基于历史数据的高速多义路径概率识别方法

高速公路多义路径问题是指如何在具有多条可选路径的高速公路网中确定车辆的一条驶经路径.目前普遍采用的基于识别点的多义路径识别方法在某些情况下（如设备故障、环境亮度或透明度不够等）存在识别率低的问题，导致一些时段存在车辆多义路径难以识别.针对以上情况，本文提出一种基于历史数据的多义路径概率识别方法，通过基于路段的聚类方法计算各路段概率值，然后结合贪心算法找出车辆的驶经路径，用来在识别设备故障时辅助识别多义路径.该方法可以有效的在识别设备故障时识别多义路径，提高了该方法的准确度.

Probabilistic Recognition Method of High Speed Polysemy Based on Historical Data

The highway polysyllabic path problem refers to how to determine a driving path of a vehicle in a highway network with multiple optional paths. At present, the identification point-based polysemy path identification method commonly used in some cases (such as equipment failure, ambient brightness or insufficient transparency) has a low recognition rate, which makes it difficult to identify the vehicle polysemy path in some time periods. Aiming at the above situation, this study proposes a multi-sense path probability identification method based on historical data. The road segment-based clustering method is used to calculate the probability values of each road segment, and then the greedy algorithm is used to find the vehicle''s driving path, which is used to identify equipment faults. It assists in identifying polysemy paths. The method can effectively identify the ambiguous path when identifying the equipment failure, and improves the accuracy of the method.