TY - GEN
T1 - Influence of Geometry on Accident Risk Levels. Application of the Predictive HSM Method on a Rural Road in Perú
AU - Canales, A.
AU - Incio, C.
AU - Silvera, M.
AU - Campos, F.
AU - Palacios-Alonso, D.
N1 - Publisher Copyright:
© 2024 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2024
Y1 - 2024
N2 - This study focuses on thoroughly examining how the geometric characteristics of rural roads in Peru impact safety, using the Highway Safety Manual (HSM) as the primary reference point. By adjusting the predictive HSM model to fit specific conditions, the most significant variables in anticipating road incidents were identified. Out of the 14 considered variables, three emerge as the most relevant in accident prediction. The presence of rumble strips, the Hazard Index in sections with obstacles and barriers account for 47.74% of relevance, while variables related to horizontal curves, such as length and radius, contribute with 18.35% importance in this predictive calculation. This study emphasizes the need to expand the database with information from other roads sharing similar characteristics. This would not only improve the accuracy of the calculation but also confirm the priority of the identified variables for all Second-Class Rural Roads. The results obtained highlight the influence of geometric aspects on the probability of accidents, thus supporting the need for specific improvements. This study underscores the importance of adapting the HSM to the specific conditions of each region in local rural roads. The presented results provide a solid foundation and concrete outcomes for decision-making in the planning and improvement of road safety in similar environments in Perú.
AB - This study focuses on thoroughly examining how the geometric characteristics of rural roads in Peru impact safety, using the Highway Safety Manual (HSM) as the primary reference point. By adjusting the predictive HSM model to fit specific conditions, the most significant variables in anticipating road incidents were identified. Out of the 14 considered variables, three emerge as the most relevant in accident prediction. The presence of rumble strips, the Hazard Index in sections with obstacles and barriers account for 47.74% of relevance, while variables related to horizontal curves, such as length and radius, contribute with 18.35% importance in this predictive calculation. This study emphasizes the need to expand the database with information from other roads sharing similar characteristics. This would not only improve the accuracy of the calculation but also confirm the priority of the identified variables for all Second-Class Rural Roads. The results obtained highlight the influence of geometric aspects on the probability of accidents, thus supporting the need for specific improvements. This study underscores the importance of adapting the HSM to the specific conditions of each region in local rural roads. The presented results provide a solid foundation and concrete outcomes for decision-making in the planning and improvement of road safety in similar environments in Perú.
KW - HSM
KW - Highways
KW - Principal component analysis
KW - Road Accidents
KW - Road Geometry
KW - Second-Class
UR - https://www.scopus.com/pages/publications/85203797338
U2 - 10.18687/LACCEI2024.1.1.939
DO - 10.18687/LACCEI2024.1.1.939
M3 - Contribución a la conferencia
AN - SCOPUS:85203797338
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - Proceedings of the 22nd LACCEI International Multi-Conference for Engineering, Education and Technology
PB - Latin American and Caribbean Consortium of Engineering Institutions
T2 - 22nd LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2024
Y2 - 17 July 2024 through 19 July 2024
ER -