Road traffic accidents: An overview of data sources, analysis techniques and contributing factors

被引：70

作者：

Chand, Arun ^{[1
]}

Jayesh, S. ^{[1
]}

Bhasi, A. B. ^{[1
]}

机构：

[1] Cochin Univ Sci & Technol, Sch Engn, Dept Mech Engn, Cochin, Kerala, India

来源：

MATERIALS TODAY-PROCEEDINGS | 2021年 / 47卷

关键词：

Road Safety; Road Traffic Accidents; Statistical Data Analysis; Driver distraction; STATISTICAL-ANALYSIS; GENETIC ALGORITHM; CRASHES; DISTRACTION; SEVERITY; MODELS;

D O I：

10.1016/j.matpr.2021.05.415

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Road traffic accidents are one among the world's leading causes of injuries and fatalities and hence represent an important field of research towards the use of traffic accident analysis and prediction techniques and the determination of the most key factors contributing to road traffic accidents. This paper aims to provide an overview of road accident data sources, data analysis techniques, various algorithms used to build road accident forecasts, and also their suitability to the types of data being examined with the ease of interpretation. The paper also summarizes the operational problems of road traffic, identifies the risk factors, the efficacy of road safety measures when they contribute to the statistical analysis of the severity of motor vehicle accidents and offers an assessment of future methodological approaches. In this review, different gaps in the road traffic accident area were found and further fields of research have been mentioned. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the International Conference on Sustainable materials, Manufacturing and Renewable Technologies 2021.

引用

页码：5135 / 5141

页数：7

共 64 条

[1] Traffic Accidents Analyzer Using Big Data [J].

Abdullah, Eyad ;

Emam, Ahmed .

2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI), 2015, :392-397

[2]

Al-bared M.A.M., 2019, LECT NOTES CIV ENG, P1273, DOI [10.1007/978-981-10-8016-6, DOI 10.1007/978-981-10-8016-6]

[3] Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol [J].

Assi, Khaled ;

Rahman, Syed Masiur ;

Mansoor, Umer ;

Ratrout, Nedal .

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2020, 17 (15) :1-17

[4] The statistical analysis of road traffic in cities of Poland [J].

Bak, Iwona ;

Cheba, Katarzyna ;

Szczecinska, Beata .

3RD INTERNATIONAL CONFERENCE GREEN CITIES - GREEN LOGISTICS FOR GREENER CITIES, 2019, 39 :14-23

[5] Regression Models of Highway Traffic Crashes: A Review of Recent Research and Future Research Needs [J].

Basu, Sujata ;

Saha, Pritam .

TRANSBALTICA 2017: TRANSPORTATION SCIENCE AND TECHNOLOGY, 2017, 187 :59-66

[6] Explaining the road accident risk: Weather effects [J].

Bergel-Hayat, Ruth ;

Debbarh, Mohammed ;

Antoniou, Constantinos ;

Yannis, George .

ACCIDENT ANALYSIS AND PREVENTION, 2013, 60 :456-465

[7] Development of Model for Road Crashes and Identification of Accident Spots [J].

Bhavsar, Rejoice ;

Amin, Amit ;

Zala, Laxmansinh .

INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH, 2021, 19 (01) :99-111

[8] Analyzing the response to traffic accidents in Medellín, Colombia, with facility location models [J].

Castañeda, Carolina P. ;

Villegas, Juan G. .

IATSS Research, 2017, 41 (01) :47-56

[9] Data mining on road safety: factor assessment on vehicle accidents using classification models [J].

Castro, Yuri ;

Kim, Young Jin .

INTERNATIONAL JOURNAL OF CRASHWORTHINESS, 2016, 21 (02) :104-111

[10] Effect of Driver Distraction Contributing Factors on Accident Causations - A Review [J].

Chand, Arun ;

Bhasi, A. B. .

INTERNATIONAL CONFERENCE ON APPLIED MECHANICS AND OPTIMISATION (ICAMEO-2019), 2019, 2134

← 1 2 3 4 5 6 7 →