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 language | English |
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Title of host publication | Intelligent Technologies |
Subtitle of host publication | Design and Applications for Society - Proceedings of CITIS 2022 |
Editors | Vladimir Robles-Bykbaev, Josefa Mula, Gilberto Reynoso-Meza |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 335-345 |
Number of pages | 11 |
ISBN (Print) | 9783031243264 |
DOIs | |
State | Published - 2023 |
Event | 8th International Conference on Science, Technology and Innovation for Society, CITIS 2022 - Guayaquil, Ecuador Duration: 22 Jun 2022 → 24 Jun 2022 |
Publication series
Name | Lecture Notes in Networks and Systems |
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Volume | 607 LNNS |
ISSN (Print) | 2367-3370 |
ISSN (Electronic) | 2367-3389 |
Conference
Conference | 8th International Conference on Science, Technology and Innovation for Society, CITIS 2022 |
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Country/Territory | Ecuador |
City | Guayaquil |
Period | 22/06/22 → 24/06/22 |
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