Crash data quality for road safety research: Current state and future directions
Crash data quality for road safety research: Current state and future directions
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DOI：
10.1016/j.aap.2017.02.022
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发表时间：
2019-09-01
影响因子：
5.9
通讯作者：
Quddus, Mohammed
中科院分区：
文献类型：
--
作者：
Imprialou, Marianna;Quddus, Mohammed
碰撞数据库是道路安全研究的主要数据来源之一。因此，它们的质量对于碰撞分析的准确性以及有效对策的设计至关重要。虽然碰撞数据经常受到正确性和完整性问题的影响，但这些问题很少在碰撞分析中讨论或解决。坠机报告旨在回答五个“W”问题（即何时？，在哪？，你说什么？谁啊？为什么？）通过包含一系列属性来分析每一次崩溃。本文回顾了目前的文献对这些问题分别碰撞数据质量的状态。最严重的数据质量问题似乎是：坠机地点和时间不准确，由于数据库不一致而难以进行数据链接（例如与交通数据），严重程度分类错误，所涉用户人口统计数据不准确和不完整，以及对坠机促成因素的识别不准确。结果表明，数据质量问题的程度和严重性是不平等的属性和道路安全分析的影响程度还不完全知道。本文强调了需要进一步研究的领域，并为智能碰撞报告系统的发展提供了一些建议。(C)2017爱思唯尔有限公司版权所有
Crash databases are one of the primary data sources for road safety research. Therefore, their quality is fundamental for the accuracy of crash analyses and, consequently the design of effective countermeasures. Although crash data often suffer from correctness and completeness issues, these are rarely discussed or addressed in crash analyses. Crash reports aim to answer the five "W" questions (i.e. When?, Where?, What?, Who? and Why?) of each crash by including a range of attributes. This paper reviews current literature on the state of crash data quality for each of these questions separately. The most serious data quality issues appear to be: inaccuracies in crash location and time, difficulties in data linkage (e.g. with traffic data) due to inconsistencies in databases, severity misclassification, inaccuracies and incompleteness of involved users' demographics and inaccurate identification of crash contributory factors. It is shown that the extent and the severity of data quality issues are not equal between attributes and the level of impact in road safety analyses is not yet entirely known. This paper highlights areas that require further research and provides some suggestions for the development of intelligent crash reporting systems. (C) 2017 Elsevier Ltd. All rights reserved.