School of Civil and Building Engineering, Loughborough University, Loughborough LE11 3TU, United Kingdom.
Accid Anal Prev. 2014 Apr;65:105-13. doi: 10.1016/j.aap.2013.12.001. Epub 2013 Dec 25.
The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to robustly carry out some key analyses in accident research including the identification of accident hot-spots, network-level risk mapping and segment-level accident risk modelling. Existing risk mapping algorithms have some severe limitations: (i) they are not easily 'transferable' as the algorithms are specific to given accident datasets; (ii) they do not perform well in all road-network environments such as in areas of dense road network; and (iii) the methods used do not perform well in addressing inaccuracies inherent in and type of road environment. The purpose of this paper is to develop a new accident mapping algorithm based on the common variables observed in most accident databases (e.g. road name and type, direction of vehicle movement before the accident and recorded accident location). The challenges here are to: (i) develop a method that takes into account uncertainties inherent to the recorded traffic accident data and the underlying digital road network data, (ii) accurately determine the type and proportion of inaccuracies, and (iii) develop a robust algorithm that can be adapted for any accident set and road network of varying complexity. In order to overcome these challenges, a distance based pattern-matching approach is used to identify the correct road segment. This is based on vectors containing feature values that are common in the accident data and the network data. Since each feature does not contribute equally towards the identification of the correct road segments, an ANN approach using the single-layer perceptron is used to assist in "learning" the relative importance of each feature in the distance calculation and hence the correct link identification. The performance of the developed algorithm was evaluated based on a reference accident dataset from the UK confirming that the accuracy is much better than other methods.
事故映射算法的目的是将交通事故准确地映射到相应的道路段上。将事故分配到正确的路段有助于在事故研究中进行一些关键分析，包括识别事故热点、网络级风险映射和路段级事故风险建模。现有的风险映射算法存在一些严重的局限性：（i）它们不容易“转移”，因为算法是特定于给定的事故数据集的；（ii）它们在所有道路网络环境中的表现都不是很好，例如在密集的道路网络区域；（iii）所使用的方法在解决固有和道路环境类型的不准确性方面表现不佳。本文的目的是开发一种新的基于大多数事故数据库中观察到的常见变量（例如道路名称和类型、事故发生前车辆的行驶方向和记录的事故位置）的事故映射算法。这里的挑战是：（i）开发一种方法，考虑到记录的交通事故数据和底层数字道路网络数据固有的不确定性，（ii）准确确定不准确的类型和比例，以及（iii）开发一种可以适应任何事故集和不同复杂程度的道路网络的稳健算法。为了克服这些挑战，使用基于距离的模式匹配方法来识别正确的道路段。这是基于包含在事故数据和网络数据中常见的特征值的向量。由于每个特征对识别正确的道路段的贡献并不相同，因此使用单层感知器的 ANN 方法用于辅助“学习”距离计算中每个特征的相对重要性，从而正确识别链接。基于英国的参考事故数据集评估了所开发算法的性能，结果表明其准确性明显优于其他方法。
