Design, Development and Validation of an Intelligent Collision Risk Detection System to Improve Transportation Safety: The Case of the City of Popayán, Colombia

Santiago Felipe Yepes Chamorro, Juan Jose Paredes Rosero, Ricardo Salazar-Cabrera (), Álvaro Pachón de la Cruz and Juan Manuel Madrid Molina
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Santiago Felipe Yepes Chamorro: Telematics Engineering Research Group (GIT), Telematics Department, Universidad del Cauca, Popayán 190001, Colombia
Juan Jose Paredes Rosero: Telematics Engineering Research Group (GIT), Telematics Department, Universidad del Cauca, Popayán 190001, Colombia
Ricardo Salazar-Cabrera: Telematics Engineering Research Group (GIT), Telematics Department, Universidad del Cauca, Popayán 190001, Colombia
Álvaro Pachón de la Cruz: Information Technology and Telecommunications Research Group (I2T), ICT Department, Universidad Icesi, Cali 760001, Colombia
Juan Manuel Madrid Molina: Information Technology and Telecommunications Research Group (I2T), ICT Department, Universidad Icesi, Cali 760001, Colombia

Sustainability, 2022, vol. 14, issue 16, 1-36

Abstract: Several approaches from different perspectives have been used to solve problems with traffic accidents (TA), which mainly affect low- and middle-income countries. Conditions of certain cities, regarding road infrastructure, enforcement of traffic safety regulations, and motor vehicle numbers, influence the increase in TAs. Therefore, medium-sized cities in developing countries (context of interest), which commonly have worrying conditions, are a relevant scenario. One of the approaches to reduce TAs has been the use of data analysis through Machine Learning (ML); however, these techniques require a large amount of data, and medium-sized cities commonly do not have enough. Techniques such as Naturalistic Driving (ND) can be applied as a data collection method. This work proposes an intelligent collision risk detection system (ICDRS) using ND and ML to improve sustainability and safety of transportation in medium-sized cities. The ICRDS design considered the limitations of the context of interest and uses two data collection devices in the vehicle. The ICRDS validation included the design and execution of tests using ND. This validation identified if the collected data in a certain time interval contained high-risk collision events (sudden acceleration, sudden braking, aggressive left or right turn, aggressive left or right lane change). The system implementation results were satisfactory. The developed ML algorithm obtained an average value 0.98 in all the metrics. Two data sets of driving on routes were collected. In addition, the performed tests were able to identify city areas with high accident rates.

Keywords: collision risk detection; machine learning; naturalistic driving; near-crash; sustainability and safety in transportation; traffic accidents (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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