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道路安全研究中的碰撞数据质量:现状和未来方向。

Crash data quality for road safety research: Current state and future directions.

机构信息

Transport Studies Group, School of Civil and Building Engineering, Loughborough University, Loughborough LE11 3TU, United Kingdom.

Transport Studies Group, School of Civil and Building Engineering, Loughborough University, Loughborough LE11 3TU, United Kingdom.

出版信息

Accid Anal Prev. 2019 Sep;130:84-90. doi: 10.1016/j.aap.2017.02.022. Epub 2017 Mar 3.

Abstract

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.

摘要

碰撞数据库是道路安全研究的主要数据源之一。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。尽管碰撞数据经常存在正确性和完整性问题,但在碰撞分析中很少讨论或解决这些问题。碰撞报告旨在通过包含一系列属性来回答每个碰撞的五个“W”问题(即何时?何地?何事?何人?为何?)。本文分别审查了当前关于这些问题的碰撞数据质量状况的文献。最严重的数据质量问题似乎是:碰撞地点和时间不准确,由于数据库中的不一致,数据链接(例如与交通数据)困难,严重程度分类错误,涉及用户的人口统计学数据不准确和不完整,以及碰撞促成因素的识别不准确。结果表明,属性之间的数据质量问题的程度和严重程度并不均等,并且在道路安全分析中的影响程度尚不完全清楚。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。

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