Extracting accurate location information from a highly inaccurate traffic accident dataset: A methodology based on a string matching technique
Extracting accurate location information from a highly inaccurate traffic accident dataset: A methodology based on a string matching technique
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DOI：
10.1016/j.trc.2016.04.003
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发表时间：
2016-07-01
影响因子：
8.3
通讯作者：
Sevrovic, Marko
中科院分区：
文献类型：
--
作者：
Miler, Mario;Todic, Filip;Sevrovic, Marko
本研究的目的是开发一种适用于全球的交通事故地点验证模型，无论语言或文化差异如何。为了实现这一目标，使用了一个嵌入式地理信息（VGI）数据集，即OpenStreetMap（OSM）项目。为了测试所开发的模型，对2010年至2014年在萨格勒布市发生的8550起致命或非致命伤害事故进行了评估。交通意外数据是以纸笔方式收集，而交通意外地点则是利用警方车辆内的全球定位系统接收器确定。这种形式的数据输入不可避免地会在几何属性和上下文属性中引入错误。为了完全抵消这些误差，开发的模型包括两个关键概念：jaro-Winkler字符串匹配技术和反距离加权方法。超过66%的交通事故地点被验证，这是一个15%的增长相比，经典的方法。本文概述的模型在估计交通事故的正确位置方面有显着的改进。这反过来又导致估计事故地点质量所需的资源急剧减少。(C)2016作者爱思唯尔有限公司出版
The objective of this research was to develop a model for validating traffic accident locations that would be applicable worldwide, regardless of linguistic or cultural differences. In order to achieve this, a Volunteered Geographic Information (VGI) dataset was used, the OpenStreetMap (OSM) project. To test the developed model, a total of 8550 accidents with fatal or non-fatal injuries that occurred in the City of Zagreb from 2010 to 2014 were evaluated. Traffic accident data was collected using the pen-and-paper method while the traffic accident locations were determined using Global Positioning System (GPS) receivers embedded within police vehicles. This form of data entry invariably introduces errors in both geometric and contextual attributes. To fully counteract these errors, the developed model consists of two key concepts: the jaro-Winkler string matching technique and the Inverse Distance Weighting method. Over 66% of traffic accident locations were validated, which is an increase of 15% when compared to the classical approach. The model outlined in this paper shows a significant improvement in estimating the correct location of traffic accidents. This in turn results in a drastic decrease in resources needed to estimate the quality of accident locations. (C) 2016 The Authors. Published by Elsevier Ltd.