Article Per Year (5 Year)
p-Index From 2021 - 2026
0.835
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All Journal International Journal on Advanced Technology, Engineering, and Information System (IJATEIS)
Sutanto Soehodho
Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Factors Influencing Traffic Accidents on the Cipularang Toll Road: An Analysis Using Multiple Linear Regression Dedi Kurniawan; Sutanto Soehodho; R. Jachrizal Sumabrata
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 5 No. 2 (2026): MAY
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v5i2.2235

Abstract

Traffic accidents remain a critical issue in road safety, particularly on toll roads with high traffic intensity such as the Cipularang Toll Road. This study aims to analyze the factors influencing traffic accidents by applying a multiple linear regression approach, with a focus on human-related factors represented by driver physical condition. Data spanning 2023–2025 from the Cipularang Toll Road corridor were collected through structured interview surveys and supported by secondary data from official accident records. The variables examined include driver characteristics, vehicle conditions, traffic and environmental factors, and behavioral aspects. The results show that estimated speed and driving license type have a statistically significant effect on the dependent variable at the 5% significance level. Estimated speed is identified as the most influential factor, indicating that higher speeds are associated with a decline in driver physical condition, which may increase accident risk. The selection of physical condition as the dependent variable is supported by police reports indicating that a substantial number of accidents are caused by driver fatigue, drowsiness, and reduced alertness, especially on long-distance toll roads. Other variables, although not statistically significant, demonstrate relationships consistent with theoretical expectations. These findings highlight the importance of addressing both speed management and driver fatigue in reducing accident risk. The study contributes to evidence-based road safety strategies by providing a comprehensive analysis of accident-related factors, helping policymakers and toll road operators design more effective safety interventions, especially on high-risk segments like the Cipularang Toll Road.
Understanding Traffic Accident Patterns on the Jakarta-Cikampek Toll Road: An Integrated Approach Combining Blackspot Analysis and Human Factors Relif Karnadi; Sutanto Soehodho; R. Jachrizal Sumabrata
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 5 No. 3 (2026): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v5i3.2237

Abstract

Traffic accidents on toll roads remain a major safety concern, particularly on high-traffic corridors such as the Jakarta-Cikampek Toll Road in Indonesia. This study aims to identify accident-prone locations (blackspots) and analyze contributing factors, with a focus on human-related aspects such as fatigue and rest adequacy. An integrated approach combining spatial analysis and multiple linear regression was employed to better understand accident patterns and their determinants. The study utilizes historical accident data from the Indonesian National Police Traffic Corps and toll road operators for the period 2021-2023, complemented by interview-based behavioral data. Blackspots were identified using a severity-based weighting method, while regression analysis examined the relationship between rest adequacy and variables such as gender, driving experience, travel characteristics, fatigue indicators, sleep duration, and risk perception. The results indicate that no variables are statistically significant at the 5 percent level. However, gender shows the strongest relationship with rest adequacy (β = -0.313; Sig. = 0.060), while sleep duration (β = 0.156) and risk perception (β = 0.102) exhibit positive tendencies. Fatigue indicators show mixed results, suggesting that fatigue is a complex and multidimensional factor. Spatial analysis also reveals several high-risk segments associated with traffic density and road conditions. These findings highlight the need for integrated safety strategies that address both location-based risks and human factors. The study contributes to evidence-based approaches for improving toll road safety.
Analysis of Human Error Factors in Traffic Accident (Case Study: Tangerang-Merak Toll Road) Dika Ruslaninur Yadi; Martha Leni Siregar; Sutanto Soehodho
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 5 No. 2 (2026): MAY
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v5i2.2230

Abstract

Traffic accidents carry significant social, economic, and public safety consequences. In Indonesia, human error accounts for over 60% of accident cases, based on data from the Indonesian National Police Traffic Corps. The Tangerang-Merak Toll Road, a vital corridor connecting industrial areas and major ports in western Java, has seen a notable increase in accidents in recent years, underscoring the need for a deeper understanding of human error contributions. This study aims to analyze the influence of human error-related factors, including driving behavior, risk perception, environmental factors, and individual characteristics, on traffic accidents on the Tangerang-Merak Toll Road. A quantitative approach using Structural Equation Modeling Partial Least Squares (SEM-PLS) was employed to examine these relationships. Data were collected through observation, literature review, and questionnaires. The model demonstrates strong explanatory power, with an R-square value of 0.606, indicating that 60.6% of the variation in traffic accidents is explained by the included variables. Driving behavior is the most dominant factor, with a path coefficient of 0.669, followed by risk perception (0.248). Environmental factors show a negative relationship (-0.478), suggesting that certain environmental conditions may reduce accident risk. Individual characteristics exhibit smaller and mixed effects, including driving experience (0.134), frequency of toll road usage (-0.149), vehicle type (-0.073), and gender (-0.144).
Driving License Testing Quality and Driver Competence in Urban Traffic Safety Johannes Johannes; Sutanto Soehodho; Martha Leni Siregar
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 5 No. 2 (2026): MAY
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v5i2.2233

Abstract

Road traffic accidents remain a pressing public safety concern in rapidly urbanizing cities. In Bekasi City, accident cases rose from 683 in 2022 to 1,146 in 2025, with a growing proportion of perpetrators found to be licensed drivers, raising questions about whether the driving license (SIM) testing system adequately ensures driver competence. This study examines the relationship between the quality of SIM testing and driver competence, focusing on hazard perception, safety knowledge, and driving behavior in Bekasi City. A quantitative approach was employed using Analysis of Variance (ANOVA) within the General Linear Model (GLM) framework, involving 120 respondents: 60 SIM A (car drivers) and 60 SIM C (motorcycle riders). Data were collected through questionnaires based on official SIM training modules. The results show that for SIM A drivers, the model significantly affects hazard perception and safety knowledge (Sig. = 0.000), but not driving behavior (Sig. = 0.102), with R² values of 0.818, 0.568, and 0.197, respectively. For SIM C riders, the model is significant across all variables (Sig. = 0.000), with R² values of 0.696, 0.633, and 0.562. Age and driving experience significantly influence cognitive aspects, while license ownership shows a consistently strong effect (Sig. = 0.000). Questionnaire accuracy ranged from 78% to 92% across all categories. Despite high cognitive scores, a gap persists between knowledge and actual driving behavior, particularly among SIM A drivers. It is recommended that the system incorporate behavior-based evaluations and real-world driving assessments to improve road safety outcomes.
Analysis of the Effect of Road Geometric Factors on Accident Frequency and Safety Level on the Sungai Duren-Sungai Buluh Road Section in Jambi Province Ilfandri Hagia; Sutanto Soehodho; R Jachrizal Sumabrata
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 5 No. 3 (2026): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v5i3.2254

Abstract

Road infrastructure conditions, particularly geometric characteristics, are important factors that may influence traffic accident risk on provincial road sections. This study examines the effect of road geometric factors on traffic accident frequency and evaluates the road safety level of the Sungai Duren-Sungai Buluh Road section in Jambi Province, Indonesia. A quantitative explanatory design was applied to a 13 km road section divided into 130 segments of 100 m. Accident data were obtained from the Integrated Road Safety Management System of the Jambi Regional Police for the 2019-August 2025 period, while geometric and road safety attribute data were collected from as-built drawings, field surveys, and video-based road inventory. Poisson regression was used to identify factors affecting accident frequency, and the iRAP Star Rating method was applied to assess infrastructure-related safety risk. The results show that hilliness, bendiness, and operating speed have significant positive effects on accident frequency, with significance values of 0.007, 0.042, and 0.038, respectively. Segments without berms were more vulnerable to accident risk, particularly due to hilliness, operating speed, and pavement condition. The iRAP assessment showed that 84.6% of the segments were classified as 1-star and 2-star roads, indicating a low safety level. The improvement simulation increased the Star Rating from 1 Star to 4 Stars through targeted infrastructure interventions. These findings imply that integrating Poisson regression and iRAP assessment can support data-driven prioritization of road safety improvements on high-risk provincial road segments.
Co-Authors Dedi Kurniawan Dika Ruslaninur Yadi Ilfandri Hagia Johannes Johannes Martha Leni Siregar R Jachrizal Sumabrata R. Jachrizal Sumabrata Relif Karnadi