Estimation of Areas with the Highest Accident Rate on the Cuenca-Loja Road According to the Driving Maneuvers

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Cuenca-Loja Road is a great reflection of the national reality, in the last five years, there have been 13 accidents of public transport vehicles, which left as consequences 159 injuries and 18 deaths. According to data from the National Traffic Agency (ANT) the main cause of accidents is the driver inexperience being 65.56% due to speeding, 23.33% due to invasion of the lane in an overtaking maneuver, and 11.11% for lost control of the vehicle. This research determines the areas with the highest accident rates along the Cuenca - Loja Road based on the physical characteristics of the road and the driving style. Fort the data acquisition a data logger device connected to the OBD II port was used that stores information such as speed, acceleration, and GPS, among others. Then, using automatic learning tools, the correlation between the different characteristics of the road (long slopes, road curves, etc.) and the driving style was determined in order to establish driving parameters appropriate to the geography and road surface.

Original languageEnglish
Title of host publicationIntelligent Technologies
Subtitle of host publicationDesign and Applications for Society - Proceedings of CITIS 2022
EditorsVladimir Robles-Bykbaev, Josefa Mula, Gilberto Reynoso-Meza
PublisherSpringer Science and Business Media Deutschland GmbH
Pages335-345
Number of pages11
ISBN (Print)9783031243264
DOIs
StatePublished - 2023
Event8th International Conference on Science, Technology and Innovation for Society, CITIS 2022 - Guayaquil, Ecuador
Duration: 22 Jun 202224 Jun 2022

Publication series

NameLecture Notes in Networks and Systems
Volume607 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference8th International Conference on Science, Technology and Innovation for Society, CITIS 2022
Country/TerritoryEcuador
CityGuayaquil
Period22/06/2224/06/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Driving behavior
  • Driving style
  • Machine learning
  • Traffic accidents

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