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Islam, M.K.; Rahman, M.; , .; Arifuzzaman, M. Sustainable Road Safety in Saudi Arabia. Encyclopedia. Available online: https://encyclopedia.pub/entry/23345 (accessed on 18 April 2024).
Islam MK, Rahman M,  , Arifuzzaman M. Sustainable Road Safety in Saudi Arabia. Encyclopedia. Available at: https://encyclopedia.pub/entry/23345. Accessed April 18, 2024.
Islam, Md Kamrul, Muhammad Rahman,  , Md Arifuzzaman. "Sustainable Road Safety in Saudi Arabia" Encyclopedia, https://encyclopedia.pub/entry/23345 (accessed April 18, 2024).
Islam, M.K., Rahman, M., , ., & Arifuzzaman, M. (2022, May 25). Sustainable Road Safety in Saudi Arabia. In Encyclopedia. https://encyclopedia.pub/entry/23345
Islam, Md Kamrul, et al. "Sustainable Road Safety in Saudi Arabia." Encyclopedia. Web. 25 May, 2022.
Sustainable Road Safety in Saudi Arabia
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Understanding the causes and effects of road accidents is critical for developing road and action plans in a country. The causation hypothesis elucidates how accidents occur and may be applied to accident analysis to more precisely anticipate, prevent, and manage road safety programs. Driving behavior is a critical factor to consider when determining the causes of traffic accidents. Inappropriate driving behaviors are a set of acts taken on the roadway that can result in aberrant conditions that may result in road accidents.

sustainable road safety driver behavior

1. Background

A transportation system cannot be sustainable unless it is safe for humans, and human life is the most precious resource. Road safety not only encompasses the steps taken to lower the risk of traffic-related injuries and deaths, but also encompasses the sensation of being safe while on the road and confidence that the user will not be seriously wounded or killed while on it. Safety is now recognized in worldwide environmental policies as being critical to achieving sustainable development and should be a precondition for mobility, particularly in countries where the number of road fatalities remains high. The goal of a sustainable and safe road traffic system is to eliminate road deaths, serious road injuries, and permanent injuries by systematically lowering the underlying risks of the whole traffic system. Human aspects are the key focus: the traffic system may be realistically altered to ensure optimum safety by investigating the behaviors, skills, and limitations of drivers.
Traffic, accidents, and pollution are three issues that are becoming increasingly prominent in urban areas as both the population and vehicle fleet continue to increase. The most detrimental of the three elements outlined above is an accident in or near a city center. According to the World Health Organization (WHO), over 1.3 million people died in road traffic accidents in 2010, while 20 to 50 million people were injured [1]. Between 1975 and 1998, the number of people killed or injured in road traffic accidents increased by around 44% in Malaysia and by over 200% in Colombia and Botswana. The World Health Organization has also forecasted that road accidents will be the sixth largest cause of mortality and the second leading cause of disability in developing nations by the year 2020 if current trends continue [2]. According to a review of 404 accident reports of 14 different types of accidents, the road environment played a role in approximately 14.5% of all accidents [3]. According to the findings of the study by the WHO [1], 30 deaths per 100,000 people were observed in Saudi Arabia in 2007, and 6358 deaths occurred as a result of road accidents. Additionally, according to the WHO report [4], in the Kingdom of Saudi Arabia, traffic accidents are the greatest cause of injury, fatality, and disability, and the cost of treating those who are injured or killed in road traffic accidents was projected to be SAR 652.5 million [2][3]. Officials in Saudi Arabia have revealed that road accidents occur every minute in the country, and the kingdom is considered to be one of the world’s top countries when it comes to traffic accidents, with approximately 21 deaths per day, ranking it as the second deadliest country in the Middle East [5][6].
There are many factors that cause traffic accidents. Some of these causes are related to road geometry, and some are related to driver behavior. Driving behavior is one of the significant issues when analyzing the reasons of traffic accidents. A report on the town of Mekelle in northern Ethiopia showed that human risk behavior is behind 96% of accidents [7]. A study in an eastern Mediterranean region showed that 86% of drivers engaged in at least one risky driving behavior while driving [8]. Crash-causing risky driving habits include speeding, ignoring red light signals, sudden lane changes, blocking intersections, not using seat belts, and vehicles turning suddenly [9]. If the effects and extent of inappropriate driving attitudes and behaviors on accident severity and type can be identified, it will be helpful in developing suitable road safety policies that would prevent traffic accidents. Saudi Arabia, similar to many other countries throughout the world, has created tactics and scenarios to help mitigate and resolve traffic disasters when they occur. However, despite the deterrent and awareness measures implemented by the Traffic Department and other concerned departments, which have taken it upon themselves to confront this danger, Saudi Arabia continues to experience a significant problem concerning traffic accidents; therefore, it is essential to analyze the effects of driving behavior on road accidents in the Saudi Arabian context.

2. Driving Behaviors

It is essential to understand the causes and effects of road accidents to adopt appropriate safety strategies and action plans. Some of these causes are reflections of the inappropriate attitudes or behaviors of drivers, such as speeding, suddenly changing lanes, and tailgating [10]. Several studies have attempted to analyze and categorize types of driving behavior. Some researchers divided driving behavior in two broad categories: “cautious” and “aggressive” driving [11]. A driver who does not accelerate, can initiate the breaks of their vehicle unexpectedly, and maintain proper speed is considered to be careful and cautious driver [12]. The Department of Transportation of Pennsylvania described aggressive driving as “the operation of a motor vehicle in a manner that endangers or is likely to endanger persons or property” [10]. Eboli et al. [13] analyzed average speed as well as the 50th and 85th percentile speeds of a road segment of a two-lane Italian rural road and classified driving behavior into three categories: (1) safe, (2) unsafe, and (3) safe but potentially dangerous. In another study conducted by Taubman-Ben-Ari et al. [14], it was suggested that driving behavior be categorized into four groups: (1) careless and reckless driving: this driving behavior is characterized by high speed, illegal maneuvers, and racing to seek thrills when driving; (2) anxious driving: related to ineffective relaxation activities with feelings of tension and alertness when driving; (3) hostile and angry driving: includes drivers with antagonistic attitudes and annoyance as well as anger, and these emotions are expressed by acts such as flashing their headlights at others; and (4) careful and patient behavior: expressed via a good attitude, planning for unforeseen situations in advance, and perfectly following traffic rules and regulations. Yasir Ali et al. [15][16][17] conducted simulation studies using the CARRS-Q Advanced Driving Simulator to evaluate various critical driving behaviors across a number of normal driving activities, including car-following, interactions with traffic lights, pedestrian crossings, and lane changes. Their findings implied that drivers who communicate well had a longer time-to-collision when following another vehicle, a longer time-to-collision when approaching a pedestrian, lower deceleration to prevent a crash when changing lanes, and a reduced proclivity to run yellow lights. In general, drivers in a networked environment make more informed (and hence safer) decisions. Using the random parameters Bayesian least absolute shrinkage and selection operator (LASSO) modeling approach, Yue Zhou et al. [18] studied the operational aspects affecting aggressive taxi speeders. Taxi GPS trajectory data in Chengdu, China, was used to extract taxi speeding habits and other operational parameters. The fuzzy C-means clustering approach was used to group taxi speeders into three cohorts based on their hourly speeding frequency and average speeding severity: restrained speeders (RS), moderate speeders (MS), and belligerent speeders (BS). MS and BS are regarded as aggressive taxi speeders compared to RS. With RS as the reference category, several binary logistic models have been built. The Bayesian binary logistic LASSO model with random parameters has been found to capture unobserved heterogeneity and to combat multicollinearity. Mohammad Jalayer et al. [19] applied a random parameter-ordered probit model to identify the attributes of wrong-way driving (WWD) crashes and injuries using 15 years of crash data from the states of Alabama and Illinois. According to the obtained results, factors such as driver age, driver condition, roadway surface conditions, and lighting conditions significantly contribute to the injury severity in WWD crashes. Zhengwu Wang et al. [20] combined a classification tree with a logistic regression model and studied the underlying risk factors for severe injuries in different categories of e-bike users. Three years of e-bike crashes in Hunan province were analyzed by considering risk factors such as rider attributes, opponent vehicle and driver attributes, incorrect riding and driving behaviors, and road and environmental characteristics. Below, Table 1 summarizes the literature related to driving behaviors.
Table 1. References on driving behavior.
References Driving Behaviors Comments
P. McTish and S. Park (2016) [10] Aggressive driving in Pennsylvania’s Delaware Valley region in the USA. Conducted statistical analysis among aggressive crash features (e.g., type, severity level), roadway features (operation and geometric), and driver behavior.
C. Wang et al. (2014) [11] Different driving styles, vision sensors, radar, GPS, and vehicle CAN bus data capture systems were installed in a small passenger car, and a real road driving test was carried out. Used the fuzzy evaluation method to categorize driving behavior.
G. Miller and O. Taubman-Ben-Ari, (2010) [12] Studied the risky behavior of young novice drivers. Analyzed the contribution of parental driving styles and personal characteristics on the behavior of young drivers.
L. Eboli et al. (2017) [13] Classified driving behavior into three categories: (1) safe, (2) unsafe, and (3) safe, with potentially dangerous behavior based on speed analysis. Conducted a survey to collect experimental speeds in a real situation in an Italian rural two-lane road.
O. Taubman-Ben-Ari et al. (2004) [14] Developed a self-report scale assessing four broad domains of driving styles—the multidimensional driving style inventory (MDSI). Applied factor analysis that revealed eight main factors, with each one representing a specific driving style—dissociative, anxious, risky, angry, high-velocity, distress reduction, patient, and careful.
Y. Ali et al. (2020) [15][16][17] Evaluated various critical driving behaviors across a number of normal driving activities, including car following, interactions with traffic lights, pedestrian crossings, and lane changes. Conducted simulation studies using the CARRS-Q Advanced Driving Simulator.
Yue Zhou et al. (2021) [18] Operational aspects affecting aggressive taxi speeders. Applied the random parameter Bayesian least absolute shrinkage and selection operator (LASSO) modeling approach on taxi GPS trajectory data from Chengdu, China.
Mohammad Jalayer et al. (2018) [19] Identified the attributes of wrong-way driving (WWD) crashes and injuries. Applied random parameter-ordered probit model using 15 years of crash data from Alabama and Illinois, USA.
Zhengwu Wang et al. (2021) [20] Studied the underlying risk factors for severe injuries in different categories of e-bike users. Combined a classification tree with a logistic regression model using e-bike crash data from Hunan province, China.
The causality hypothesis can be applied to accident analysis to anticipate, prevent, and manage road safety initiatives on a more precise level in order to explain how driver behavior leads to accidents. Researchers from all around the world have undertaken numerous studies on the causality of road accidents through the use of a variety of data sets, locations, sample sizes, and factors as well as analytical models to determine the causes of accidents. As an example, references [21][22][23] provided aggregate models in which an accident frequency analysis and χ2 Test [21][24] were devised as key tools. Lord et al. [25] proposed a non-negative, discrete disaggregated model in which they assumed from experience that the rate of accidents follows a Poisson distribution and applied a Poisson distribution to observe the influence of risk factors on the rate of accidents. Researchers [26][27][28] have extensively employed the negative binomial regression model for road safety analysis; nevertheless, it has been discovered that the real-world scenarios do not always correspond to the assumption of equal mean and variance required for the Poisson distribution, as stated in different studies. Furthermore, when applying the Poisson regression model and negative binomial regression to longitudinal data samples, there is a substantial danger of obtaining a skewed estimate, if not completely wrong results. These models are based on invariant parameters that do not account for temporal variability. There are some recent modelling techniques that analyze traffic accidents in real time and that update the model parameters recursively and react to abrupt trend changes [29][30][31].
Researchers have gradually advanced from developing aggregated models to developing complex disaggregated models; aggregate modeling entails straightforward descriptive analysis, whereas disaggregate modeling entails complex multivariate analysis. However, there is a persistent lack of understanding of the circumstances, meaning that accident-causing elements have not been completely explored. The existing literature is deficient in that the majority of analyses and models are isolated, single factor, or case-specific and fail to present, correlate, and explain the underlying processes and complex multidimensional relationships between accident causes, occurrences, and consequences. Although some scholars have attempted to address these issues [32][33][34][35][36][37], the theoretical and empirical foundations for accident mechanisms have not been established systematically.

References

  1. WHO. Eastern Mediterranean Status Report on Road Safety: Call for Action. 94p; 30 cm. 2010. Available online: https://apps.who.int/iris/handle/10665/119908 (accessed on 21 May 2022).
  2. WHO. Global launch: Decade of Action for Road Safety 2011–2020, World Health Organization. 2011. Available online: https://apps.who.int/iris/bitstream/handle/10665/82578/WHO_NMH_VIP11.08_eng.pdf (accessed on 19 July 2021).
  3. Bendak, S. An in-depth analysis of red light crossing problem in Saudi Arabia. Adv. Transp. Stud. 2011, 25, 67–74.
  4. WHO. Country Cooperation Strategy for WHO and Saudi, 2012–2016. 2012. Available online: https://apps.who.int/iris/bitstream/handle/10665/113227/CCS_Saudia_2013_EN_14914.pdf (accessed on 19 July 2021).
  5. Arab News. Saudi Arabia 2nd in ME in Accident Fatalities. 2015. Available online: https://www.arabnews.com/saudi-arabia/news/827366 (accessed on 19 July 2021).
  6. Arab News. Traffic Accidents Occur Every Minute in KSA. 2015. Available online: https://www.arabnews.com/saudi-arabia/news/717596 (accessed on 19 July 2021).
  7. Hassen, A.; Godesso, A.; Abebe, L.; Girma, E. Risky driving behaviors for road traffic accident among drivers in Mekele city, Northern Ethiopia. BMC Res. Notes 2011, 4, 535.
  8. Khalil, I. Transport injuries and deaths in the Eastern Mediterranean Region: Findings from the Global Burden of Disease 2015 Study. Int. J. Public Health 2018, 63, 187–198.
  9. Goniewicz, K.; Goniewicz, M.; Pawłowski, W.; Fiedor, P. Road accident rates: Strategies and programmes for improving road traffic safety. Eur. J. Trauma Emerg. Surg. 2016, 42, 433–438.
  10. McTish, P.; Park, S. Exploring aggressive driving behavior in Pennsylvania’s Delaware Valley region. Procedia Eng. 2016, 145, 836–843.
  11. Wang, C.; Fu, R.; Peng, J.; Mao, J. Driving style classification method for lane change warning system. J. Transp. Sys. Eng. Inf. Technol. 2014, 14, 187–193.
  12. Miller, G.; Taubman-Ben-Ari, O. Driving styles among young novice drivers—The contribution of parental driving styles and personal characteristics. Accid. Anal. Prev. 2010, 42, 558–570.
  13. Eboli, L.; Guido, G.; Mazzulla, G.; Pungillo, G.; Pungillo, R. Investigating car users’ driving behaviour through speed analysis. Promet Traffic Transp. 2017, 29, 193–202.
  14. Taubman-Ben-Ari, O.; Mikulincer, M.; Gillath, O. The multidimensional driving style inventory—Scale construct and validation. Accid. Anal. Prev. 2004, 36, 323–332.
  15. Ali, Y.; Zheng, Z.; Haque, M.; Yildirimoglu, M.; Washington, S. Understanding the discretionary lane-changing behaviour in the connected environment. Accid. Anal. Prev. 2020, 137, 105463.
  16. Ali, Y.; Sharma, A.; Haque, M.; Zheng, Z.; Saifuzzaman, M. The impact of the connected environment on driving behavior and safety: A driving simulator study. Accid. Anal. Prev. 2020, 144, 105643.
  17. Ali, Y.; Haque, S.M.M.; Zheng, Z.; Washington, S.; Haque, M.; Yildirimoglu, M. A hazard-based duration model to quantify the impact of connected driving environment on safety during mandatory lane-changing. Transp. Res. Part C Emerg. Technol. 2019, 106, 113–131.
  18. Zhou, Y.; Jiang, X.; Fu, C.; Liu, H. Operational factor analysis of the aggressive taxi speeders using random parameters Bayesian LASSO modeling approach. Accid. Anal. Prev. 2021, 157, 106183.
  19. Jalayer, M.; Shabanpour, R.; Rouholamin, M.; Golshani, N.; Zhou, H. Wrong-way driving crashes: A random-parameters ordered probit analysis of injury severity. Accid. Anal. Prev. 2018, 117, 128–135.
  20. Wang, J.; Huang, S.; Wang, J.; Sulaj, D.; Hao, W.; Kuang, A. Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model. J. Safety Res. 2021, 76, 176–183.
  21. Al-Ghamdi, A.S. Pedestrian–vehicle crashes and analytical techniques for stratified contingency tables. Accid. Anal. Prev. 2002, 34, 205–214.
  22. Stone, M.; Broughton, J. Getting off your bike: Cycling accidents in Great Britain in 1990–1999. Accid. Anal. Prev. 2003, 35, 549–556.
  23. Lefler, D.E.; Gabler, H.C. The fatality and injury risk of light truck impacts with pedestrians in the United States. Accid. Anal. Prev. 2004, 36, 295–304.
  24. Holubowycz, O.T. Age, sex, and blood alcohol concentration of killed and injured pedestrians. Accid. Anal. Prev. 1995, 27, 417–422.
  25. Lord, D.; Washington, S.P.; Ivan, J.N. Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: Balancing statistical fit and theory. Accid. Anal. Prev. 2005, 37, 35–46.
  26. Ng, K.S.; Hung, W.T.; Wong, W.G. An algorithm for assessing the risk of traffic accident. J. Safety Res. 2002, 33, 387–410.
  27. Martin, J.L. Relationship between crash rate and hourly traffic flow on interurban motorways. Accid. Anal. Prev. 2002, 34, 619–629.
  28. Poch, M.; Mannering, F. Negative Binomial Analysis of Intersection-Accident Frequencies. J. Transp. Eng. 1996, 122, 105–113.
  29. Yang, K.; Wang, X.; Yu, R. A Bayesian dynamic updating approach for urban expressway real-time crash risk evaluation. Transp. Res. Part C Emerg. Technol. 2018, 96, 192–207.
  30. Fu, C.; Sayed, T. Bayesian dynamic extreme value modeling for conflict-based real-time safety analysis. Anal. Methods Accid. Res. 2022, 34, 100204.
  31. Fei, X.; Lu, C.; Liu, K. A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction. Transp. Res. Part C Emerg. Technol. 2011, 19, 1306–1318.
  32. Chang, L.Y.; Chen, W.C. Data mining of tree-based models to analyze freeway accident frequency. J. Safety Res. 2005, 36, 365–375.
  33. Kuhnert, P.M.; Do, K.A.; McClure, R. Combining non-parametric models with logistic regression: An application to motor vehicle injury data. Comput. Stat. Data Anal. 2000, 34, 371–386.
  34. Abellán, J.; López, G.; de Oña, J. Analysis of traffic accident severity using Decision Rules via Decision Trees. Expert Syst. Appl. 2013, 40, 6047–6054.
  35. De Oña, J.; Mujalli, R.O.; Calvo, F.J. Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accid. Anal. Prev. 2011, 43, 402–411.
  36. Heydari, S.; Miranda-Moreno, L.F.; Lord, D.; Fu, L. Bayesian methodology to estimate and update safety performance functions under limited data conditions: A sensitivity analysis. Accid. Anal. Prev. 2014, 64, 41–51.
  37. Mbakwe, A.C.; Saka, A.A.; Choi, K.; Lee, Y.J. Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network. Accid. Anal. Prev. 2016, 93, 135–146.
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