Literature DB >> 24531111

Simulation of safety: a review of the state of the art in road safety simulation modelling.

William Young1, Amir Sobhani2, Michael G Lenné3, Majid Sarvi4.   

Abstract

Recent decades have seen considerable growth in computer capabilities, data collection technology and communication mediums. This growth has had considerable impact on our ability to replicate driver behaviour and understand the processes involved in failures in the traffic system. From time to time it is necessary to assess the level of development as a basis of determining how far we have come. This paper sets out to assess the state of the art in the use of computer models to simulate and assess the level of safety in existing and future traffic systems. It reviews developments in the area of road safety simulation models. In particular, it reviews computer models of driver and vehicle behaviour within a road context. It focuses on stochastic numerical models of traffic behaviour and how reliable these are in estimating levels of safety on the traffic network. Models of this type are commonly used in the assessment of traffic systems for capacity, delay and general performance. Adding safety to this assessment regime may allow more comprehensive assessment of future traffic systems. To date the models have focused primarily on vehicular traffic that is, cars and heavy vehicles. It has been shown that these models have potential in measuring the level of conflict on parts of the network and the measure of conflict correlated well with crash statistics. Interest in the prediction of crashes and crash severity is growing and new models are focusing on the continuum of general traffic conditions, conflict, severe conflict, crash and severe crashes. The paper also explores the general data types used to develop, calibrate and validate these models. Recent technological development in in-vehicle data collection, driver simulators and machine learning offers considerable potential for improving the behavioural base, rigour and application of road safety simulation models. The paper closes with some indication of areas of future development.
Copyright © 2014. Published by Elsevier Ltd.

Keywords:  Models; Safety; Simulation; Surrogate safety measures

Mesh:

Year:  2014        PMID: 24531111     DOI: 10.1016/j.aap.2014.01.008

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  3 in total

1.  A Take-Over Performance Evaluation Model for Automated Vehicles from Automated to Manual Driving.

Authors:  Lixin Yan; Jiayu Chen; Chengyue Wen; Ping Wan; Liqun Peng; Xujin Yu
Journal:  Comput Intell Neurosci       Date:  2022-04-15

2.  A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities.

Authors:  Juan S Angarita-Zapata; Gina Maestre-Gongora; Jenny Fajardo Calderín
Journal:  Sensors (Basel)       Date:  2021-12-16       Impact factor: 3.576

3.  The impact of vehicle moving violations and freeway traffic flow on crash risk: An application of plugin development for microsimulation.

Authors:  Junhua Wang; Yumeng Kong; Ting Fu; Joshua Stipancic
Journal:  PLoS One       Date:  2017-09-08       Impact factor: 3.240

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.