Probability of Single-Vehicle Accidents Among Elderly Motorcyclists in Indonesia
DOI:
https://doi.org/10.31436/iiumej.v26i3.3474Keywords:
Accident, Bayesian, Elderly, Motorcyclists, RiderAbstract
The number of accidents involving elderly motorcyclists is relatively high compared to other age groups. This is due to various limitations commonly experienced by older riders. This study aims to determine the probability of single-vehicle accidents among elderly motorcyclists in relation to human, road, and environmental factors. A total of 564 respondents participated in the study conducted in Riau Province, Indonesia. Data were collected through interviews with elderly motorcyclists who had previously experienced accidents. The data were analyzed using a Bayesian Network model with GeNIe 2.0 software. The results showed that the probability of single-vehicle accidents among elderly motorcyclists is 59%. Model validation indicated a Mean Absolute Deviation (MAD) of 23%. Male elderly motorcyclists have a 60% likelihood of experiencing single-vehicle accidents (Scenario 1), while female elderly motorcyclists have a 59% likelihood (Scenario 2). Those who ride while fatigued have a 65% probability of a single-vehicle accident (Scenario 3), compared to 57% for those who are not fatigued (Scenario 4). Riding in rainy conditions increases the probability to 70% (Scenario 5), whereas riding in dry conditions reduces it to 55% (Scenario 6). Elderly motorcyclists riding on potholed roads have a 64% chance of accidents (Scenario 7), compared to 57% on roads without potholes (Scenario 8). These findings indicate that elderly riders are highly vulnerable to single-vehicle accidents. Among human factors, fatigue is the most significant variable influencing accident probability. Regarding environmental factors, driving in the rain plays a key role, while riding on potholed roads is the primary influence for road factors. This study highlights the dominant factors contributing to single-vehicle accidents among elderly motorcyclists by integrating human, road, and environmental considerations.
ABSTRAK: Bilangan kemalangan dalam kalangan penunggang motosikal warga emas agak tinggi berbanding kumpulan umur yang lain.Hal ini disebabkan oleh beberapa batasan yang dialami oleh penunggang motosikal warga emas.Tujuan kajian ini adalah untuk menentukan kebarangkalian kemalangan bujang dalam kalangan penunggang motosikal warga emas berkaitan faktor manusia, jalan raya dan persekitaran.Jumlah sampel ialah 563 responden dan lokasi kajian adalah di Wilayah Riau, Indonesia. Pengumpulan data yang pernah dialami oleh penunggang motosikal dengan menggunakan analisis elderata. Rangkaian Bayesian dengan Perisian GeNie 2.0. Keputusan menunjukkan kebarangkalian kemalangan bujang dalam kalangan penunggang motosikal warga emas ialah 59%.Hasil pengesahan model menunjukkan nilai MAD sebanyak 23%. Penunggang motosikal warga emas lelaki berkemungkinan mengalami kemalangan bujang sebanyak 60% (senario 1), penunggang motosikal warga emas wanita sebanyak 59% (senario 2). Penunggang motosikal warga emas yang memandu semasa keletihan berkemungkinan mengalami satu kemalangan sebanyak 65% (senario 3), dalam keadaan tidak letih 57% (senario 4 yang memandu dalam keadaan hujan yang berkemungkinan besar dalam keadaan hujan). 70% (senario 5), dalam keadaan tidak hujan 55% (senario 6). Penunggang motosikal warga emas yang memandu di jalan berlubang berkemungkinan akan mengalami satu kemalangan sebanyak 64% (senario 7), di jalan tanpa jalan berlubang 57% (senario 8). Bermakna pemandu warga emas sangat terdedah untuk mengalami kemalangan bujang. Pembolehubah yang paling mempengaruhi kemungkinan pemandu warga emas mengalami kemalangan bujang ialah keletihan dari segi faktor manusia. Bagi faktor persekitaran, pembolehubah yang mempengaruhi pemandu warga emas yang mengalami kemalangan bujang ialah memandu dalam hujan. Bagi faktor jalan raya, pembolehubah yang mempengaruhi kemalangan tunggal ialah memandu di jalan berlubang. Dapatan kajian ini mendapat faktor dominan yang menyebabkan kemalangan bujang pada pemandu warga emas dengan mengambil kira faktor manusia, jalan raya dan persekitaran.
Downloads
Metrics
References
Bucsuházy K, Matuchová E, Z?vala R, Moravcová P, Kostíková M, Mikulec R. (2020) Human factors contributing to the road traffic accident occurrence.Transportation Research Procedia, 45(2019), 555–561. https://doi.org/10.1016/j.trpro.2020.03.057
Chouhan SS, Kathuria A, Sekhar CR. (2023) The motorcycle rider behaviour questionnaire as a predictor of crashes: A systematic review and meta-analysis. IATSS Research, 47 (2023):61–72
Lumba P, Ariyanto A, Fathoni A. (2024) Strategies for Enhancing Traffic Safety among Adolescent and Adult Motorcycle Riders in Indonesia. Jordan Journal of Civil Engineering, Volume 18(2):293-307
Chu H-C. (2020) Risky behaviors of older taxi drivers and suggested requirements for renewing their professional driver’s licenses. Transportation Research Interdisciplinary Perspectives, 8 (2020), 100272. https://doi.org/10.1016/j.trip.2020.100272
Yeung JS, Wong YD. (2015) Effects of driver age and experience in abruptonset hazards. Accident Analysis and Prevention, 78(2015):110–117. http://dx.doi.org/10.1016/j.aap.2015.02.024
Wada S, Hagiwara T, Hamaoka H, Ninomiya Y, Ohiro T, Tada M. (2020) Differences in situational awareness between elderly and middle-aged drivers in level 2 automated vehicles versus nonautomated vehicles. Asian Transport Studies, 6 (2020), 100014. https://doi.org/10.1016/j.eastsj.2020.100014
Classen S, Mason J, Hwangbo SW, Wersal J, Rogers J, Sisiopiku V. (2021). Older drivers’ experience with automated vehicle technology. Journal of Transport & Health, 22 (2021), 101107. https://doi.org/10.1016/j.jth.2021.101107
Stamatiadis N, Kirk A. (2019) Use of Technology-Based Strategies for Older Driver Risk Mitigation. AIIT 2nd International Congress on Transport Infrastructure and Systems in a Changing World. (TIS ROMA 2019), 23rd-24th September 2019, Rome, Italy. Transportation Research Procedia, 45(2020): 651–658. https://doi.org/10.1016/j.trpro.2020.03.045
Nguyen H, Coxon K, Brown J, Neville N, Tanna GLD, Hsieh Y-W, Keay L. (2023) Older drivers in Australia and advanced vehicle technologies: What are their opinions? A qualitative study. Journal of Transport & Health, 31 (2023) 101646
Toepper M, Austerschmidt KL, Schlueter DA, Koenig J, Beblo T, Driessen M. (2024) On-road driving performances at traffic signs and signals, complex intersections and left turns distinguish fit and unfit older drivers. Transportation Research Part F: Psychology and Behaviour, 102(2024):54–63
Skyving M, Forsman A, Willstrand TD, Laflamme L, Moller J. (2021) Medical impairment and road traffic crashes among older drivers in Sweden – A national, population-based, case-control study. Accident Analysis and Prevention, 163 (2021), 106434. https://doi.org/10.1016/j.aap.2021.106434
Madvari RF, Sefidkar R, Halvani GH, Alizadeh HM. (2023) Quantitative indicators of street lighting with mood, fatigue, mental workload and sleepiness in car drivers: Using generalized structural equation modeling. Heliyon, 9 (2023) e12904. https://doi.org/10.1016/j.heliyon.2023.e12904
Davidovic, J., Pešic, D., Lipovac, K., & Antic, B. (2020). The Significance of developing road safety performance indicators related to Driver Fatigue. AIIT 2nd International Congress on Transport Infrastructure and Systems in a changing world (TIS ROMA 2019), 23rd-24th September 2019, Rome, Italy. Transportation Research Procedia, 45 (2020): 333–342. https://doi.org/10.1016/j.trpro.2020.03.024
Lumba P, Edison B, Fahmi K, Sibarani AS, Ariyanto A, Hidayat A, Rahmi A, Rismalinda. (2022) Effects of Sleep Deprivation on Probability of Traffic Violations in Motorcyclists; Analysis Using Bayesian Network. Science and Technology Asia, Vol.27 No.2 April - June 2022, Page [115-125]. doi: 10.14456/scitechasia.2022.29.
Elvik R. (2023) Driver mileage and accident involvement: A synthesis of evidence. Accident Analysis and Prevention, 179 (2023), 106899. https://doi.org/10.1016/j.aap.2022.106899
Lumba P, Ariyanto A, Fathoni A. (2024a). “STRATEGIES TO REDUCE THE NUMBER OF SEVERELY INJURED VICTIMS IN ADOLESCENT MOTORCYCLE RIDERS”. IIUM Engineering Journal, Vol. 25(1):153-166. https://doi.org/10.31436/iiumej.v25i1.2997
Wijayanto T, Marcillia SR, Lu?tyanto G, Wisnugraha BB, Alma TG, Abdianto RU. (2021) The effect of situation awareness on driving performance in young sleep-deprived drivers. IATSS Research, 45 (2021): 218–225. https://doi.org/10.1016/j.iatssr.2020.10.002
Takeyama E, Tomooka K, Wada H, Sato S, Sakiyama N, Shirahama R, Tanigawa T. (2023) Association between daytime sleepiness and motor vehicle accidents among Japanese male taxi drivers. IATSS Research, 47(2023):299–304. https://doi.org/10.1016/j.iatssr.2023.05.002
Kim K, Matsuhashi K, Ishikawa M. (2023) Analysis of primary-party traffic accident rates per driver in Japan from 1995 to 2015: Do older drivers cause more accidents?. IATSS Research, 47(2023):447–454
Dzinyela R, Adanu EK, Lord D, Islam S. (2023) Analysis of factors that influence injury severity of single and multivehicle crashes involving at-fault older drivers: A random parameters logit with heterogeneity in means and variances approach. Transportation Research Interdisciplinary Perspectives, 22 (2023), 100974
Ulleberg P, Bjørnskau T, Fostervold KI. (2022) Does age matter? Examining age-dependent differences in at-fault collisions after attending a refresher course for older drivers. Transportation Research Part F: Psychology and Behaviour, 87 (2022): 379–390. https://doi.org/10.1016/j.trf.2022.04.016
Keskinen E. (2014). Education for older drivers in the future. IATSS Research, 38(2014):14–21
Zhao L, Wang C, Yang H, Wu X, Zhu T, Wang J. (2023) Exploring injury severity of Non-Motor vehicle riders involving in traf?c accidents using the generalized ordered logit model. Ain Shams Engineering Journal, 14 (2023), 101962. https://doi.org/10.1016/j.asej.2022.101962
Larue GS, Rakotonirainya A, Pettitt AN. (2011) Driving performance impairments due to hypovigilance on monotonous roads. Accident Analysis and Prevention, 43(2011):2037– 2046, Elsevier. https://doi.org/10.1016/j.aap.2011.05.023
Regev S, Rolison JJ, Moutari S. (2018). “Crash risk by driver age, gender, and time of day using a new exposure methodology”. Journal of Safety Research, 66 (2018), 131–140. https://doi.org/10.1016/j.jsr.2018.07.002
Ministry of Transportation. (2020). Available from: https://dephub.go.id/post/read/korban-kecelakaan-lalin-didominasi-usia-produktif,-menhub-ajak-para-pelajar-selalu-disiplin-berlalu-lintas-dan-utamakan-aspek-keselamatan
Zhu Y, Jiang M, Yamamoto T. (2022) Analysis on the driving behavior of old drivers by driving recorder GPS trajectory data. Asian Transport Studies, 8 (2022), 100063. https://doi.org/10.1016/j.eastsj.2022.100063
BPS-STATISTIC INDONESIA. Land Transportation Statistics. 2023. Available from: https://www.bps.go.id/en/publication/2024/11/25/cdcf9b5e74dd2e9bb3458ee4/land-transportation-statistics-2023.html
Haan TD, Stuiver A, Lorist MM, Waard DD. (2022) Other road users’ adaptations to increase safety in response to older drivers’ behavior. Transportation Research Part F: Psychology and Behaviour, 84 (2022): 277–286. https://doi.org/10.1016/j.trf.2021.12.009
Abdubrani R, Mustafa M, Zahari Zl. (2023). A robust framework for driver fatigue detection from EEG signals using enhancement of modified z-score and multiple machine learning architectures. IIUM Engineering Journal, 24(2):354372. https://doi.org/10.31436/iiumej.v24i2.2799
Polda Provinsi Riau; 2022. Available from: https://www.detik.com/sumut/hukum-dan-kriminal/d-6490638/polda-ada-651-orang-tewas-kecelakaan-di-riau-sepanjang-2022
Software GeNie. (2023). Available from: https://download.bayesfusion.com/files.html?category=Academia
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 IIUM Press
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
