applications. Bachelor Thesis, Department of Electrical Engineering and Information Technology

  71. Newson P, Krumm J (2009) Hidden Markov map matching through noise and sparseness. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 336–343

  72. Hunter T, Abbeel P, Bayen AM (2013) The path inference filter: model-based low-latency map matching of probe vehicle data. In: Algorithmic foundations of robotics X. Springer, pp 591–607

  73. Li W, Zhang W, Gao C (2022) A historical-trajectories-based map matching algorithm for container positioning and tracking. Sensors 22(8):3057

    Article  Google Scholar 

  74. Tang J, Zhang S, Zou Y, Liu F (2017) An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data. PLoS ONE 12(12):e0188796

    Article  Google Scholar 

  75. El Najjar ME, Bonnifait P (2005) A road-matching method for precise vehicle localization using belief theory and Kalman filtering. Auton Robot 19(2):173–191

    Article  Google Scholar 

  76. Minett CF, Salomons AM, Daamen W, Van Arem B, Kuijpers S (2011) Eco-routing: comparing the fuel consumption of different routes between an origin and destination using field test speed profiles and synthetic speed profiles. In: Integrated and sustainable transportation system (FISTS), 2011 IEEE Forum on. IEEE, pp 32–39

  77. Ahn K, Rakha HA (2013) Network-wide impacts of eco-routing strategies: a large-scale case study. Transp Res D 25:119–130

    Article  Google Scholar 

  78. Kubivcka M, Klusavcek J, Sciarretta A, Cela A, Mounier H, Thibault L et al (2016) Performance of current eco-routing methods. In: Intelligent vehicles symposium (IV). IEEE, pp 472–477

  79. Lou Y, Zhang C, Zheng Y, Xie X, Wang W, Huang Y (2009) Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 352–361

  80. Zhang Y, Sui X (2021) RCIVMM: a route choice-based interactive voting map matching approach for complex urban road networks. IEEE Trans Big Data

  81. Toledo-Moreo R, Bétaille D, Peyret F (2010) 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

    Article  Google Scholar 

  82. Hunter T, Abbeel P, Bayen A (2014) The path inference filter: model-based low-latency map matching of probe vehicle data. IEEE Trans Intell Transp Syst 15(2):507–529

    Article  Google Scholar 

  83. Mohamed R, Aly H, Youssef M (2014) Accurate and efficient map matching for challenging environments. In: Proceedings of the 22nd ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 401–404

  84. Mohamed R, Aly H, Youssef M (2017) Accurate real-time map matching for challenging environments. IEEE Trans Intell Transp Syst 18(4):847–857

    Article  Google Scholar 

  85. Bierlaire M, Frejinger E (2008) Route choice modeling with network-free data. Transp Res C 16(2):187–198

    Article  Google Scholar 

  86. Xu H, Liu H, Tan CW, Bao Y (2010) Development and application of an enhanced Kalman filter and global positioning system error-correction approach for improved map-matching. J Intell Transport Syst 14(1):27–36

    Article  MATH  Google Scholar 

  87. Bierlaire M, Chen J, Newman J (2013) A probabilistic map matching method for smartphone GPS data. Transp Res C 26:78–98

    Article  Google Scholar 

  88. Gong YJ, Chen E, Zhang X, Ni LM, Zhang J (2018) AntMapper: an ant colony-based map matching approach for trajectory-based applications. IEEE Trans Intell Transp Syst 19(2):390–401

    Article  Google Scholar 

  89. Miler M, Todić F, Ševrović M (2016) Extracting accurate location information from a highly inaccurate traffic accident dataset: a methodology based on a string matching technique. Transp Res C 68:185–193

    Article  Google Scholar 

  90. Kubivcka M, Cela A, Moulin P, Mounier H, Niculescu SI (2015) Dataset for testing and training of map-matching algorithms. In: Intelligent vehicles symposium (IV). IEEE, pp 1088–1093

  91. Nikolić M, Jović J (2016) Implementation of generic algorithm in map-matching model. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2016.10.061

    Article  Google Scholar 

  92. Naumann S, Kovalyov MY (2017) Pedestrian route search based on OpenStreetMap. In: Intelligent transport systems and travel behaviour. Springer, pp 87–96

  93. Zhu L, Holden JR, Gonder JD (2017) Trajectory segmentation map-matching approach for large-scale, high-resolution GPS data. Transp Res Rec 2645(1):67–75

    Article  Google Scholar 

  94. Atia MM, Hilal AR, Stellings C, Hartwell E, Toonstra J, Miners WB et al (2017) 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

    Article  Google Scholar 

  95. Loomis P. Vehicle navigation by dead reckoning and GNSS-aided map-matching. Google Patents. US Patent App. 15/270,299

  96. Liu X, Liu K, Li M, Lu F (2017) A ST-CRF map-matching method for low-frequency floating car data. IEEE Trans Intell Transp Syst 18(5):1241–1254

    Article  Google Scholar 

  97. Jagadeesh GR, Srikanthan T (2017) 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

    Article  Google Scholar 

  98. Yang C, Gidofalvi G (2018) Fast map matching, an algorithm integrating hidden Markov model with precomputation. Int J Geogr Inf Sci 32(3):547–570

    Article  Google Scholar 

  99. Zhao X, Cheng X, Zhou J, Xu Z, Dey N, Ashour AS et al (2018) Advanced topological map matching algorithm based on D–S theory. Arab J Sci Eng 43(8):3863–3874

    Article  Google Scholar 

  100. Chen C, Ding Y, Xie X, Zhang S (2018) A three-stage online map-matching algorithm by fully using vehicle heading direction. J Ambient Intell Humaniz Comput 9(5):1623–1633

    Article  Google Scholar 

  101. Karamete BK, Adhami L, Glaser E (2021) An adaptive Markov chain algorithm applied over map-matching of vehicle trip GPS data. Geo-spat Inf Sci. https://doi.org/10.1080/10095020.2020.1866956

    Article  Google Scholar 

  102. Zhang H, Luo Y, Yu Q, Zheng X, Li X (2021) Map-matching approach based on link factor and hidden Markov model. J Intell Fuzzy Syst 40(3):5455–5471

    Article  Google Scholar 

  103. Hsueh YL, Chen HC (2018) Map matching for low-sampling-rate GPS trajectories by exploring real-time moving directions. Inf Sci 433:55–69

    Article  MathSciNet  Google Scholar 

  104. Maaref M, Kassas ZM (2019) A closed-loop map-matching approach for ground vehicle navigation in GNSS-denied environments using signals of opportunity. IEEE Trans Intell Transp Syst

  105. Sharath M, Velaga NR, Quddus MA (2019) A dynamic two-dimensional (D2D) weight-based map-matching algorithm. Transp Res C 98:409–432

    Article  Google Scholar 

  106. He K, Cao Q, Ren G, Li D, Zhang S (2021) Map matching for fixed sensor data based on utility theory. J Adv Transp. https://doi.org/10.1155/2021/5585131

    Article  Google Scholar 

  107. Ding Y, Zhou X, Liao Q, Tan H, Luo Q, Ni LM (2021) iMatching: an interactive map-matching system. Neurocomputing. https://doi.org/10.1016/j.neucom.2020.04.155

    Article  Google Scholar 

  108. Cao Q, Ren G, Li D, Li H, Ma J (2021) Map matching for sparse automatic vehicle identification data. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2021.3058123

    Article  Google Scholar 

  109. Trogh J, Botteldooren D, De Coensel B, Martens L, Joseph W, Plets D (2020) Map matching and lane detection based on Markovian behavior, GIS, and IMU data. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.3031080

    Article  Google Scholar 

  110. Dogramadzi M, Khan A (2021) Accelerated map matching for GPS trajectories. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/tits.2020.3046375

    Article  Google Scholar 

  111. Wang X, Gilliam C, Kealy A, Close J, Moran B (2022) Probabilistic map matching for robust inertial navigation aiding. arXiv Preprint. arXiv:2203.16932

  112. Feng J, Li Y, Zhao K, Xu Z, Xia T, Zhang J et al (2020) DeepMM: deep learning based map matching with data augmentation. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2020.3043500

    Article  Google Scholar 

  113. Chambers E, Fasy BT, Wang Y, Wenk C (2020) Map-matching using shortest paths. ACM Trans Spat Algorithms Syst 6(1):1–17

    Article  Google Scholar 

  114. Kassas ZZM, Maaref M, Morales JJ, Khalife JJ, Shamei K (2020) Robust vehicular localization and map matching in urban environments through IMU, GNSS, and cellular signals. IEEE Intell Transp Syst Mag 12(3):36–52

    Article  Google Scholar 

  115. Luo L, Hou X, Cai W, Guo B (2020) Incremental route inference from low-sampling GPS data: an opportunistic approach to online map matching. Inf Sci 512:1407–1423

    Article  Google Scholar 

  116. Zhang D, Dong Y, Guo Z (2021) A turning point-based offline map matching algorithm for urban road networks. Inf Sci 565:32–45

    Article  MathSciNet  Google Scholar 

  117. Jin Z, Kim J, Yeo H, Choi S (2022) Transformer-based map-matching model with limited labeled data using transfer-learning approach. Transp Res C 140:103668

    Article  Google Scholar 

  118. Ta N, Wang J, Li G (2018) 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. Springer, pp 182–198

  119. Mantri A, Dutt S, Gupta J, Chitkara M (2008) Design and evaluation of a PBL-based course in analog electronics. IEEE Trans Educ 51(4):432–438

    Article  Google Scholar 

  120. Sehra SS, Singh J, Rai HS (2017) Assessing OpenStreetMap data using intrinsic quality indicators: an extension to the QGIS processing toolbox. Future Internet 9(2):15

    Article  Google Scholar 

  121. Sehra SS, Rai HS, Singh J (2015) Quality assessment of crowdsourced data against custom recorded map data. Indian J Sci Technol. https://doi.org/10.17485/ijst/2015/v8i33/79884

  122. Sehra SS (2014) Assessing the topological consistency of crowdsourced OpenStreetMap data. Hum Comput 1(2):267–282

    Article  Google Scholar 

  123. Xu Z, Li Y, Rizos C, Xu X (2010) Novel hybrid of LS-SVM and Kalman filter for GPS/INS integration. J Navig 63(02):289–299

    Article  Google Scholar 

  124. Zeng Z, Zhang T, Li Q, Wu Z, Zou H, Gao C (2016) Curvedness feature constrained map matching for low-frequency probe vehicle data. Int J Geogr Inf Sci 30(4):660–690

    Article  Google Scholar 

  125. Arregui H, Loyo E, Otaegui O, Arbelaitz O (2017) Impact of the road network configuration on map-matching algorithms for FCD in urban environments. IET Intel Transp Syst 12(1):12–21

    Article  Google Scholar 

  126. Singh S, Singh J (2020) Intrinsic parameters based quality assessment of Indian OpenStreetMap dataset using supervised learning technique. In: 2020 Indo-Taiwan 2nd international conference on computing, analytics and networks (Indo-Taiwan ICAN). IEEE, pp 52–57

  127. Singh S, Singh J, Goyal S, Sehra SS, Ali F, Alkhafaji MA et al (2023) A novel framework to avoid traffic congestion and air pollution for sustainable development of smart cities. Sustain Energy Technol Assess 56:103125

    Google Scholar 

  128. Biljecki F, Chow YS, Lee K (2023) Quality of crowdsourced geospatial building information: a global assessment of OpenStreetMap attributes. Build Environ 237:110295

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jaiteg Singh or Manoj Kumar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, S., Singh, J., Goyal, S.B. et al. Analytical Review of Map Matching Algorithms: Analyzing the Performance and Efficiency Using Road Dataset of the Indian Subcontinent. Arch Computat Methods Eng 30, 4897–4916 (2023). https://doi.org/10.1007/s11831-023-09962-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-023-09962-5

Navigation