Data information table.

The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.

随着交通轨迹数据的快速增长与定位技术的持续迭代，其分析处理的需求日益攀升。然而当前实际应用场景中仍存在诸多痛点：定位数据与真实道路路网存在偏移偏差，且现有轨迹数据交通预测模型的精度普遍偏低。为此，本研究提出了一种基于隐马尔可夫模型（Hidden Markov Model, HMM）的地图匹配算法。该算法以全局路线为基准，选取K近邻候选路径，并通过候选路径筛选匹配候选点；算法利用车速、方位角等信息的变化生成状态转移矩阵，实现轨迹点与实际道路的精准匹配。实验阶段处理轨迹数据时，选取K=5作为最优参数，该算法单条轨迹匹配耗时仅51毫秒，匹配准确率达95.3%。该算法在多种道路工况下均表现优异，尤其在并行道路与混合道路路段，匹配准确率超过96%。尽管相较于传统算法，该算法的耗时略有增加，但匹配精度更为稳定。在不同道路工况下，该算法的匹配准确率分别为98.3%、97.5%、94.8%与96%；而传统隐马尔可夫模型算法在对应工况下的准确率仅分别为95.9%、95.7%、95.4%与94.6%。由此可见，本研究提出的算法具备更优异的匹配精度。实验结果表明，基于隐马尔可夫模型的地图匹配算法在匹配精度方面优于其他同类算法。本研究有效提升了交通轨迹数据的处理精度。