International Journal of Technology 13(1) 92-102 (2022) Received November 2020 / Revised December 2020 / Accepted June 2021 International Journal of Technology http://ijtech.eng.ui.ac.id Predicting the Segment-Based Effects of Heterogeneous Traffic and Road Geometric Features on Fatal Accidents Martha Leni Siregar 1 , Tri Tjahjono 1* , Nahry Yusuf 1 1 Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia Abstract. Inter-urban roads in Indonesia are characterized mainly by distinct road geometry and heterogeneous traffic features. The accident database from the Republic of Indonesia National Traffic Police recorded a substantial number of fatal accidents and fatalities along inter-urban roads. This study aimed to analyze the effects of traffic heterogeneity and road geometry features on fatal accidents along inter-urban roads in South Sulawesi, Indonesia. Segment-based accident analysis was adopted to minimize bias due to the large standard deviations of road lengths. Vehicle-specific speeds, speed standard deviations, and volumes of six vehicle categories, road surface condition, and road geometry were the classified predicting factors. A machine learning technique was adopted to produce predictions of the classification problem. A total of 1,068 road segment observations from 2013–2016 were used to build and validate the model. Model generalization was carried out using the out-of-sample 2019 data. With 26 potential predictors, three machine learning techniques based on the ensembles of regression trees were used to avoid removing potential predictors altogether. The results indicate that road-related features show the greatest importance in predicting the number of fatal accidents. Among the speed features, the average speed of angkots and speed standard deviation of motorcycles showed the greatest importance. The average daily traffic (ADT) of pickups had the greatest importance among other vehicle-specific ADTs. Keywords: Fatal accidents; Heterogeneous traffic; Machine learning; Segment-based effects; Speed standard deviation 1. Introduction Traffic accidents on inter-urban roads in Indonesia are still considered a serious problem, with high rates of fatalities. As an illustration, the fatality rates on Bantaeng – Bulukumba, Jeneponto–Bantaeng, and Bulukumba–Tondong in South Sulawesi were 23.6, 6.5, and 5.1 deaths per 100 million vehicle-km in 2015 (Australia–Indonesia Partnership, 2017) and were 7.03, 3.35, and 8.19 deaths per 100 million vehicle-km in 2019, based on the present study. Various factors related to traffic safety have been studied using different approaches. Studies on speed variations have revealed that both the standard deviation (SD) of speed and the coefficient of speed variation (CSV) were significantly related to traffic collisions (Choudhary et al., 2018; Wang et al., 2018). The effects of road geometry on traffic safety were studied by Chen et al. (2019) and Papadimitriou et al. (2019), and the effects on * Corresponding author’s email: tjahjono@eng.ui.ac.id, Tel.: +62-81311467022 doi: 10.14716/ijtech.v13i1.4450