Predictive Models and GIS for Road Safety: Application to a Segment of the Chone–Flavio Alfaro Road

Luis Alfonso Moreno-Ponce (), Ana María Pérez-Zuriaga () and Alfredo García
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Luis Alfonso Moreno-Ponce: Department of Transport Infrastructure and Engineering, School of Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Valencia, Spain
Ana María Pérez-Zuriaga: Department of Transport Infrastructure and Engineering, School of Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Valencia, Spain
Alfredo García: Department of Transport Infrastructure and Engineering, School of Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Valencia, Spain

Sustainability, 2025, vol. 17, issue 11, 1-24

Abstract: The analysis of traffic crashes facilitates the identification of trends that can inform strategies to enhance road safety. This study aimed to detect high-risk zones and forecast collision patterns by integrating spatial analysis and predictive modeling. Traffic incidents along the Chone–Flavio Alfaro road segment in Manabí, Ecuador, were examined using Geographic Information Systems (GIS) and Kernel Density Estimation (KDE), based on official data from the National Traffic Agency (ANT) covering the period 2017–2023. Additionally, ARIMA, Prophet, and Long Short-Term Memory (LSTM) models were applied to predict crash occurrences. The most influential contributing factors were driver distraction, excessive speed, and adverse weather. Four main crash hotspots were identified: near Chone (PS 0–2.31), PS 2.31–7.10, PS 13.39–21.31, and PS 31.27–33.92, close to Flavio Alfaro. A total of 55 crashes were recorded, with side impacts (27.3%), pedestrian-related collisions (14.5%), and rear-end crashes (12.7%) being the most frequent types. The predictive models performed well, with Prophet achieving the highest estimated accuracy (90.8%), followed by LSTM (88.2%) and ARIMA (87.6%), based on MAE evaluations. These findings underscore the potential of intelligent transportation systems (ITSs) and predictive analytics to support proactive traffic management and resilient infrastructure development in rural regions.

Keywords: traffic crash forecasting; GIS and spatial analysis; predictive models (ARIMA, LSTM, Prophet); infrastructure resilience; rural road safety (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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