利用事故当量数和泰国农村道路控制上限确定黑点

Q2 Engineering
Wanit Treeranurat, Suthathip Suanmali
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引用次数: 0

摘要

农村公路部(DRR)是泰国负责48000多公里农村公路和公路网的公路主管部门之一。其职责之一是提供更好的道路安全管理。在道路安全程序中,通常通过观察特定路段的事故频率来确定黑点。本研究旨在建立一个模型,其中包括黑点识别过程中的事故严重程度。严重程度的分类仅包括死亡、重伤、轻伤和财产损失。采用层次分析法(AHP)确定各严重等级的权重。利用等效事故数(EAN)和控制上限(UCL)建立了事故识别模型。模型中使用的数据来源于交通事故调查。这5条道路分别是呵叻3052、春武里1032、备武里3021、三木prakarn 2001和清迈3029,是根据过去3年记录的最高事故频率选出的。根据此次研究的黑点结果,大部分事故是由于超速导致的正面和尾部碰撞。本文讨论了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determination of black spots by using accident equivalent number and upper control limit on rural roads of Thailand
Abstract The Department of Rural Roads (DRR) is one of the highway authorities in Thailand responsible for over 48 000 kilometres of rural roads and highway networks. One of its responsibilities is to provide better road safety management. In road safety procedures, black spots are usually identified by observing the frequency of accidents at a particular road section. This research aims to develop a model that includes levels of accident severity in the black spot identification process. The classification of severity levels includes fatalities, serious injuries, minor injuries, and damaged property only. The Analytic Hierarchy Process (AHP) is employed to derive the weight of each severity level. The identification model is developed using Equivalent Accident Number (EAN) and Upper Control Limit (UCL). The data applied in the model are obtained from the road accident investigation of DRR. Five roads — Nakhon Ratchasima 3052, Chonburi 1032, Nonthaburi 3021, Samutprakarn 2001 and Chiangmai 3029 — have been selected based on the top frequency accident recorded in the last three years. Based on the results of black spots identified in the study, most accidents occurred from frontal and rear-ended impacts due to exceeded speed limits. The article discusses recommendations.
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来源期刊
Engineering Management in Production and Services
Engineering Management in Production and Services Business, Management and Accounting-Management Information Systems
CiteScore
3.40
自引率
0.00%
发文量
27
审稿时长
7 weeks
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