Network-wide road crash risk screening: A new framework

被引:12
作者
Bonera, Michela [1 ]
Barabino, Benedetto [2 ]
Yannis, George [3 ]
Maternini, Giulio [2 ]
机构
[1] Brescia Mobil SpA, Ufficio Studi Ric & Sviluppo, Brescia, Italy
[2] Univ Brescia, Dept Civil Environm Architectural Engn & Math DICA, Brescia, Italy
[3] Natl Tech Univ Athens NTUA, Sch Civil Engn, Dept Transportat Planning & Engn, Athens, Greece
关键词
Network wide screening; Road crash risk; Road infrastructure safety management; Crash probability; Crash severity; Crash exposure; STATISTICAL-ANALYSIS; SAFETY PERFORMANCE; TRAFFIC VOLUME; MODEL; FREQUENCY; REGRESSION; IDENTIFICATION; SEVERITIES; RANKING; COUNTS;
D O I
10.1016/j.aap.2024.107502
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
Network-wide road crash risk screening is a crucial issue for road safety authorities in governing the impact of road infrastructures over road safety worldwide. Specifically, screening methods, which also enable a proactive approach (i.e., pinpointing critical segments before crashes occur), would be extremely beneficial. Existing literature provided valuable insights on road network screening and crash prediction models. However, no research tried to quantify the risk of crash on the road network by considering its main components together (i.e., probability, vulnerability, and exposure). This study covers this gap by a new framework. It integrates road safety factors, prediction models and a riskbased method, and returns the risk value on each road segment as a function of the probability of a crash occurrence and the related severity as well as the exposure model. Next, road segments are ranked according to the risk value and classified by a five-level scale, to show the parts of road network with the highest crash risk. Experiments show the capability of this framework by integrating base map data, context information, road traffic data and five years of real-world crash data records of the whole non-urban road network of the Province of Brescia (Lombardy Region - Italy). This framework introduces a valid support for road safety authorities to help identify the most critical road segments on the network, prioritise interventions and, possibly, improve the safety performance. Finally, this framework can be incorporated in any safety managerial system.
引用
收藏
页数:17
相关论文
共 63 条
[1]   Applying a joint model of crash count and crash severity to identify road segments with high risk of fatal and serious injury crashes [J].
Afghari, Amir Pooyan ;
Haque, Md. Mazharul ;
Washington, Simon .
ACCIDENT ANALYSIS AND PREVENTION, 2020, 144
[2]   Bayesian Multivariate Poisson Lognormal Models for Crash Severity Modeling and Site Ranking [J].
Aguero-Valverde, Jonathan ;
Jovanils, Paul P. .
TRANSPORTATION RESEARCH RECORD, 2009, (2136) :82-91
[3]   An international review of challenges and opportunities in development and use of crash prediction models [J].
Ambros, Jiri ;
Jurewicz, Chris ;
Turner, Shane ;
Kiec, Mariusz .
EUROPEAN TRANSPORT RESEARCH REVIEW, 2018, 10 (02)
[4]  
American Association of State Highway and Transportation Officials - AASHTO, 2010, High-way Safety Manual
[5]  
American Association of State Highway and Transportation Officials - AASHTO, 2014, High-way Safety Manual
[6]   Comparison of univariate and two-stage approaches for estimating crash frequency by severity-Case study for horizontal curves on two-lane rural roads [J].
Anarkooli, Alireza Jafari ;
Persaud, Bhagwant ;
Hosseinpour, Mehdi ;
Saleem, Taha .
ACCIDENT ANALYSIS AND PREVENTION, 2019, 129 :382-389
[7]   An empirical assessment of fixed and random parameter logit models using crash- and non-crash-specific injury data [J].
Anastasopoulos, Panagiotis Ch. ;
Mannering, Fred .
ACCIDENT ANALYSIS AND PREVENTION, 2011, 43 (03) :1140-1147
[8]   Estimating traffic volume on Wyoming low volume roads using linear and logistic regression methods [J].
Apronti, Dick ;
Ksaibati, Khaled ;
Gerow, Kenneth ;
Hepner, Jaime Jo .
JOURNAL OF TRAFFIC AND TRANSPORTATION ENGINEERING-ENGLISH EDITION, 2016, 3 (06) :493-506
[9]   Bus crash risk evaluation: An adjusted framework and its application in a real network [J].
Barabino, Benedetto ;
Bonera, Michela ;
Maternini, Giulio ;
Olivo, Alessandro ;
Porcu, Fabio .
ACCIDENT ANALYSIS AND PREVENTION, 2021, 159
[10]  
Bonera M., 2022, ADV ROAD INFRASTRUCT, P525, DOI DOI 10.1007/978-3-030-79801-7_38