基于在线地图交通状态的关键道路动态识别方法

交通拥堵问题日趋严重，关键道路识别成为了交通领域的研究重点. 以在线地图的交通状态数据为基础，利用时空相关性理论计算道路交通状态的预测值和波动影响值，并通过Moran 散点图划分道路类型，提出了基于在线地图交通状态的关键道路动态识别方法. 首先，调用在线地图开发者平台API 采集路网的交通状态数据，利用集成学习动态预测交通状态；其次，分析交通状态波动的传播结构，以此量化道路对其近邻道路的影响值；然后，结合道路交通状态的预测值和波动影响值划分道路类型，进而识别出关键道路；最后，以实际路网为例，论证方法可行性.

Dynamic Identification Method of Critical Roads Based on Traffic State of Online Map

Traffic congestion becomes more and more serious, and critical roads identification has become a research focus in the field of transportation. Based on the traffic state data of online map, this paper calculates prediction of traffic state and affection of traffic state wave by using spatial-temporal correlation theory, classifies different road types by using Moran scatterplot, and then proposes the dynamic identification method of critical roads based on traffic state of online map. Firstly, API of developer platform provided by online map is employed to collect the traffic state in road network, to dynamically predict traffic state by using ensemble learning. Then, the propagation structure of traffic state wave is analyzed to quantify the affection of a road on the roads neighbored it. Next, to identify critical roads, different road types are classified according to the prediction of traffic state and the affection of traffic state wave. Finally, taking actual road network as an example, the feasibility of the method is demonstrated.