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Network-wide identification of turn-level intersection congestion using only low-frequency probe vehicle data

机译:仅使用低频探测车辆数据在全网范围内识别转弯路口拥堵

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Locating the bottlenecks in cities where traffic congestion usually occurs is essential prior to solving congestion problems. Therefore, this paper proposes a low-frequency probe vehicle data (PVD)-based method to identify turn-level intersection traffic congestion in an urban road network. This method initially divides an urban area into meter-scale square cells and maps PVD into those cells and then identifies the cells that correspond to road intersections by taking advantage of the fixed-location stop-and-go characteristics of traffic passing through intersections. With those rasterized road intersections, the proposed method recognizes probe vehicles' turning directions and provides preliminary analysis of traffic conditions at all turning directions. The proposed method is map-independent (i.e., no digital map is needed) and computationally efficient and is able to rapidly screen most of the intersections for turn-level congestion in a road network. Thereby, this method is expected to greatly decrease traffic engineers' workloads by providing information regarding where and when to investigate and solve traffic congestion problems.
机译:在解决交通拥堵问题之前,必须将瓶颈定位在通常发生交通拥堵的城市。因此,本文提出了一种基于低频探测车辆数据(PVD)的方法来识别城市道路网络中转弯路口的交通拥堵。此方法首先将市区划分为米级正方形单元,然后将PVD映射到这些单元中,然后利用通过交叉路口的交通的固定位置走走停停的特征来识别与道路交叉口相对应的单元。对于那些栅格化的道路交叉口,该方法可以识别探测车辆的转弯方向,并提供所有转弯方向的交通状况的初步分析。所提出的方法是与地图无关的(即,不需要数字地图)并且计算效率高,并且能够针对路网中的转弯水平拥塞快速地筛选大多数路口。因此,期望该方法通过提供有关在何时何地调查和解决交通拥堵问题的信息来大大减少交通工程师的工作量。

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