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Abstract

Road safety is one of the critical government transportation concerns, especially on the toll roads. With the increasing number of toll roads as part of infrastructure planning, road traffic accidents are significantly escalating. Developing a system that predicts accidents on toll roads will benefit to reduce the harm that is caused by traffic accidents. This study will propose a method for analysing toll road accidents in Indonesia using historical toll road accident data as a dataset to become a pattern to examine the frequency of accidents. This dataset consists of various parameters from three main factors that cause accidents: human, environmental, and road infrastructure factors. Machine learning technique will be mainly used to determine the most influencing factors by employing classifiers such as Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbors (KNN) can construct the prediction model. Fourteen subfactors from the data were used to predict the future fatalities caused by accidents, which allowed the system to forecast the accident fatality. The results show accuracy performance on the test set with LR, DT, KNN, and GNB models, 85.3%, 79.4%, 87.1%, and 77.1%, respectively. The KNN Classifier model has the most minor error value of 0.6 compared to the other models. The study’s findings will help analyse the causal factors involved in toll road accidents and could be utilised by road authorities to employ risk control options to mitigate the ramifications.

Keywords

Road safety Logistic regression Decision tree Gaussian naive bayes K-nearest neighbors

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References

  1. M. of Transportation, “National Development in the 2020-2024 RPJP,” Jakarta, 2019.
  2. BPS-Statistics Indonesia, “Land Transportation Statistics,” Jakarta, 2021.
  3. Kompas, “List of accidents on Cipali Toll Road throughout 2023,” 2023. .
  4. A. Iqbal, Z. U. Rehman, S. Ali, K. Ullah, and U. Ghani, “Road traffic accident analysis and identification of black spot locations on highway,” Civil Engineering Journal, vol. 6, no. 12, pp. 2448–2456, 2020, doi: 10.28991/cej-2020-03091629.
  5. H. Hanafi, F. Rusgiyarto, and R. Pratama, “Analisis Tingkat Keselamatan Jalan Tol Berdasarkan Metode Pembobotan Korlantas (Studi Kasus: Jalan Tol Cipularang),” Jurnal Teknik: Media Pengembangan Ilmu dan Aplikasi Teknik, vol. 18, no. 2, p. 49, 2020, doi: 10.26874/jt.vol18no2.106.
  6. S. Plainis, I. J. Murray, and I. G. Pallikaris, “Road traffic casualties: understanding the night-time death toll,” Injury prevention, vol. 12, no. 2, pp. 125–138, 2006, doi: 10.1136/ip.2005.011056.
  7. T. Åkerstedt, G. Kecklund, and L. G. Hörte, “Night driving, season, and the risk of highway accidents,” Sleep, vol. 24, no. 4, pp. 401–406, 2001, doi: 10.1093/sleep/24.4.401.
  8. K. H. Abdullah, “Road Safety Intervention : Publication Trends and Future Research Directions,” International Journal of Road Safety, vol. 2, no. 1, pp. 10–18, 2021.
  9. A. J. Ghandour, H. Hammoud, and S. Al-Hajj, “Analyzing factors associated with fatal road crashes: A machine learning approach,” International Journal of Environmental Research and Public Health, vol. 17, no. 11, 2020, doi: 10.3390/ijerph17114111.
  10. A. Tavakoli Kashani, M. Rakhshani Moghadam, and S. Amirifar, “Factors affecting driver injury severity in fatigue and drowsiness accidents: a data mining framework.,” Journal of injury & violence research, vol. 14, no. 1, pp. 75–88, 2022, doi: 10.5249/jivr.v14i1.1679.
  11. M. L. S. Zainy, G. B. Pratama, R. R. Kurnianto, and H. Iridiastadi, “Fatigue Among Indonesian Commercial Vehicle Drivers: A Study Examining Changes in Subjective Responses and Ocular Indicators,” International Journal of Technology, vol. 14, no. 5, pp. 1039–1048, 2023, doi: 10.14716/ijtech.v14i5.4856.
  12. N. Md Yusof et al., “Effect of Road Darkness on Young Driver Behaviour when Approaching Parked or Slow-moving Vehicles in Malaysia,” Automotive Experiences, vol. 6, no. 2, pp. 216–233, May 2023, doi: 10.31603/ae.8206.
  13. E. Yong et al., “Investigation of the Vehicle Driving Trajectory During Turning at Intersectional Roads Using Deep Learning Model,” Automotive Experiences, vol. 7, no. 1, pp. 63–76, Apr. 2024, doi: 10.31603/ae.10649.
  14. W. A. Al Bargi, M. M. Rohani, B. D. Daniel, N. A. Khalifaa, M. I. M. Masirin, and J. Kironde, “Estimating of Critical Gaps at Uncontrolled Intersections under Heterogeneous Traffic Conditions,” Automotive Experiences, vol. 6, no. 2, pp. 429–437, 2023, doi: 10.31603/ae.9406.
  15. A. I. Petrov and A. V Pistsov, “Training and Applying Artificial Neural Networks in Traffic Light Control: Improving the Management and Safety of Road Traffic in Tyumen (Russia),” Automotive Experiences, vol. 6, no. 3, pp. 528–550, 2023, doi: 10.31603/ae.10025.
  16. A. Sudiarno, A. M. D. Ma’arij, I. P. Tama, A. Larasati, and D. Hardiningtyas, “Analyzing Cognitive Load Measurements of the Truck Drivers to Determine Transportation Routes and Improve Safety Driving: A Review Study,” Automotive Experiences, vol. 6, no. 1, pp. 149–161, Apr. 2023, doi: 10.31603/ae.8301.
  17. D. H. Waskito et al., “Analysing the Impact of Human Error on the Severity of Truck Accidents through HFACS and Bayesian Network Models,” Safety, vol. 10, no. 1, p. 8, 2024, doi: https://doi.org/10.3390/safety10010008.
  18. I. Ansori et al., “Enhancing Brake System Evaluation in Periodic Testing of Goods Transport Vehicles through FTA-FMEA Risk Analysis,” Automotive Experiences, vol. 6, no. 2, pp. 320–335, Aug. 2023, doi: 10.31603/ae.8394.
  19. D. W. Karmiadji et al., “Theoretical Experiments on Road Profile Data Analysis using Filter Combinations,” Automotive Experiences, vol. 6, no. 3, pp. 584–598, 2023, doi: 10.31603/ae.9901.
  20. F. Valent, F. Schiava, C. Savonitto, T. Gallo, S. Brusaferro, and F. Barbone, “Risk factors for fatal road traffic accidents in Udine, Italy,” Accident Analysis and Prevention, vol. 34, no. 1, pp. 71–84, 2002, doi: 10.1016/S0001-4575(00)00104-4.
  21. N. Verzosa and R. Miles, “Severity of road crashes involving pedestrians in Metro Manila, Philippines,” Accident Analysis and Prevention, vol. 94, pp. 216–226, 2016, doi: 10.1016/j.aap.2016.06.006.
  22. J. P. S. S. Madushani, R. M. K. Sandamal, D. P. P. Meddage, H. R. Pasindu, and P. I. A. Gomes, “Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers,” Transportation Engineering, vol. 13, no. April, p. 100190, 2023, doi: 10.1016/j.treng.2023.100190.
  23. M. F. Labib, A. S. Rifat, M. M. Hossain, A. K. Das, and F. Nawrine, “Road Accident Analysis and Prediction of Accident Severity by Using Machine Learning in Bangladesh,” 2019 7th International Conference on Smart Computing and Communications, ICSCC 2019, pp. 1–5, 2019, doi: 10.1109/ICSCC.2019.8843640.
  24. G. Mahendra and R. H. Roopashree, “Prediction of Road Accidents in the Different States of India using Machine Learning Algorithms,” 2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023, pp. 1–6, 2023, doi: 10.1109/ICICACS57338.2023.10099519.
  25. A. K. Goel, K. Khan, A. Kushwaha, V. Srivastava, S. Malik, and A. Singh, “A Machine Learning Approach to Analyze Road Accidents,” 2022 IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2022, pp. 1–5, 2022, doi: 10.1109/ICBDS53701.2022.9935867.
  26. S. Soleimani, M. Leitner, and J. Codjoe, “Applying machine learning, text mining, and spatial analysis techniques to develop a highway-railroad grade crossing consolidation model,” Accident Analysis and Prevention, vol. 152, no. January, p. 105985, 2021, doi: 10.1016/j.aap.2021.105985.
  27. P. A. Nandurge and N. V. Dharwadkar, “Analyzing road accident data using machine learning paradigms,” Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017, pp. 604–610, 2017, doi: 10.1109/I-SMAC.2017.8058251.
  28. T. Bokaba, W. Doorsamy, and B. S. Paul, “Comparative study of machine learning classifiers for modelling road traffic accidents,” Applied Sciences, vol. 12, no. 2, p. 828, 2022.
  29. R. E. Al Mamlook, A. Ali, R. A. Hasan, and H. A. Mohamed Kazim, “Machine Learning to Predict the Freeway Traffic Accidents-Based Driving Simulation,” Proceedings of the IEEE National Aerospace Electronics Conference, NAECON, vol. 2019-July, pp. 630–634, 2019, doi: 10.1109/NAECON46414.2019.9058268.
  30. O. H. Kwon, W. Rhee, and Y. Yoon, “Application of classification algorithms for analysis of road safety risk factor dependencies,” Accident Analysis and Prevention, vol. 75, pp. 1–15, 2015, doi: 10.1016/j.aap.2014.11.005.
  31. J. De Oña, G. López, R. Mujalli, and F. J. Calvo, “Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks,” Accident Analysis and Prevention, vol. 51, pp. 1–10, 2013, doi: 10.1016/j.aap.2012.10.016.
  32. S. Y. Sohn and S. H. Lee, “Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea,” Safety Science, vol. 41, no. 1, pp. 1–14, 2003, doi: 10.1016/S0925-7535(01)00032-7.
  33. D. D. Clarke, R. Forsyth, and R. Wright, “Machine learning in road accident research: Decision trees describing road accidents during cross-flow turns,” Ergonomics, vol. 41, no. 7, pp. 1060–1079, 1998, doi: 10.1080/001401398186603.
  34. O. Nedjmedine and M. Tahar, “Analysis of road accident factors using Decision Tree Algorithm: a case of study Algeria,” ISIA 2022 - International Symposium on Informatics and its Applications, Proceedings, pp. 1–6, 2022, doi: 10.1109/ISIA55826.2022.9993530.
  35. A. Iranitalab and A. Khattak, “Comparison of four statistical and machine learning methods for crash severity prediction,” Accident Analysis and Prevention, vol. 108, no. August, pp. 27–36, 2017, doi: 10.1016/j.aap.2017.08.008.
  36. W. Lu, J. Liu, X. Fu, J. Yang, and S. Jones, “Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes,” Accident Analysis and Prevention, vol. 168, no. February, p. 106622, 2022, doi: 10.1016/j.aap.2022.106622.
  37. M. L. Siregar, T. Tjahjono, and N. Yusuf, “Predicting the Segment-Based Effects of Heterogeneous Traffic and Road Geometric Features on Fatal Accidents,” International Journal of Technology, vol. 13, no. 1, pp. 92–102, 2022, doi: 10.14716/ijtech.v13i1.4450.
  38. M. L. Siregar, R. Jachrizal Sumabrata, A. Kusuma, O. B. Samosir, and S. N. Rudrokasworo, “Analyzing driving environment factors in pedestrian crashes injury levels in Jakarta and the surrounding cities,” Journal of Applied Engineering Science, vol. 17, no. 4, pp. 482–489, 2019, doi: 10.5937/jaes17-22121.
  39. A. Rizaldi, V. Dixit, A. Pande, and R. A. Junirman, “Predicting casualty-accident count by highway design standards compliance,” International Journal of Transportation Science and Technology, vol. 6, no. 3, pp. 174–183, 2017, doi: 10.1016/j.ijtst.2017.07.005.
  40. J. Zhang, X. Chen, and Y. Tu, “Environmental and Traffic Effects on Incident Frequency Occurred on Urban Expressways,” Procedia - Social and Behavioral Sciences, vol. 96, no. Cictp, pp. 1366–1377, 2013, doi: 10.1016/j.sbspro.2013.08.155.

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