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摘要: 为推动地图匹配技术的发展，从匹配方法角度深入研究地图匹配算法并分类阐述原理、特点及应用场景，全面介绍了现有地图匹配数据集，总结了地图匹配在智能交通领域的应用场景，提出了地图匹配技术未来的研究方向。研究结果表明：GPS数据的准确性和完整性可能会因多种因素而受到影响，导致轨迹数据变得稀疏，稀疏的GPS轨迹会导致车辆的实际行驶路径无法被准确还原，增加地图匹配的不确定性；车道级匹配需求因智能交通系统的发展、自动驾驶技术的兴起和城市交通网络的日益复杂等因素而日益迫切；未来地图匹配技术的研究方向主要集中在2个方面；对于稀疏轨迹的地图匹配技术，需聚焦数据插值技术，提高轨迹的连续性，运用多传感器数据融合技术，增强定位的准确性和可靠性，应用深度学习技术，提高匹配算法的智能水平；对于车道级的地图匹配技术，重点在于整合高精度地图数据和实时交通信息，提供更准确的道路特征和交通状况信息，优化深度学习模型，识别复杂的交通模式和道路特征，开发适应动态交通环境的算法，提高算法的稳定性和适应性；这些研究方向将有助于提高地图匹配技术的准确性、可靠性和实时性，为智能交通系统和自动驾驶技术提供更有力的支持。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.
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Key words:
- intelligent transportation system /
- map matching /
- hidden Markov model /
- deep learning /
- GPS trajectory
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图 5 基于几何和基于简单拓扑关系的匹配算法对比
Figure 5. Comparison between geometry-based and simple topology relation-based matching algorithms
图 7 基于遗传算法的地图匹配算法流程
Figure 7. Process of map matching algorithm based on genetic algorithm
图 8 基于蚁群算法的地图匹配算法流程
Figure 8. Process of map matching algorithm based on ant colony algorithm
图 10 基于D-S证据理论的地图匹配算法流程
Figure 10. Process of map matching algorithm based on D-S evidence theory
图 11 传统基于概率决策规则的地图匹配算法
Figure 11. Traditional map matching algorithm based on probability decision rule
图 12 基于卡尔曼滤波器的地图匹配算法流程
Figure 12. Process of map matching algorithm based on Kalman filter
图 13 基于粒子滤波器的地图匹配算法流程
Figure 13. Process of map matching algorithm based on particle filter
图 15 基于深度学习的地图匹配算法流程
Figure 15. Process of map matching algorithm based on deep learning
表 1 各类地图匹配算法简介
Table 1. Introduction of various map matching algorithms
算法 匹配方法 文献 特点 适用场景 传统地图匹配算法 几何 [13] 简单高效，易于实现，对噪声敏感 适用于道路网络较为简单或者对实时性要求较高的情况 拓扑 [14] 快速确定候选路段，实时性好，受初始匹配结果影响较大，容错性差 适用于道路网络结构复杂，例如在城市环境中，平行道路和频繁的路段变更 基于优化算法的地图匹配算法 遗传算法 [15] 全局最优搜索能力，处理复杂问题，容错性，适应性强 适合于需要在多个匹配结果中寻找最优解的复杂道路网络，以及需要快速响应的实时地图匹配应用 蚁群算法 [16] 全局最优解，自适应和局部搜索策略 适用于解决空间数据集中和融合中的关键技术问题，如同名实体匹配，复杂的道路网匹配问题，实时匹配 基于逻辑和推理的地图匹配算法 模糊逻辑 [17] 处理不确定性和模糊性，基于规则的推理，集成多种数据源 适用于复杂的交通网络环境，尤其是在城市密集道路网络中，以及存在不确定性的匹配问题 D-S证据理论 [18] 处理不确定性和证据冲突，鲁棒性 适用于城市复杂路网，车辆监控和管理系统 基于概率统计的地图匹配算法 概率决策 [19] 考虑车辆定位的不确定性，有效减少待匹配的路段数量，对置信区域设置敏感 适用于简单路网环境，定位误差较小的情况，通常需要与其他地图匹配技术结合 滤波器 [20] 考虑传感器误差和地图误差，较高的可信度，效率较低 适用于对定位精度要求较高的场景，如城市复杂道路环境中的车辆导航系统、自动驾驶汽车、无人机导航等 HMM [21] 考虑测量噪声和道路布局，处理时间序列数据，动态规划优化 适合于处理不准确和稀疏的GPS数据 扩展HMM [22] 考虑实时移动方向，综合空间、时间和方向特征，减少计算成本 适用于道路网络复杂，低采样率GPS轨迹，需要更精细匹配结果的场景 基于深度学习的地图匹配算法 [23] 能够处理低质量(噪声、低频率、非均匀采样)的GPS轨迹数据，利用深度学习模型提高匹配质量 适合于需要处理大量复杂轨迹数据的情况，尤其是在数据质量较差时 
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表 2 基于HMM的地图匹配算法比较
Table 2. Comparison of map matching algorithms based on HMM
算法 具体方法 输入信息 改进 HMM[21] HMM+Viterbi 时间戳的纬度、经度对，道路网络图 通过手动匹配或已知的行驶路线来验证和校正算法 理想HMM[82] 简化的HMM框架+Viterbi 时间戳的纬度、经度对，道路网络图 简化了状态转移概率，使其适应理想HMM框架，以避免在寻找最可能路径时的缺陷 OHMM[83] HMM+VSW+SVW+概率评分机制+Viterbi 车辆的经度、纬度、速度和时间戳，道路网络；其他传感器数据和拓扑信息 利用支持向量机(Support Vector Machine, SVM)来学习转移概率函数，而不是事先选择模型并估计其参数 基于IRL的HMM[84] HMM+IRL+Viterbi+Dijkstra+最大熵IRL GPS数据点，真实路线，道路网络 考虑了转弯次数作为评估路线可能性的指标 混合贝叶斯框架匹配算法[85] 卡尔曼滤波器+HMM+链式状态空间表示 传感器数据，差分全球定位系统(DifferentialGlobal Position System, DGPS)接收器提供的GPS位置数据，道路网络 引入了链式状态空间表示，允许使用精确的线性方程进行多传感器数据融合，提出了一个段选择策略，用于假设跟踪 快速HMM[86] HMM+Dijkstra+懒惰评估 GPS数据点，道路网络，搜索半径 经过优化以减少计算复杂性，特别是在处理稀疏和嘈杂的GPS轨迹数据时 SnapNet[87] 一系列过滤器+插值+增量式HMM+Viterbi 由纬度、经度和误差估计值组成的位置信息，数字地图 运用道路转换启发式规则，处理虚假转换，减少不必要的道路切换 基于HMM的增强模型[88] HMM+Viterbi GPS数据和手机定位数据，道路网络，几何数据，精炼的四叉树数据 对于状态空间的定义，使用四叉树网格结构化道路网络，以减少计算复杂性 FMM[89] 预计算+HMM+Viterbi+空间索引技术+线性引用算法 位置、时间戳、速度、方向和加速度的GPS观测数据，道路网络 通过预计算和使用哈希表搜索来替换重复的路由查询 基于路口决策域和HMM[90] HMM+路口决策域模型+Viterbi 车辆的实时位置信息，道路网络 定义路口决策域，综合道路宽、路口角、GPS及路网精度，划定区域，指导车辆GPS位置匹配，减少误差 基于拓扑关系的快速匹配算法[91] HMM+A*算法+Viterbi 车辆的纬度、经度、时间、速度、方向角度，道路网络数据 预计算每个区域包含的道路段，提高了查找候选道路段的效率，改进了A*算法的启发式搜索效率，减少了计算量 加速HMM[92] HMM+道路网络分割+空间感知启发式方法+Viterbi 原始GPS点，道路网络 通过道路网络分割和空间感知启发式方法显著加速转移概率计算 IHMM[93] 误差置信椭圆+IHMM+Viterbi 原始GPS点，道路网络 基于方向和距离权重的发射概率计算，以及考虑道路段曲率的状态转移概率计算
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表 3 基于深度学习的地图匹配算法比较
Table 3. Comparison of map matching algorithms based on deep learning
模型 数据集信息 采样间隔/s 神经网络的输入 神经网络的输出 FNN[98] 郑州城市区域测试车辆GPS轨迹 4 投影距离以及方位角之间的差异 地图匹配度 ANN[99] 芝加哥5条道路区域内的GPS轨迹，包含13 204个GPS点 速度、水平精度因子和常数 GPS点的水平偏移量，包括长度和方向 基于Seq2Seq学习框架，结合注意力机制[100] 北京地区车辆GPS轨迹包含12 436名用户的1 336 567条 11 原始轨迹数据和道路网络信息 匹配后的轨迹和校准后的轨迹 DMM框架[101] 伦敦198条车辆GPS轨迹，总长度为1 701 km，涉及2 848个不同的蜂窝基站 5个等级 蜂窝基站序列，包含了基站的标识符(ID)和时间戳以及道路网络地图 道路轨迹和匹配精度 Transformer[102] 韩国首尔江南区运营的出租车上安装的DTG GPS轨迹 点级或线级路线 RoutesFormer[103] 上海8 407辆出租车的GPS轨迹 60、180、300、420 不连续的路径(道路链接)、已行驶的路径 最有可能的下一个链接、完整推断路径 ImIn-GAIL框架[104] 杭州和洛杉矶地区GPS车辆轨迹 0.1 稀疏驾驶轨迹、驾驶状态、驾驶行为 密集驾驶轨迹、模仿性能、插值性能 L2MM[23] 波尔图市200 000条GPS轨迹、重庆市100 000条GPS轨迹 15 低质量GPS轨迹、高质量GPS轨迹、道路网络数据 映射后的轨迹、轨迹表示、识别的模式
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表 4 地图匹配数据集
Table 4. Map matching datasets
数据集类型 数据集名称 是否公开 公开数据集 优点 缺点 GPS数据集 GPS[105-107] 部分公开 GeoLife GPS Trajectories、T-Drive Taxi Trajectories、SynMob等 全球范围覆盖，精度较高 室内效果差，信号易受遮挡，能耗高 传感器数据集 AVI[77-78, 95] 私有 精度高 数据稀疏 INS[108] 部分公开 多IMU车载GNSS/INS数据集、KITTI等 抗干扰能力强，高频采样，短时间内精度较高 长时间使用会产生误差累积，成本较高 PDR[109] 私有 无需外部信号，可与手机传感器结合 误差会随步数增加而累积 SLAM[110-113] 部分公开 UZH-FPV Drone Racing、OpenLORIS-Scene、OxfordRobotCar、Newer College等 实时性好，精度较高 容易受动态障碍物和视线遮挡影响 无线网络数据集 WiFi[114] 部分公开 SODIndoorLoc、RSSI、UJIIndoorLoc等 成本低，覆盖范围广 精度较低，受环境因素干扰大 蓝牙 部分公开 成本低，功耗低 受环境因素干扰大 RFID 私有 精度较高，功耗低 成本高 UWB[116] 部分公开 精度高，抗干扰能力强，实时定位效果好 成本高 地图数据集 OSM[117] 公开 OpenStreetMap 开源，全球覆盖，数据多样 数据精度不均，信息不完整，格式复杂 街景图像[118-120] 部分公开 Google地图、百度地图、高德地图等 提供360°全景视角 隐私问题，存储成本高
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