Abstract
Purpose
Motorcycles are one of the highly used modes of transport in Barcelona, Spain, in particular, and in many different regions, in general. This situation is compromising safety on the road and may be attributed to a potential increase in traffic crashes. Therefore, this study examines several risk factors and their consequential impacts on the level of injury that is resulted in case of a traffic crash.
Methods
Two approaches are employed to analyze the risk factors, including a supervised learning technique which is a binary probit model, and an unsupervised technique which is the Kohonen clustering.
Results
The results for both models show that alcoholism and road in poor condition can indeed increase the probability of having different levels of injuries as reasons for the crash. Elderly users are less likely to be involved in motorcycle crash injuries compared to other age categories, especially the age group that ranges from 25 to 40 years old which has the highest odds. For both techniques, the performance in analyzing the utilized data shows that both approaches can be successfully utilized for this type of dataset.
Conclusion
This study highlights the considerable danger faced by motorcyclists due to various risk factors. It stresses the critical importance of maintaining roads in optimal condition not just for efficient travel but also to enhance motorcyclists’ safety. Additionally, the research underscores the significant threat posed by speeding, particularly exceeding speed limits, to motorcyclists’ safety, emphasizing the urgent need for more 30 km/h speed limit zones and stricter enforcement of speed regulations. As a result, the research has identified several risk factors that increase the likelihood of severe or fatal injuries among motorcyclists in Barcelona and has suggested certain recommendations to mitigate their impact.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00068-024-02521-y/MediaObjects/68_2024_2521_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00068-024-02521-y/MediaObjects/68_2024_2521_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00068-024-02521-y/MediaObjects/68_2024_2521_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00068-024-02521-y/MediaObjects/68_2024_2521_Fig4_HTML.png)
Similar content being viewed by others
References
Konlan KD, Doat AR, Mohammed I, Amoah RM, Saah JA, Konlan KD, Abdulai JA. Prevalence and pattern of road traffic accidents among commercial motorcyclists in the Central Tongu District, Ghana. Sci World J. 2020;2020:10.
Pinch P, Reimer S. Moto-mobilities: geographies of the motorcycle and motorcyclists. Mobilities. 2012;7(3):439–57.
Marquet O, Miralles-Guasch C. City of motorcycles. On how objective and subjective factors are behind the rise of two-wheeled mobility in Barcelona. Transp Policy. 2016;52:37–45.
de Oliveira LK, de Oliveira IK, Caliari PHC, de Mello CA, Maia MLA. Influence of demographic and socioeconomic factors on motorcycle usage in Brazil. Case Studies on Transport Policy. 2021;9(4):1757–69.
Konkor I. Examining the relationship between transportation mode and the experience of road traffic accident in the upper west region of Ghana. Case Studies Transport Policy. 2021;9(2):715–22.
Rice TM, Troszak L, Ouellet JV, Erhardt T, Smith GS, Tsai B-W. Motorcycle helmet use and the risk of head, neck, and fatal injury: revisiting the Hurt Study. Accid Anal Prev. 2016;91:200–7.
Cunto FJC, Ferreira S. An analysis of the injury severity of motorcycle crashes in Brazil using mixed ordered response models. J Transp Saf Sec. 2017;9(1):33–46.
Kashani AT, Rabieyan R, Besharati MM. A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers. J Safety Res. 2014;51:93–8.
Kim C-Y, Wiznia DH, Averbukh L, Dai F, Leslie MP. The economic impact of helmet use on motorcycle accidents: a systematic review and meta-analysis of the literature from the past 20 years. Traffic Inj Prev. 2015;16(7):732–8.
Moskal A, Martin J-L, Laumon B. Risk factors for injury accidents among moped and motorcycle riders. Accid Anal Prev. 2012;49:5–11.
Vajari MA, Aghabayk K, Sadeghian M, Shiwakoti N. A multinomial logit model of motorcycle crash severity at Australian intersections. J Safety Res. 2020;73:17–24.
Islam M. The effect of motorcyclists’ age on injury severities in single-motorcycle crashes with unobserved heterogeneity. J Safety Res. 2021;77:125–38.
Sohadi RUR, Mackay M, Hills B. Multivariate analysis of motorcycle accidents and the effects of exclusive motorcycle lanes in Malaysia. J Crash Prev Inj Control. 2000;2(1):11–7.
Lemonakis P, Eliou N, Karakasidis T. Investigation of speed and trajectory of motorcycle riders at curved road sections of two-lane rural roads under diverse lighting conditions. J Safety Res. 2021;78:138–45.
Teoh ER, Campbell M. Role of motorcycle type in fatal motorcycle crashes. J Safety Res. 2010;41(6):507–12.
McCartt AT, Blanar L, Teoh ER, Strouse LM. Overview of motorcycling in the United States: a national telephone survey. J Safety Res. 2011;42(3):177–84.
Plasència A, Borrell C, Antó JM. Emergency department and hospital admissions and deaths from traffic injuries in Barcelona, Spain. A one-year population-based study. Accid Anal Prev. 1995;27(4):591–600.
Cirera E, Plasència A, Ferrando J, Seguí-Gómez M. Factors associated with severity and hospital admission of motor-vehicle injury cases in a southern European urban area. Eur J Epidemiol. 2001;17:201–8.
Ferrando J, Plasència A, Orós M, Borrell C, Kraus JF. Impact of a helmet law on two wheel motor vehicle crash mortality in a southern European urban area. Inj Prev. 2000;6(3):184–8.
Ferrando J, Plasència A, MacKenzie E, Orós M, Arribas P, Borrell C. Disabilities resulting from traffic injuries in Barcelona, Spain: 1-year incidence by age, gender and type of user. Accid Anal Prev. 1998;30(6):723–30.
Albalate D, Fernández-Villadangos L. Exploring determinants of urban motorcycle accident severity: the case of Barcelona. XREAP (2009);02.
Albalate D, Fernández-Villadangos L. Motorcycle injury severity in Barcelona: the role of vehicle type and congestion. Traffic Inj Prev. 2010;11(6):623–31.
Pérez K, Santamariña-Rubio E. Do advanced stop lines for motorcycles improve road safety? J Transp Health. 2019;15:100657.
Fagnant DJ, Kockelman KM. Motorcycle use in the United States: crash experiences, safety perspectives, and countermeasures. J Transp Saf Sec. 2015;7(1):20–39.
Open Data BCN. Ajuntament de Barcelona’s open data service. 2021. [Online]. Available: https://opendata-ajuntament.barcelona.cat/en/. (accessed 10 May 2022).
Ospina-Mateus H, Jiménez LAQ, Lopez-Valdes FJ, Garcia SB, Barrero LH, Sana SS. Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists. J Ambient Intell Humaniz Comput. 2021;12:10051–72.
Yu R, Abdel-Aty M. Using hierarchical Bayesian binary probit models to analyze crashinjury severity on high speed facilities with real-time traffic data. Accid Anal Prev. 2014;62:161–7.
Garrido R, Bastos A, de Almeida A, Elvas JP. Prediction of road accident severity using the ordered probit model. Transportation Research Procedia. 2014;3:214–23.
Aiash A, Robusté F. Traffic accident severity analysis in Barcelona using a binary probit and CHAID tree. Int J Inj Contr Saf Promot. 2021;29(2):256–64.
Aidoo EN, Amoh-Gyimah R, Ackaah W. The relationship between driver and passenger’s seatbelt use: a bivariate probit analysis. Int J Inj Contr Saf Promot. 2021;28(2):179–84.
Sivasankaran SK, Rangam H, Balasubramanian V. Investigation of factors contributing to injury severity in single vehicle motorcycle crashes in India. Int J Inj Contr Saf Promot. 2021;28(2):243–54.
Rahman MH, Zafri NM, Akter T, Pervaz S. Identification of factors influencing severity of motorcycle crashes in Dhaka, Bangladesh using binary logistic regression model. Int J Inj Contr Saf Promot. 2021;28(2):141–52.
Salum JH, Kitali AE, Bwire H, Sando T, Alluri P. Severity of motorcycle crashes in Dar es Salaam, Tanzania. Traffic Inj Prev. 2019;20(2):189–95.
Francis F, Moshiro C, Yngve BH. Investigation of road infrastructure and traffic density attributes at high-risk locations for motorcycle-related injuries using multiple correspondence and cluster analysis in urban Tanzania. Int J Inj Contr Saf Promot. 2021;28(4):428–38.
Gazder U, Almalki Y, Alam MS, Arifuzzaman M. The effect of different mobile uses on crash frequency among young drivers: application of statistical models and clustering analysis. Int J Injury Control Saf Promot. 2022;30(1):4–14.
IBM. IBM SPSS modeler 18.0 algorithms guide. IBM Corporation; 2016. [Online]. Available: https://www.public.dhe.ibm.com/software/analytics/spss/documentation/modeler/18.0/en/AlgorithmsGuide.pdf. Accessed 1 Jan 2022.
Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53–65.
Author information
Authors and Affiliations
Contributions
Ahmad Aiash is involved in conceptualization, methodology, software, validation, formal analysis, investigation, writing - original Draft, visualization, and resource’s part. Francesc Robusté is involved in writing - review & editing, supervision, and validation part. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Conflict of interest
No potential conflict of interest was reported by the author(s).
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Aiash, A., Robusté, F. Supervised and unsupervised techniques to analyze risk factors associated with motorcycle crash. Eur J Trauma Emerg Surg (2024). https://doi.org/10.1007/s00068-024-02521-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00068-024-02521-y