Using Spatial Modeling for Identifying Determinants Influencing Crash Estimate: Case Study of Hamedan, Iran

IF 1.6 4区工程技术 Q3 ENGINEERING, CIVIL

Transportation Research Record Pub Date : 2023-10-07 DOI:10.1177/03611981231197647

Seyed Ahmadreza Almasi, Amir Reza Bakhshi Lomer, Hassan Khaksar, Aynaz Lotfata

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引用次数: 0

Abstract

Road crashes are a major cause of fatalities worldwide, posing significant challenges for road-safety experts in selecting appropriate crash-frequency estimation models. This study introduces localized safety performance functions (C-SPFs), which explore the spatial variation of crash frequency and the spatial correlation between dependent variables. The exploratory spatial regression method is employed to identify optimal spatial associations. The study further predicts crashes using geographically weighted Poisson regression (GWPR) and generalized Poisson regression. Results indicate that C-SPFs offer greater accuracy than do models calibrated solely on annual average daily traffic. Moreover, the proposed model is especially relevant for jurisdictions facing higher heavy-vehicle traffic and frequent crashes. The development of C-SPFs and the use of GWPR provide valuable tools for policymakers and road-safety experts in enhancing crash-frequency estimation accuracy. Implementing these techniques can aid in prioritizing safety measures and countermeasures, especially in regions with significant heavy-vehicle traffic and crash occurrences. Additionally, the integration of spatial-analysis techniques and localized models can lead to more effective transportation planning and targeted road-safety interventions, ultimately contributing to reducing the burden of road crashes on a global scale.

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利用空间模型识别影响事故估计的决定因素:以伊朗哈马丹为例

道路碰撞是世界范围内造成死亡的一个主要原因，这给道路安全专家选择适当的碰撞频率估计模型带来了重大挑战。本研究引入局部安全性能函数(C-SPFs)，探讨碰撞频率的空间变化和因变量之间的空间相关性。采用探索性空间回归方法识别最优空间关联。研究进一步使用地理加权泊松回归(GWPR)和广义泊松回归预测崩溃。结果表明，c - spf比仅根据年平均每日交通量校准的模型提供更高的准确性。此外，所提出的模型特别适用于面临重型车辆流量增加和事故频发的司法管辖区。c - spf的发展和GWPR的使用为政策制定者和道路安全专家提高碰撞频率估计的准确性提供了宝贵的工具。实施这些技术有助于确定安全措施和对策的优先次序，特别是在重型车辆交通和碰撞事件频发的地区。此外，空间分析技术和本地化模型的整合可以带来更有效的交通规划和有针对性的道路安全干预措施，最终有助于在全球范围内减轻道路交通事故的负担。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Transportation Research Record 工程技术-工程：土木

CiteScore

3.20

自引率

11.80%

发文量

918

审稿时长

4.2 months

期刊介绍： Transportation Research Record: Journal of the Transportation Research Board is one of the most cited and prolific transportation journals in the world, offering unparalleled depth and breadth in the coverage of transportation-related topics. The TRR publishes approximately 70 issues annually of outstanding, peer-reviewed papers presenting research findings in policy, planning, administration, economics and financing, operations, construction, design, maintenance, safety, and more, for all modes of transportation. This site provides electronic access to a full compilation of papers since the 1996 series.