Citation

Paredes JJ, Yepes SF, Salazar-Cabrera R, Pachón de la Cruz, Madrid Molina JM. J. Traffic Transp. Eng. Engl. Ed. 2022; 9(6): 912-929.

Copyright

(Copyright © 2022, Periodical Offices of Chang'an University, Publisher Elsevier Publishing)

DOI

10.1016/j.jtte.2022.07.003

PMID

unavailable

Abstract

Traffic accidents are one of the most serious problems worldwide, being one of the leading causes of death and economic loss in the world. Low- and middle-income countries, mainly their medium-sized cities, are among the most affected by this problem. 93% of traffic accidents occur in low and middle-income countries, even though these countries have approximately 60% of the world's vehicles. This occurs mainly because in these types of countries, especially in medium-sized cities (target context), there are no ideal conditions for driving, such as adequate road infrastructure, good condition of vehicles, and rigorous safety policies. Advanced data analysis techniques including machine learning (ML) have increasingly been used to solve this problem. Naturalistic driving (ND) can be applied as a data collection method that provides information on traffic accidents. ND commonly uses a vehicle's kinematic data to detect high-risk driving behaviors that could cause an accident. The objectives of this document are to present a review of different alternatives that help in data collection and creation of intelligent solutions related to detection of possible traffic accidents, principally using ND; and to propose an intelligent collision risk detection system (ICRDS) for identification of areas with a high probability of TA in the target context. Through the review, it was possible to analyze and evaluate the devices, variables and algorithms that help characterize a risk event in driving, considering the target context. The development of a prototype of an ICRDS for a medium-sized city in a developing country is considered viable, considering the identified components, with the aim of identifying risk events in driving, and areas of high probability of accidents in the city.

Language: en

Keywords

Naturalistic driving; Near-crash; Traffic accident; Vehicle data collection