Volume 24 Issue 5
Oct.  2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2024  >  24(5): 301-332
LI Ying, FEI Yi-xuan, AN Yi-sheng, LIU Yang. Review on map matching technologies in intelligent transportation scenarios[J]. Journal of Traffic and Transportation Engineering, 2024, 24(5): 301-332. doi: 10.19818/j.cnki.1671-1637.2024.05.020
Citation: LI Ying, FEI Yi-xuan, AN Yi-sheng, LIU Yang. Review on map matching technologies in intelligent transportation scenarios[J]. Journal of Traffic and Transportation Engineering, 2024, 24(5): 301-332. doi: 10.19818/j.cnki.1671-1637.2024.05.020
shu

Review on map matching technologies in intelligent transportation scenarios

doi: 10.19818/j.cnki.1671-1637.2024.05.020
  • 1.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China

Funds:

National Key Research and Development Program of China 2021YFB1600100

Key Research and Development Program of Shaanxi Province 2024GX-YBXM-002

National Natural Science Foundation of China 52002031

National Natural Science Foundation of China 52220105001

National Natural Science Foundation of China 52221005

More Information
  • Author Bio:

    LI Ying(1986-), female, associate professor, PhD, yingli@chd.edu.cn

    Yang(1991-), male, assistant professor, PhD, thu_ets_lab@tsinghua.edu.cn

  • Received Date: 2024-06-19
    Available Online: 2024-12-20
  • Publish Date: 2024-10-25
    Abstract
  • To promote the development of map matching technology, an in-depth study of map-matching algorithms was conducted from the perspective of matching methods. The principles, characteristics, and application scenarios were classified and described. The existing map matching datasets were comprehensively introduced. The application scenarios of map matching in the field of intelligent transportation were summarized, and future research directions for map matching technology were proposed. Research results indicate that the accuracy and completeness of global position system (GPS) data can be affected by various factors, which results in sparse trajectory data. Sparse GPS trajectories can result in the inability to accurately reconstruct the actual driving paths of vehicles, increasing the uncertainty in map matching. The demand for lane-level matching has become increasingly urgent due to the development of intelligent transportation systems, the rise of autonomous driving technology, and the growing complexity of urban transportation networks. The future research directions of map matching technology primarily focus on two aspects. For map matching technology with sparse trajectories, attention needs to be paid to data interpolation techniques to improve trajectory continuity, multi-sensor data fusion technology should be employed to enhance the accuracy and reliability of positioning, and deep learning techniques should be applied to improve the intelligence level of matching algorithms. For lane-level map matching technology, the focus lies in integrating high-precision map data with real-time traffic information to provide more accurate information on road characteristics and traffic conditions, optimizing deep learning models to recognize complex traffic patterns and road characteristics, and developing algorithms that adapt to dynamic traffic environments to obtain algorithms with improved stability and adaptability. These research directions will help enhance the accuracy, reliability, and real-time performance of map matching technology and provide stronger support for intelligent transportation system and autonomous driving technology.

     

    • FullText(HTML)
  • loading
    • References(131)
  • [1]
    于娟, 杨琼, 鲁剑锋, 等. 高级地图匹配算法: 研究现状和趋势[J]. 电子学报, 2021, 49(9): 1818-1829.

    YU Juan, YANG Qiong, LU Jian-feng, et al. Advanced map matching algorithms: a survey and trends[J]. Acta Electonica Sinica, 2021, 49(9): 1818-1829. (in Chinese)
    [2]
    KUBICKA M, CELA A, MOUNIER H, et al. Comparative study and application-oriented classification of vehicular map-matching methods[J]. IEEE Intelligent Transportation Systems Magazine, 2018, 10(2): 150-166. doi: 10.1109/MITS.2018.2806630
    [3]
    孙健, 张颖, 张纯. 基于驾驶人路径选择偏好的OD行程时间预测方法[J]. 交通运输工程学报, 2016, 16(2): 143-149. doi: 10.19818/j.cnki.1671-1637.2016.02.017

    SUN Jian, ZHANG Ying, ZHANG Chun. Prediction method of OD travel time based on driver's route choice preference[J]. Journal of Traffic and Transportation Engineering, 2016, 16(2): 143-149. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2016.02.017
    [4]
    HUANG Zhen-feng, QIAO Shao-jie, HAN Nan, et al. Survey on vehicle map matching techniques[J]. CAAI Transactions on Intelligence Technology, 2021, 6(1): 55-71. doi: 10.1049/cit2.12030
    [5]
    NIKOLIC M, JOVIC J. Implementation of generic algorithm in map-matching model[J]. Expert Systems with Applications, 2017, 72: 283-292. doi: 10.1016/j.eswa.2016.10.061
    [6]
    廖律超, 蒋新华, 林铭榛, 等. 基于交通轨迹数据挖掘的道路限速信息识别方法[J]. 交通运输工程学报, 2015, 15(5): 118-126. doi: 10.19818/j.cnki.1671-1637.2015.05.015

    LIAO Lyu-chao, JIANG Xin-hua, LIN Ming-zhen, et al. Recognition method of road speed limit information based on data mining of traffic trajectory[J]. Journal of Traffic and Transportation Engineering, 2015, 15(5): 118-126. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2015.05.015
    [7]
    SIRI E, SIRI S, SACONE S. A topology-based bounded rationality day-to-day traffic assignment model[J]. Communications in Transportation Research, 2022, 2: 1-16.
    [8]
    ZHANG Wen-wei, ZHAO Hui, XU Min. Optimal operating strategy of short turning lines for the battery electric bus system[J]. Communications in Transportation Research, 2021, 1: 1-12.
    [9]
    张平, 陈一凡, 江书真, 等. 高速公路上自动超车过程的轨迹规划与跟踪控制[J]. 汽车安全与节能学报, 2022, 13(3): 463-472.

    ZHANG Ping, CHEN Yi-fan, JIANG Shu-zhen, et al. Trajectory planning and tracking control of automatic overtaking process on highway[J]. Journal of Automotive Safety and Energy, 2022, 13(3): 463-472. (in Chinese)
    [10]
    孙智威, 裴晓飞, 刘一平, 等. 无人驾驶清扫车的路径跟踪及远程控制[J]. 汽车安全与节能学报, 2022, 13(4): 729-737.

    SUN Zhi-wei, PEI Xiao-fei, LIU Yi-ping, et al. Path tracking and remote control of driverless sweeper[J]. Journal of Automotive Safety and Energy, 2022, 13(4): 729-737. (in Chinese)
    [11]
    LIU Zhi-yuan, LIU Yang, LYU Cheng, et al. Building personalized transportation model for online taxi-hailing demand prediction[J]. IEEE Transactions on Cybernetics, 2020, 51(9): 4602-4610.
    [12]
    CHAO Ping-fu, XU Ye-hong, HUA Wen, et al. A survey on map-matching algorithms[C]//BOROVICA-GAJIC R. Databases Theory and Applications: 31st Australasian Database Conference. Berlin: Springer, 2020: 121-133.
    [13]
    BERNSTEIN D, KORNHAUSER A. An introduction to map matching for personal navigation assistants[J]. New Jersey Institute of Technology, 1998, DOI: 10.1001/archopht.122.7.1082-b.
    [14]
    MAHPOUR A, FORSI H, VAFAEENEJAD A, et al. An improvement on the topological map matching algorithm at junctions: a heuristic approach[J]. International Journal of Transportation Engineering, 2022, 9(4): 749-761.
    [15]
    谷远利, 陆文琦, 邵壮壮. 基于多目标遗传算法的浮动车地图匹配方法[J]. 北京工业大学学报, 2019, 45(6): 585-592.

    GU Yuan-li, LU Wen-qi, SHAO Zhuang-zhuang. Multi-criteria genetic algorithm-based map-matching method for floating car data[J]. Journal of Beijing University of Technology, 2019, 45(6): 585-592. (in Chinese)
    [16]
    王跃钢, 文超斌, 左朝阳, 等. 自适应混沌蚁群径向分析算法求解重力辅助导航匹配问题[J]. 物理学报, 2014, 8(63): 454-459.

    WANG Yue-gang, WEN Chao-bin, ZUO Chao-yang, et al. Adaptive chaotic ant colony radial analysis algorithm for solving gravity-assist navigation matching problem[J]. Acta Physica Sinica, 2014, 8(63): 454-459. (in Chinese)
    [17]
    QUDDUS M A, NOLAND R B, OCHIENG W Y. A high accuracy fuzzy logic based map matching algorithm for road transport[J]. Journal of Intelligent Transportation Systems, 2006, 10(3): 103-115. doi: 10.1080/15472450600793560
    [18]
    谷正气, 胡林, 黄晶, 等. 基于改进D-S证据理论的车辆导航地图匹配[J]. 汽车工程, 2008, 30(2): 141-145.

    GU Zheng-qi, HU Lin, HUANG Jing, et al. Vehicle navigation map matching based on modified D-S evidence rule[J]. Automotive Engineering, 2008, 30(2): 141-145. (in Chinese)
    [19]
    QUDDUS M A, NOLAND R B, OCHIENG W Y. Validation of map matching algorithms using high precision positioning with GPS[J]. Journal of Navigation, 2005, 58(2): 257-271. doi: 10.1017/S0373463305003231
    [20]
    YU Biao, DONG Lin, XUE De-yi, et al. A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching for vehicle self-localization[J]. Journal of Intelligent Transportation Systems, 2018, 23(1): 84-98.
    [21]
    NEWSON P, KRUMM J. Hidden Markov map matching through noise and sparseness[C]//ACM. Proceedings of the 17th ACM Sigspatial International Conference on Advances in Geographic Information Systems. New York: ACM, 2009: 336-343.
    [22]
    HSUEH Y L, CHEN He-qian. Map matching for low-sampling- rate GPS trajectories by exploring real-time moving directions[J]. Information Sciences, 2018, 433: 55-69.
    [23]
    JIANG Lin-li, CHEN Chao-xiong, CHEN Chao. L2MM: learning to map matching with deep models for low-quality GPS trajectory data[J]. ACM Transactions on Knowledge Discovery from Data, 2023, 17(3): 1-25.
    [24]
    刘峰, 郭阳, 郑辛, 等. 基于道路几何特征的地图匹配方法研究[J]. 导航定位与授时, 2020, 7(1): 67-72.

    LIU Feng, GUO Yang, ZHENG Xin, et al. Study on map matching method based on geometric features of road[J]. Navigation Positioning and Timing, 2020, 7(1): 67-72. (in Chinese)
    [25]
    MICHAU G, NANTES A, BHASKAR A, et al. Bluetooth data in an urban context: retrieving vehicle trajectories[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(9): 2377-2386. doi: 10.1109/TITS.2016.2642304
    [26]
    WHITE C E, BERNSTEIN D, KORNHAUSER A L. Some map matching algorithms for personal navigation assistants[J]. Transportation Research Part C: Emerging Technologies, 2000, 8(1-6): 91-108. doi: 10.1016/S0968-090X(00)00026-7
    [27]
    汪小寒, 何增宇, 胡王悟, 等. 复杂路段的角度差和后续点地图匹配方法[J]. 计算机应用研究, 2022, 39(2): 379-384.

    WANG Xiao-han, HE Zeng-yu, HU Wang-wu, et al. Map matching method for complex road sections via difference and subsequent points[J]. Application Research of Computers, 2022, 39(2): 379-384. (in Chinese)
    [28]
    TAYLOR G, BLEWITT G, STEUP D, et al. Road reduction filtering for GPS-GIS navigation[J]. Transactions in GIS, 2001, 5(3): 193-207. doi: 10.1111/1467-9671.00077
    [29]
    CHEN D, DRIEMEL A, GUIBAS L J, et al. Approximate map matching with respect to the Fréchet distance[C]// WHITFORD A B. 2011 Proceedings of the Thirteenth Workshop on Algorithm Engineering and Experiments. Naples: Society for Industrial and Applied Mathematics, 2011: 75-83.
    [30]
    上官伟, 袁重阳, 蔡伯根, 等. 北斗二代在西部低密度铁路中的应用[J]. 交通运输工程学报, 2016, 16(5): 132-141. doi: 10.19818/j.cnki.1671-1637.2016.05.015

    SHANGGUAN Wei, YUAN Chong-yang, CAI Bai-gen, et al. Application of BDS in western low-density railway lines[J]. Journal of Traffic and Transportation Engineering, 2016, 16(5): 132-141. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2016.05.015
    [31]
    XU Qian, WANG Mei-ling, DU Zhi-fang, et al. A positioning algorithm of autonomous car based on map-matching and environmental perception[C]//IEEE. Proceedings of the 33rd Chinese Control Conference. New York: IEEE, 2014: 707-712.
    [32]
    HAN Yong-qian, ZHAO Dun-hui, ZHANG Xin-gang, et al. Map matching algorithm based on trajectory feature identification[C]//IEEE. 2021 40th Chinese Control Conference. New York: IEEE, 2021: 3657-3661.
    [33]
    BLAZQUEZ C A, VONDEROHE A P. Simple map-matching algorithm applied to intelligent winter maintenance vehicle data[J]. Transportation Research Record, 2005(1935): 68-76.
    [34]
    PEREIRA F C, COSTA H, PEREIRA N M. An off-line map-matching algorithm for incomplete mapdatabases[J]. European Transport Research Review, 2009, 1(3): 107-124. doi: 10.1007/s12544-009-0013-6
    [35]
    LIU You-wen. Forecast map matching model for vehicle- borne navigation based on roadway character-istic[C]//IEEE. 2010 International Conference on Optoelectronics and Image Processing. New York: IEEE, 2010: 569-571.
    [36]
    VELAGA N R, QUDDUS M A, BRISTOW A L. Improving the performance of a topological map-matching algorithm through error detection and correction[J]. Journal of Intelligent Transportation Systems, 2012, 16(3): 147-158. doi: 10.1080/15472450.2012.691852
    [37]
    李殿茜, 王翌, 刘垒, 等. 一种地图匹配算法的设计与实现[J]. 导航定位与授时, 2017, 4(2): 31-34.

    LI Dian-xi, WANG Yi, LIU Lei, et al. The design and implementation of a map matching algorithm[J]. Navigation Positioning and Timing, 2017, 4(2): 31-34. (in Chinese)
    [38]
    QUDDUS M A, OCHIENG W Y, ZHAO Lin, et al. A general map matching algorithm for transport telematics applications[J]. GPS Solutions, 2003, 7(3): 157-167. doi: 10.1007/s10291-003-0069-z
    [39]
    VELAGA N R, QUDDUS M A, BRISTOW A L. Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems[J]. Transportation Research Part C: Emerging Technologies, 2009, 17(6): 672-683. doi: 10.1016/j.trc.2009.05.008
    [40]
    YUAN Jing, ZHENG Yu, ZHANG Cheng-yang, et al. An interactive-voting based map matching algorithm[C]//IEEE. 2010 Eleventh International Conference on Mobile Data Management. New York: IEEE, 2010: 43-52.
    [41]
    QUDDUS M, WASHINGTON S. Shortest path and vehicle trajectory aided map-matching for low frequency GPS data[J]. Transportation Research Part C: Emerging Technologies, 2015, 55: 328-339. doi: 10.1016/j.trc.2015.02.017
    [42]
    DU Zi-xue, LIU Bin-yan, XIA Qin. Map matching algorithm based on V2I technology[C]//IEEE. 2018 International Conference on Robots and Intelligent System. New York: IEEE, 2018: 137-140.
    [43]
    张贺, 孙婉莹, 宋国平. 基于特征权重的地图匹配算法研究[J]. 中国新技术新产品, 2022, DOI: 10.13612/j.cnkicntp.2022.23.027.

    ZHANG He, SUN Wan-ying, SONG Guo-ping. Research on feature weight-based map matching algorithm[J]. China New Technology and New Products, 2022, DOI: 10.13612/j.cnkicntp.2022.23.027.(inChinese)
    [44]
    ZHANG Lun, ZHANG Meng, YANG Wen-chen, et al. Golden ratio genetic algorithm based approach for modelling and analysis of the capacity expansion of urban road traffic network[J]. Computational Intelligence and Neuroscience, 2015, 2015(1): 1-9.
    [45]
    CHEN Peng, TONG Rui, LU Guang-quan, et al. The α-reliable path problem in stochastic road networks with link correlations: a moment-matching-based path finding algorithm[J]. Expert Systems with Applications, 2018, 110: 20-32. doi: 10.1016/j.eswa.2018.05.022
    [46]
    吴刚, 邱煜晶, 王国仁. 基于隐马尔可夫模型和遗传算法的地图匹配算法[J]. 东北大学学报(自然科学版), 2017, 38(4): 472-475.

    WU Gang, QIU Yu-jing, WANG Guo-ren. Map matching algorithm based on hidden markov model and genetic algorithm[J]. Journal of Northeastern University (Natural Science), 2017, 38(4): 472-475. (in Chinese)
    [47]
    CHEHREGHAN A, ALI ABBASPOUR R. A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm[J]. Cartography and Geographic Information Science, 2018, 45(3): 255-269. doi: 10.1080/15230406.2017.1324823
    [48]
    SINGH S, SINGH J, SEHRA S S. Genetic-inspired map matching algorithm for real-time GPS trajectories[J]. Arabian Journal for Science and Engineering, 2020, 45(4): 2587-2603. doi: 10.1007/s13369-019-04247-1
    [49]
    YIN Dan-dong, DU Shi-hong, WANG Shao-wen, et al. A direction-guided ant colony optimization method for extraction of urban road information from very-high-resolution images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(10): 4785-4794. doi: 10.1109/JSTARS.2015.2477097
    [50]
    巩现勇, 武芳, 姬存伟, 等. 道路网匹配的蚁群算法求解模型[J]. 武汉大学学报(信息科学版), 2014, 39(2): 191-195.

    GONG Xian-yong, WU Fang, JI Cun-wei, et al. Ant colony algorithm solution model for road network matching[J]. Geomatics and Information Science of Wuhan University, 2014, 39(2): 191-195. (in Chinese)
    [51]
    GONG Yue-jiao, CHEN En, ZHANG Xing-lin, et al. AntMapper: an ant colony-based map matching approach for trajectory-based applications[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 19(2): 390-401.
    [52]
    LIU Ji-ping, XU Sheng-hua, ZHANG Fu-hao, et al. A hybrid genetic-ant colony optimization algorithm for the optimal path selection[J]. Intelligent Automation and Soft Computing, 2017, 23(2): 235-242. doi: 10.1080/10798587.2016.1196926
    [53]
    FU Meng-yin, LI Jie, WANG Mei-ling. A hybrid map matching algorithm based on fuzzy comprehensive judgment[C]// IEEE. The 7th International IEEE Conference on Intelligent Transportation Systems. New York: IEEE, 2004: 613-617.
    [54]
    SYED S, CANNON M E. Fuzzy logic based-map matching algorithm for vehicle navigation system in urban canyons[C]// WELLS J K. Proceedings of the 2004 National Technical Meeting of the Institute of Navigation. Washington DC: Institute of Navigation, 2004: 982-993.
    [55]
    ZHANG Yong-qiang, GAO Yan-yan. A fuzzy logic map matching algorithm[C]//IEEE. 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery. New York: IEEE, 2008: 132-136.
    [56]
    张涛, 杨殿阁, 李克强, 等. 车辆导航中带匹配度反馈的模糊地图匹配算法[J]. 清华大学学报: 自然科学版, 2009, 49(2): 277-280.

    ZHANG Tao, YANG Dian-ge, LI Ke-qiang, et al. Fuzzy map-matching algorithm with confidence feedback for vehicle navigation[J]. Journal of Tsinghua University: Science and Technology, 2009, 49(2): 277-280. (in Chinese)
    [57]
    YANG Yan-lan, YE Hua, FEI Shu-min. Integrated map-matching algorithm based on fuzzy logic and dead reckoning[C]//IEEE. 2010 International Conference on Control, Automation and Systems. New York: IEEE, 2010: 1139-1142.
    [58]
    NASSREDDINE G, ABDALLAH F, DENലUX T. Map matching algorithm using belief function theory[C]//IEEE. 2008 11th International Conference on Information Fusion. New York: IEEE, 2008: 1-8.
    [59]
    胡林, 谷正气, 杨易, 等. 基于权值D-S证据理论的车辆导航地图匹配[J]. 中国公路学报, 2008, 21(2): 116-120.

    HU Lin, GU Zheng-qi, YANG Yi, et al. Map matching in vehicle navigation based on weighted DS evidence theory[J]. China Journal of Highway and Transport, 2008, 21(2): 116-120. (in Chinese)
    [60]
    曹闻, 朱述龙, 彭煊, 等. 基于短时预测的地图匹配算法[J]. 计算机应用, 2010, 30(11): 2910-2913.

    CAO Wen, ZHU Shu-long, PENG Xuan, et al. Map matching algorithm based on short-term prediction[J]. Computer Applications, 2010, 30(11): 2910-2913. (in Chinese)
    [61]
    ZHAO Xiang-mo, CHENG Xin, ZHOU Jing-mei, et al. Advanced topological map matching algorithm based on D-S theory[J]. Arabian Journal for Science and Engineering, 2018, 43: 3863-3874. doi: 10.1007/s13369-017-2569-0
    [62]
    YANG Feng-bao, WEI Hong, FENG Pei-pei. A hierarchical dempster-shafer evidence combination framework for urban area land cover classification[J]. Measurement, 2020, 151: 105916. doi: 10.1016/j.measurement.2018.09.058
    [63]
    NIE Qing-hui, XIA Jing-xin, QIAN Zhen-dong, et al. Use of multisensor data in reliable short-term travel time forecasting for urban roads: Dempster-Shafer approach[J]. Transportation Research Record, 2015(2526): 61-69.
    [64]
    CAI Jian-nan, JEON J H, CAI Hu-bo, et al. Fusing heterogeneous information for underground utility map generation based on Dempster-Shafer theory[J]. Journal of Computing in Civil Engineering, 2020, 34(3): 1-14.
    [65]
    杨易, 谷正气, 胡林, 等. 基于概率决策的车辆导航系统地图匹配算法[J]. 汽车工程, 2006, 28(10): 897-901.

    YANG Yi, GU Zheng-qi, HU Lin, et al. Map matching algorithm for vehicle navigation system based on probability decision rule[J]. Automotive Engineering, 2006, 28(10): 897-901. (in Chinese)
    [66]
    ZHAO Shuai-dong, ZHANG Kui-lin. A distributionally robust optimization approach to reconstructing missing locations and paths using high-frequency trajectory data[J]. Transportation Research Part C: Emerging Technologies, 2019, 102: 316-335. doi: 10.1016/j.trc.2019.03.012
    [67]
    JABBOUR M, BONNIFAIT P, CHERFAOUI V. Map-matching integrity using multihypothesis road-tracking[J]. Journal of Intelligent Transportation Systems, 2008, 12(4): 189-201. doi: 10.1080/15472450802448179
    [68]
    CHEN Bi-yu, YUAN Hui, LI Qing-quan, et al. Map-matching algorithm for large-scale low-frequency floating car data[J]. International Journal of Geographical Information Science, 2014, 28(1): 22-38. doi: 10.1080/13658816.2013.816427
    [69]
    ZHANG Qing-xiang, WANG Mei-ling, YUE Yu-feng. Robust semantic map matching algorithm based on probabilistic registration model[C]//IEEE. 2021 IEEE International Conference on Robotics and Automation. New York: IEEE, 2021: 5289-5295.
    [70]
    OBRADOVIC D, LENZ H, SCHUPFNER M. Fusion of map and sensor data in a modern car navigation system[J]. The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 2006, 45(1/2): 111-122.
    [71]
    CHU H J, TSAI G J, CHIANG K W, et al. GPS/MEMS INS data fusion and map matching in urban areas[J]. Sensors, 2013, 13(9): 11280-11288. doi: 10.3390/s130911280
    [72]
    WANG Hong-yu, LI Jin, HOU Zhen-shan, et al. Research on parallelized real-time map matching algorithm for massive GPS data[J]. Cluster Computing, 2017, 20: 1123-1134. doi: 10.1007/s10586-017-0869-5
    [73]
    ASGHAR R, GARZÓN M, LUSSEREAU J, et al. Vehicle localization based on visual lane marking and topological map matching[C]//IEEE. 2020 IEEE International Conference on Robotics and Automation. New York: IEEE, 2020: 258-264.
    [74]
    王东京, 刘继涛, 俞东进. 电动自行车轨迹简化与自适应地图匹配算法[J]. 软件学报, 2023, 34(8): 3793-3820.

    WANG Dong-jing, LIU Ji-tao, YU Dong-jin. Trajectory simplification and adaptive map matching algorithm for electric bicycle[J]. Journal of Software, 2023, 34(8): 3793-3820. (in Chinese)
    [75]
    XU Hao, LIU Hong-chao, TAN C W, et al. Development and application of an enhanced Kalman filter and global positioning system error-correction approach for improved map-matching[J]. Journal of Intelligent Transportation Systems, 2010, 14(1): 27-36. doi: 10.1080/15472450903386013
    [76]
    YUAN Yu-shan, LYU Jin-yang. Research on train positioning system based on map matching and multi-information fusion[C]// IEEE. 2020 IEEE 6th International Conference on Computer and Communications. New York: IEEE, 2020: 2271-2275.
    [77]
    YU Bao-guo, JIA Hao-nan, WANG Xin-jian, et al. An indoor map matching algorithm based on improved particle filter[C]//IEEE. 2022 IEEE 10th International Conference on Information, Communication and Networks. New York: IEEE, 2022: 158-163.
    [78]
    赖国良, 胡钊政, 周哲, 等. 基于语义似然与高精度地图匹配的智能车辆同时定位与检测[J]. 上海交通大学学报, 2023. DOI: 10.16183/j.cnki.jsjtu.2023.086.

    LAI Guo-liang, HU Zhao-zheng, ZHOU Zhe, et al. Simultaneous detection and localization for intelligent vehicles from HD map matchingwith semantic likelihood model[J]. Journal of Shanghai Jiao Tong University, 2023, DOI: 10.16183/j.cnki.jsjtu.2023.086.(inChinese)
    [79]
    RAO Wen-ming, WU Yao-jan, XIA Jing-xin, et al. Origin-destination pattern estimation based on trajectory reconstruction using automatic license plate recognition data[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 29-46. doi: 10.1016/j.trc.2018.07.002
    [80]
    YANG Jian-hao, SUN Jian. Vehicle path reconstruction using automatic vehicle identification data: an integrated particle filter and path flow estimator[J]. Transportation Research Part C: Emerging Technologies, 2015, 58: 107-126. doi: 10.1016/j.trc.2015.07.003
    [81]
    FENG Yu, SUN Jian, CHEN Peng. Vehicle trajectory reconstruction using automatic vehicle identification and traffic count data[J]. Journal of Advanced Transportation, 2015, 49(2): 174-194. doi: 10.1002/atr.1260
    [82]
    RAYMOND R, MORIMURA T, OSOGAMI T, et al. Map matching with hidden Markov model on sampled road network[C]//IEEE. Proceedings of the 21st International Conference on Pattern Recognition. New York: IEEE, 2012: 2242-2245.
    [83]
    GOH C Y, DAUWELS J, MITROVIC N, et al. Online map-matching based on hidden markov model for real-time traffic sensing applications[C]//IEEE. 2012 15th International IEEE Conference on Intelligent Transportation Systems. New York: IEEE, 2012: 776-781.
    [84]
    OSOGAMI T, RAYMOND R. Map matching with inverse reinforcement learning[C]//ROSSI F. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Beijing: International Joint Conferences on Artificial Intelligence, 2013: 2547-2553.
    [85]
    SMAILI C, NAJJAR M E B E, CHARPILLET F. A hybrid Bayesian framework for map matching: formulation using switching kalman filter[J]. Journal of Intelligent and Robotic Systems, 2014, 74(3/4): 725-743.
    [86]
    KOLLER H, WIDHALM P, DRAGASCHNIG M, et al. Fast hidden Markov model map-matching for sparse and noisy trajectories[C]//IEEE. 2015 IEEE 18th International Conference on Intelligent Transportation Systems. New York: IEEE, 2015: 2557-2561.
    [87]
    MOHAMED R, ALY H, YOUSSEF M. Accurate real-time map matching for challenging environments[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 18(4): 847-857.
    [88]
    LUO An, CHEN Sheng-hua, XU Bin. Enhanced map-matching algorithm with a hidden Markov model for mobile phone positioning[J]. ISPRS International Journal of Geo-Information, 2017, 6(11): 327. doi: 10.3390/ijgi6110327
    [89]
    YANG Can, GIDOFALVI G. Fast map matching, an algorithm integrating hidden Markov model with precomputation[J]. International Journal of Geographical Information Science, 2018, 32(3): 547-570. doi: 10.1080/13658816.2017.1400548
    [90]
    QI Hui, DI Xiao-qiang, LI Jin-qing. Map-matching algorithm based on the junction decision domain and the hidden Markov model[J]. PLoS ONE, 2019, 14(5): 1-20.
    [91]
    LI Hai-jun, CHEN Zhan-fang, CUI Guang-cai, et al. A fast map matching algorithm based on topological relation of road network[C]//IEEE. 2019 International Conference on Machine Learning, Big Data and Business Intelligence. New York: IEEE, 2019: 14-18.
    [92]
    DOGRAMADZI M, KHAN A. Accelerated map matching for GPS trajectories[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(5): 4593-4602.
    [93]
    袁祎, 陈光武. 基于改进HMM的车辆轨迹匹配方法研究(英文)[J]. 计量科学与仪器, 2024, 15(2): 235-243.

    YUAN Yi, CHEN Guang-wu. Research on vehicle trajectory matching method based on improved HMM[J]. Journal of Measurement Science and Instrumentation, 2024, 15(2): 235-243. (in Chinese)
    [94]
    LOU Yin, ZHANG Cheng-yang, ZHENG Yu, et al. Map-matching for low-sampling-rate GPS trajectories[C]//ACM. Proceedings of the 17th ACM Sigspatial International Conference on Advances in Geographic Information Systems. New York: ACM, 2009: 352-361.
    [95]
    CAO Qi, DENG Yue, REN Gang, et al. Jointly estimating the most likely driving paths and destination locations with incomplete vehicular trajectory data[J]. Transportation Research Part C: Emerging Technologies, 2023, 155: 1-27.
    [96]
    CAO Qi, REN Gang, LI Da-wei, et al. Map matching for sparse automatic vehicle identification data[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(7): 6495-6508.
    [97]
    JIANG Lin-li, CHEN Chao-xiong, CHEN Chao, et al. From driving trajectories to driving paths: a survey on map-matching algorithms[J]. CCF Transactions on Pervasive Computing and Interaction, 2022, 4(3): 252-267. doi: 10.1007/s42486-022-00101-w
    [98]
    苏海滨, 王光政, 王继东. 基于模糊神经网络的地图匹配算法[J]. 北京科技大学学报, 2012, 34(1): 43-47.

    SU Hai-bin, WANG Guang-zheng, WANG Ji-dong. Map matching algorithm based on fuzzy neural networks[J]. Journal of University of Science and Technology Beijing, 2012, 34(1): 43-47. (in Chinese)
    [99]
    HASHEMI M, KARIMI H A. A machine learning approach to improve the accuracy of GPS-based map-matching algorithms[C]//IEEE. 2016 IEEE 17th International Conference on Information Reuse and Integration. New York: IEEE, 2016: 77-86.
    [100]
    ZHAO Kai, FENG Jie, XU Zhao, et al. Deep MM: deep learning based map matching with data augmentation[C]//ACM. Proceedings of the 27th ACM Sigspatial International Conference on Advances in Geographic Information Systems. New York: ACM, 2019: 452-455.
    [101]
    SHEN Zhi-hao, DU Wan, ZHAO Xi, et al. DMM: fast map matching for cellular data[C]//ACM. Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. New York: ACM, 2020: 1-14.
    [102]
    JIN Zhi-xiong, KIM J, YEO H, et al. Transformer-based map-matching model with limited labeled data using transfer-learning approach[J]. Transportation Research Part C: Emerging Technologies, 2022, 140: 1-25.
    [103]
    QIU Shu-han, QIN Guo-yang, WONG M, et al. Routes former: a sequence-based route choice transformer for efficient path inference from sparse trajectories[J]. Transportation Research Part C: Emerging Technologies, 2024, 162: 1-38.
    [104]
    WEI Hua, CHEN Cha-cha, LIU Chang, et al. Learning to simulate on sparse trajectory data[C]//DONG Yu-xiao. 2021 European Conference on Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track. Berlin: Springer, 2021: 530-545.
    [105]
    ZHENG Yu, XIE Xing, MA Wei-ying. GeoLife: a collaborative social networking service among user, location and trajectory[J]. IEEE Data Engineering Bulletin, 2010, 33(2): 32-39.
    [106]
    YUAN Jing, ZHENG Yu, ZHANG Cheng-yang, et al. T-drive: driving directions based on taxi trajectories[C]//ACM. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM, 2010: 99-108.
    [107]
    ETEMAD M, SOARES JÚNIOR A, MATWIN S. Predicting transportation modes of gps trajectories using feature engineering and noise removal[C]//Canadian Society for Intelligent Research. 2018 Canadian Conference on Artificial Intelligence. Berlin: Springer, 2018: 259-264.
    [108]
    ABDELAZIZ N, EL-RABBANY A. Deep learning-aided inertial/visual/lidar integration for GNSS-challenging environments[J]. Sensors, 2023, 23(13): 1-20. doi: 10.1109/JSEN.2023.3287172
    [109]
    ASRAF O, SHAMA F, KLEIN I. PDRNet: a deep-learning pedestrian dead reckoning framework[J]. IEEE Sensors Journal, 2021, 22(6): 4932-4939.
    [110]
    DELMERICO J, CIESLEWSKI T, REBECQ H, et al. Are we ready for autonomous drone racing? The UZH-FPV drone racing dataset[C]//IEEE. 2019 IEEE International Conference on Robotics and Automation. New York: IEEE, 2019: 6713-6719.
    [111]
    SHI Xue-song, LI Dong-jiang, ZHAO Peng-peng, et al. Are we ready for service robots? The openloris-scene datasets for lifelong slam[C]//IEEE. 2020 IEEE International Conference on Robotics and Automation. New York: IEEE, 2020: 3139-3145.
    [112]
    MADDERN W, PASCOE G, LINEGAR C, et al. 1 year, 1000 km: the Oxford robotcar dataset[J]. The International Journal of Robotics Research, 2017, 36(1): 3-15. doi: 10.1177/0278364916679498
    [113]
    RAMEZANI M, WANG Y, CAMURRI M, et al. The newer college dataset: handheld lidar, inertial and vision with ground truth[C]//IEEE. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. New York: IEEE, 2020: 4353-4360.
    [114]
    BI Jing-xue, WANG Yun-jia, YU Bao-guo, et al. Supplementary open dataset for WiFi indoor localization based on received signal strength[J]. Satellite Navigation, 2022, 3(1): 1-15.
    [115]
    吴欢欢, 周建平, 许燕, 等. RFID发展及其应用综述[J]. 计算机应用与软件, 2013, 30(12): 203-206.

    WU Huan-huan, ZHOU Jian-ping, XU Yan, et al. A comprehensive review on RFID development and ITS application[J]. Computer Applications and Software, 2013, 30(12): 203-206. (in Chinese)
    [116]
    NGUYEN T M, YUAN Sheng-hai, CAO Mu-qing, et al. NTU VIRAL: a visual-inertial-ranging-lidar dataset, from an aerial vehicle viewpoint[J]. The International Journal of Robotics Research, 2022, 41(3): 270-280.
    [117]
    MOONEY P, MINGHINI M. A review of OpenStreetMap data[J]. Mapping and the Citizen Sensor, 2017, DOI: 10.5334/bbf.c.
    [118]
    ZAMIR A R, SHAH M. Accurate image localization based on google maps street view[C]//BARROW H G. 11th European Conference on Computer Vision. Berlin: Springer, 2010: 255-268.
    [119]
    王昭雨, 庄惟敏. 基于图像深度学习的街区更新后评估方法研究——以北京什刹海街区为例[J]. 新建筑, 2022(3): 5-8.

    WANG Zhao-yu, ZHUANG Wei-min. Research on block renewal post occupancy evaluation methods based on image deep learning: a case study of Shichahai in Beijing[J]. New Architecture, 2022(3): 5-8. (in Chinese)
    [120]
    李力, 张婧, 瓦希德·穆萨维, 等. 大数据驱动的城市更新设计方法初探[J]. 新建筑, 2021(2): 37-41.

    LI Li, ZHANG Jing, MOOSAVI V, et al. A preliminary study of big data driven urban renewal design[J]. New Architecture, 2021(2): 37-41. (in Chinese)
    [121]
    陈滨, 王平, 施文灶, 等. GPS轨迹数据的综合地图匹配算法研究[J]. 电子科技, 2014, 27(12): 20-23.

    CHEN Bin, WANG Ping, SHI Wen-zao, et al. An integrated map-matching algorithm based on GPS[J]. Electronic Science and Technology, 2014, 27(12): 20-23. (in Chinese)
    [122]
    HASHEMI M, KARIMI H A. A critical review of real-time map-matching algorithms: current issues and future directions[J]. Computers, Environment and Urban Systems, 2014, 48: 153-165.
    [123]
    GAN Nian-fei, ZHANG Miao-miao, ZHOU Bing, et al. Spatio-temporal heuristic method: a trajectory planning for automatic parking considering obstacle behavior[J]. Journal of Intelligent and Connected Vehicles, 2022, 5(3): 177-187.
    [124]
    ZHAO Jian-sen, MA Xin, YANG Bing, et al. Global path planning of unmanned vehicle based on fusion of A* algorithm and Voronoi field[J]. Journal of Intelligent and Connected Vehicles, 2022, 5(3): 250-259.
    [125]
    彭涛, 许庆, 陈强, 等. 异构载货车辆队列高速换道分布式反馈线性化控制[J]. 汽车安全与节能学报, 2022, 13(3): 473-481.

    PENG Tao, XU Qing, CHEN Qiang, et al. Distributed feedback linear control for heterogeneous freight vehicle platoon on highway[J]. Journal of Automotive Safety and Energy, 2022, 13(3): 473-481. (in Chinese)
    [126]
    DABIRI A, KULCSÁR B. Incident indicators for freeway traffic flow models[J]. Communications in Transportation Research, 2022, 2: 1-9.
    [127]
    陈华伟, 邵毅明, 敖谷昌, 等. 面向在线地图的GCN-LSTM神经网络速度预测[J]. 交通运输工程学报, 2021, 21(4): 183-196. doi: 10.19818/j.cnki.1671-1637.2021.04.014

    CHEN Hua-wei, SHAO Yi-ming, AO Gu-chang, et al. Speed prediction by online map-based GCN-LSTM neural network[J]. Journal of Traffic and Transportation Engineering, 2021, 21(4): 183-196. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2021.04.014
    [128]
    TSCHARAKTSCHIEW S, REIMANN F. Less workplace parking with fully autonomous vehicles?[J]. Journal of Intelligent and Connected Vehicles, 2022, 5(3): 283-301.
    [129]
    ZHANG Dong-qing, DONG Yu-cheng, GUO Zhao-xia. A turning point-based offline map matching algorithm for urban road networks[J]. Information Sciences, 2021, 565: 32-45.
    [130]
    LYU Cheng, WU Xin-hua, LIU Yang, et al. A partial-Fréchet- distance-based framework for bus route identification[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(7): 9275-9280.
    [131]
    李军, 唐爽, 黄志祥, 等. 融合稳定性的高速无人驾驶车辆纵横向协调控制方法[J]. 交通运输工程学报, 2020, 20(2): 205-218. doi: 10.19818/j.cnki.1671-1637.2020.02.017

    LI Jun, TANG Shuang, HUANG Zhi-xiang, et al. Longitudinal and lateral coordination control method of high- speed unmanned vehicles with integrated stability[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 205-218. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.02.017
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (11) PDF downloads(2) Cited by()
    Proportional views
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return