基于向量识别的启发式路径推测算法

A Heuristic Path-Estimating Algorithm by Using Vector-Based Recognition

浮动车数据主要是由车辆的轨迹点数据组成,是一种重要的原始数据,可以广泛地用于各种交通应用,如交通管理和控制、路况计算等.但是原始的车辆GPS数据存在定位误差,必须经过路径推测的修正处理才可以应用.传统的路径推测算法主要采用两种方法:渐增式和全局式.两种方法各有优缺点,渐增式方法计算速度快但准确性差,全局式方法准确性好但计算速度慢.通过综合考虑两种传统算法,文中提出了一种基于向量识别的启发式路径推测算法,该算法采用了启发式图搜索方式,导入几何运算的约束条件,根据车辆轨迹点所形成的向量与路网模型比较来进行启发式搜索,并选择车辆所有可能行驶的候选路径.根据全局择优的方式从整体进行比较,确定车辆最有可能的行驶路径.实验结果表明,这种算法能够在复杂路网下,比较准确地推测距离间隔较大的车辆轨迹点,并且能够实时高效地处理大规模数据.

Floating Car Data （FCD） is an important material for a broad range of application such as traffic management and control, traffic conditions computation and so on. However, in the original data exists error, and the data must be handled by path-estimating and be related to the road. The traditional path-estimating algorithms mainly use two methods： the incremental method and the global method. Both of them have advantages and disadvantages of themselves, while the global map-matching algorithm produces better matching results, the incremental algorithm produces results of lower quality faster. All things considering the two traditional algorithms, this paper proposes a heuristic path-estimating algorithm by using vector-based recognition. Firstly, the algorithm uses the heuristic search method, and it makes use of geometric operation to form the restriction, and make the comparison between the vector formed with the vehicular GPS points and the special road network model to search and select the vehicular possible traveling routes. Secondly, it globally compares all the vehicular possible traveling routes, and then chooses the optimal one. The result of testing demonstrates the efficiency of the algorithm both at accuracy and computational speed when handling the large-scale data of GPS tracking data even under the complex road network conditions.