Navigating Road Traffic Accidents in Jordan: A Retrospective Exploration of the Health and Economic Impacts.

Background Road traffic accidents (RTAs) are considered a major public health threat. The causes of RTAs are multifactorial, comprising both human and non-human factors. RTAs may result not only in detrimental health consequences but also in serious economic burdens. This study aimed to provide a retrospective overview of the health and economic impacts of RTAs in Jordan during the period 2013-2021, including the implemented multisectoral mitigation strategies. Methodology This study presents a retrospective chart review of the traffic reports published by the Public Security Directorate (PSD) in Jordan during the period 2013-2021. The examined variables were the total number of road accidents involving human injuries per year, average road accidents per day, annual deaths, average deaths per day, deaths per 100,000 population, annual number of injuries, average number of injuries per day, severity of human injury, number of registered vehicles, and the annual financial costs. Data were extracted from PSD reports and presented descriptively. Results The number of registered vehicles has risen from 1,263,754 in 2013 to 1,795,215 in 2021 with a 42.1% increase. This was also associated with an increase in the average RTAs per day from 28.0 in 2013 to 30.8 in 2021. The total number of RTAs that involved human injuries during the period 2013-2021 fluctuated with 8,451 in 2020 (lowest) and 11,241 in 2021 (highest). In the same period, annual deaths caused by RTAs peaked in 2013 with 768 deaths (highest) compared to 461 in 2020 (lowest). In the five years from 2017 to 2021, the total financial cost of RTAs in Jordan was nearly 1.561 billion Jordanian Dinars (~2.2 billion U.S. dollars) compared to 1.363 billion Jordanian Dinars (~1.92 billion U.S. dollars) in the five years from 2012 to 2016. Conclusions Targeting human and road infrastructure factors through effective government-society partnerships is crucial in mitigating the health and economic burden of RTAs in Jordan. More strict enforcement of traffic laws, along with incremental increases in the penalties for dangerous traffic violations, and regular road safety campaigns are expected to provide more control over the human factors of RTAs. Further research is needed to evaluate the impact of the recently amended traffic law on the trends of RTAs in the country.

Accelerated failure time modeling of in-lane street hawkers' lane entry and exit behaviors at signalized intersections.

In-lane street hawking is the intermittent entry of signalized intersections by traders to sell groceries to drivers and passengers. Studies have shown that hawkers get exposed to traffic injuries but the lack of quantitative analysis of their lane entry and exit behaviors in signalized intersections makes it difficult to improve traffic safety. This study analyzes the significant predictors of in-lane street hawkers' (1) lane entry within 30 s after the red signal illumination, (2) lane exit within 30 s after the green signal illumination, and (3) probability of getting injuries during the green signal time. Drone-based trajectory data were collected from a selected signalized intersection in Accra, Ghana. A Weibull accelerated failure time duration model incorporating Gamma frailty was used to evaluate hawkers' behaviors. Overall, the majority of hawkers exhibited red-light running behaviors exposing them to traffic injuries. An increase in traffic speed, especially beyond 20 km/h, exposed hawkers to injury risks significantly. Notably, hawkers' lane entry decreased significantly as the traffic speed increased. Their lane exit duration was significantly predicted by the queue lengths and traffic volumes. Accordingly, safety practitioners can enhance traffic regulation and control methods in addition to pro-poor social interventions to demotivate hawking at signalized intersections.

Understanding key contributing factors on the severity of traffic violations by elderly drivers: a hybrid approach of latent class analysis and XGBoost based SHAP.

Traffic violation is one of the leading causes of traffic crashes. In the context of global aging, it is important to study traffic violations by elderly drivers for improving traffic safety in preparation for a worldwide aging population. In this study, a hybrid approach of Latent Class Analysis (LCA) and XGBoost based SHAP is proposed to identify hidden clusters and to understand the key contributing factors on the severity of traffic violations by elderly drivers, based on the police-reported traffic violation dataset of Beijing (China). First, LCA is applied to segment the dataset into several latent homogeneous clusters, then XGBoost based SHAP is established on each cluster to identify feature contributions and the interaction effects of the key contributing factors on the severity of traffic violations by elderly drivers. Two comparison groups were set up to analyze factors, which are responsible for the different severities of traffic violations. The results show that elderly drivers can be classified into four groups by age, urban or not, license, and season; factors such as less annual number of traffic violations, national & provincial highway, night and winter are key contributing factors for higher severity of traffic violations, which are consistent with common cognition; key contributing factors for all clusters are similar but not identical, for example, more annual number of traffic violations contribute to more severe violation for all clusters except for Cluster 2; some factors which are not key contributing factors may affect the severity of traffic violations when they are combined with other factors, for example, the combination of lower annual number of traffic violations and county & township highway contributes to more severe violation for Cluster 1. These findings can help government to formulate targeted countermeasures to decrease the severity of traffic violations by specific elderly groups and improve road service for the driving population.

Identification and screening of key traffic violations: based on the perspective of expressing driver's accident risk.

Drawing on the core idea of Propensity Score Matching, this study proposes a new concept named Historical Traffic Violation Propensity to describe the driver's historical traffic violations, and combines the new concept with an improved mutual information-based feature selection algorithm to construct a method for screening key traffic violations from the perspective of expressing driver's accident risk. The validation analysis based on the real data collected in Shenzhen demonstrated that drivers' state of Historical Traffic Violation Propensity on 19 key traffic violations screened have a stronger predictive ability of their subsequent accidents compared to the level in existing research. The positive state of Historical Traffic Violation Propensity on 'Drinking', 'Parking in dangerous areas', 'Wrong use of turn lights', 'Violating prohibited and restricted traffic regulations', and 'Disobeying prohibition sign' will increase the probability of a driver's subsequent accident by more than 1.7 times. The research provides directions to more efficiently and accurately capture the driver's accident risk through historical traffic violations, which is valuable for identifying high-risk drivers as well as the key psychological or physical risk factors that manifest in daily driving activities and lead to subsequent accidents.

Traffic violations analysis: Identifying risky areas and common violations.

Road traffic accidents caused by traffic violations are a major public health issue that results in loss of lives and economic costs. Therefore, it is important to prioritize road safety measures that reduce the incidence and severity of accidents. In this study, we suggest an incremental road safety strategy that identifies high-risk areas and common traffic violations in order to prioritize further enforcement. In fact, by analyzing data on traffic violations in different districts and comparing them to the overall average using the Kolmogorov-Smirnov (KS) test, risky areas are identified and the most common violations are detected. We performed a comparison between several types of clustering optimizations to spot clusters to be enforced in order to reduce violations. Our results indicate that some Districts have a higher risk of traffic violations than others do, and some violations (Speeding, Registration, License, Belt, Influence, Phone, etc.) are more common than others are. We also find that k-means clustering provides the best results for identifying clusters of violations records and optimizing enforcement strategies. Our findings can be adopted by law enforcement agencies to focus on high-risk areas and target the most common violations in order to optimize their resources and improve road safety.

Examining enforcement coverage for speeding and red-light offenses across various populations and driver characteristics.

Over the years empirical evidence has shown that traffic enforcement reduces traffic violations, crashes, and casualties. However, less attention has been paid to enforcement coverage across different populations and driver characteristics. The current study develops and explores a method for estimating police enforcement coverage, by comparing the share of drivers across several characteristics who received tickets from automatic speed and red-light cameras - as an objective estimate of offenses committed - to the share of drivers who received tickets through manual police enforcement. Using data from all speeding and red-light tickets issued to Israelis over a period of one and a half years, we found under-enforcement by police officers for female drivers, two-wheeled vehicle drivers (for speeding), and drivers with previous tickets. We found over-enforcement for younger drivers, truck drivers, and two-wheeled vehicle drivers (for red-light offenses). The findings suggest that the method developed in the research is able to identify groups of drivers who are over- or under-enforced. Police authorities can use this information to create evidence-based enforcement policies.

No mercy for victims: Exploring the link between dark personality traits, aggressive video game behavior, and severe traffic violations.

This study investigates the relationship between dark personality traits, aggressive behavior in violent video games, and severe traffic violations among 200 driving offenders from Tehran, Iran, participating in a rehabilitation program. Participants engaged in a computerized shooting decision task, where their tendency to shoot unarmed targets (innocent victims), compared to armed targets (criminals), was used as an indicator of aggressive behavior toward innocent victims. Additionally, they completed self-report measures of narcissism, Machiavellianism, psychopathy, and sadism to evaluate the impact of Dark Tetrad personality traits on their behavior. Bivariate analyses revealed associations between Dark Tetrad personality traits and aggressive behavior in the video game with serious traffic offenses. Multivariate analyses identified Machiavellianism, sadism, and aggressive behavior in video games as significant predictors of severe traffic offenses. The results suggest that dark personality traits and aggressive behavior in video games may aid in better identifying road traffic offenders with the most severe violations. Potential implications for preventing repeated traffic offenses by tailoring rehabilitation programs are discussed.

Public health crisis and risky road behaviors.

This study investigates how exposure to riskier environments influences risky road behaviors, using the COVID-19 pandemic as a natural experiment. Utilizing administrative individual traffic violation records from Taipei, where neither mandatory lockdown nor mobility restrictions were imposed, we find that pandemic-induced risk decreased speeding violations and that the effect was transitory. However, no significant changes were observed concerning violations with a minimal risk of casualties, such as illegal parking. These findings suggest that experiencing a higher level of life-threatening risk discourages risky behaviors concerning human life but has little spillover effect on those concerning only financial costs.

Taxi drivers' traffic violations detection using random forest algorithm: A case study in China.

OBJECTIVE: To effectively explore the impacts of several key factors on taxi drivers' traffic violations and provide traffic management departments with scientific decisions to reduce traffic fatalities and injuries. METHODS: 43,458 electronic enforcement data about taxi drivers' traffic violations in Nanchang City, Jiangxi Province, China, from July 1, 2020, to June 30, 2021, were utilized to explore the characteristics of traffic violations. A random forest algorithm was used to predict the severity of taxi drivers' traffic violations and 11 factors affecting traffic violations, including time, road conditions, environment, and taxi companies were analyzed using the Shapley Additionality Explanation (SHAP) framework. RESULTS: Firstly, the ensemble method Balanced Bagging Classifier (BBC) was applied to balance the dataset. The results showed that the imbalance ratio (IR) of the original imbalanced dataset reduced from 6.61% to 2.60%. Moreover, a prediction model for the severity of taxi drivers' traffic violations was established by using the Random Forest, and the results showed that accuracy, m_F1, m_G-mean, m_AUC, and m_AP obtained 0.877, 0.849, 0.599, 0.976, and 0.957, respectively. Compared with the algorithms of Decision Tree, XG Boost, Ada Boost, and Neural Network, the performance measures of the prediction model based on Random Forest were the best. Finally, the SHAP framework was used to improve the interpretability of the model and identify important factors affecting taxi drivers' traffic violations. The results showed that functional districts, location of the violation, and road grade were found to have a high impact on the probability of traffic violations; their mean SHAP values were 0.39, 0.36, and 0.26, respectively. CONCLUSIONS: Findings of this paper may help to discover the relationship between the influencing factors and the severity of traffic violations, and provide a theoretical basis for reducing the traffic violations of taxi drivers and improving the road safety management.

Reducing traffic violations in the online food delivery industry-A case study in Xi'an City, China.

Online food delivery (OFD) is one of the top industries in the Online-to-offline (O2O) commerce sector. Deliverymen need to complete a large number of delivery orders in limited default time every day, which cause high working stress to them. Therefore, a high level of traffic violations and crashes by deliverymen and corresponding negative impact on public safety are observed. To reduce traffic violations by deliverymen and resulting crashes, a hierarchical online food delivery framework is proposed, which is based on data from questionnaire surveys conducted in Xi'an City, China. The study includes the analysis of the root cause correlated with traffic violations during online food delivery as part of an empirical study on the priority delivery fee by applying a conditional price sensitivity measurement (PSM) model. The feasibility and rationality of the framework are further investigated by using cross analysis of urban dwellers' occupation, income, and commuting cost. The results identify that, through rationally shunting the demand of online food delivery, prolonging the default delivery duration, and providing diversified delivery services, the proposed hierarchical online food delivery mechanism is able to relieve the stress of deliverymen during peak hours of food requests. This reduces the willingness of deliverymen to engage in traffic violations, and other risky behaviors during food delivery trips. All of which facilitate high-quality and timely online food delivery service while enabling improved safety of deliverymen and others as part of enhanced public safety and health.

Association between psychological symptoms and illegal driving behaviors in a sample of Chinese private car drivers.

Background: Findings on the associations between psychological symptoms and driving behaviors in private car drivers are inadequate. Method: The study consisted of 3,115 private car drivers in Yulin, China. The measurements included socio-demographic data, traffic violations, accidents, and Symptom Checklist-90 (SCL-90). In addition, an ordered logistic regression model was employed to examine the association between each psychological symptom and risky driving behaviors. Results: The overall prevalence rate of any self-reported psychological symptom was 10.24%, with 9.22% for males and 11.49% for females. Among them, obsessive-compulsive, interpersonal sensitivity, additional items, hostility, and depression were the five most common psychological symptoms, with prevalence rates of 7.90, 6.29, 6.00, 5.91, and 5.62%, respectively. Any psychological symptom factor was associated with a higher risk of traffic violations and accidents. However, the intensity of the correlations varied, with obsessive-compulsive symptoms the strongest in general traffic violations and anxiety symptoms in traffic accidents. All psychological symptoms except phobic anxiety and paranoid ideation contributed to a higher risk of failing the driver's license test. Conclusions: The prevalence rate of psychological symptoms was high in private car drivers. This study calls for an urgent need to establish a pilot tertiary prevention strategy to reduce risky driving behaviors through psychological symptom screening and interventions among private car drivers.

The guilty, the unlucky, or the unaware? Assessing self-reported behavioral contributors and attributions on pedestrian crashes through structural equation modeling and mixed methods.

INTRODUCTION: Recent literature suggests that the causation of pedestrians' crashes and the contribution of safety-related behaviors within them may substantially differ compared to other road users. This study aimed to test the effect of individual factors and safety-related road behaviors on the self-reported walking crashes suffered by pedestrians and, complementarily, to analyze the causes that pedestrians attributed to the crashes they suffered as pedestrians during the previous five years. METHOD: For this cross-sectional research performed in Spain, data from a nationwide sample of 2,499 pedestrians from the 17 regions of the country were collected. Participants had a mean age of 31 years. They responded to a questionnaire on demographics, safety-related walking behaviors, and self-reported pedestrian crashes and the causes attributed to them. RESULTS: Utilizing Structural Equation Models (SEM), it was found that self-reported walking crashes can be predicted through unintentional risky behaviors (errors). However, violations and positive behaviors remain non-significant predictors, allowing to hypothesize that they might, rather, play a key role in the pedestrian's involvement in pre-crash scenarios (critical situations preceding crashes). Also, categorical analyses allowed to determine that the causes that pedestrians attributed to the walking crashes they had suffered were principally their own errors (44.6%), rather than their own traffic violations (8.5%). Nevertheless, this trend is inverse when they believe the responsibility of the crash weighs on the driver. That is to say, they usually attribute the crash to their traffic violations rather than errors. However, many biases could help explain these attributional findings. PRACTICAL APPLICATIONS: The results of this study highlight key differences in behavioral features and crash predictors among pedestrians, with potentially relevant applications in the study and improvement of walking safety from behavioral-based approaches.

Traffic costs of air pollution: the effect of PM2.5 on traffic violation.

Although emerging studies have investigated the effect of air pollution on traffic crashes, it is unclear to scholars whether air pollution affects another road safety problem-traffic violations. To address this gap, the current paper constructs a data set from 1,390,221 traffic violation records of 640,971 drivers from the Wuhan Traffic Management Bureau between January 2018 and December 2018. An ordered logistic regression was conducted to verify our hypotheses. The result shows that PM2.5 has no overall impact on the severity of traffic violations, but each 1% increase in the daily concentration of PM2.5 leads to a 1.02-fold increase in the odds of serious inexperience-related violations and a 0.99-fold decrease in the odds of serious overconfidence-related violations. This effect is the strongest in PM2.5, followed by NO2, and has not been observed in CO and O3. In addition, robustness tests indicate that the relationship between air pollution and traffic violations is consistent among the different subsets (e.g., clear weather, no rain and snow, and good visibility). We also provide valuable practical advice for drivers and traffic authorities.

Theory of planned behaviour, psychological stressors and intention to avoid violating traffic rules: A Multi-Level modelling analysis.

This study applies a multilevel model approach to test the predictive effect of the theory of planned behaviour (TPB) plus moral norm, past behaviour and crash history to account for intentions to avoid traffic violations within the context of commuting to or from work. This study also extended the theory by adding psychological stressors of perceived stress and work-family conflict. In this study, we systematically tested the direct and mediated models. A sample of Malaysian drivers (N = 482; 44.6% were men and 55.4% women), with the average age of 36.7 years (SD = 10.0) was surveyed. The self-report questionnaire contained multiple observations nested within individual drivers, with respect to 3 different driving violations. As predicted, multi-level modelling showed that within-person predictor variables of all the TPB components emerged as independent predictors, with injunctive norm being the most predictive variable, followed by self-efficacy and cognitive attitude. Intention was also predicted by moral norms and crash history. Between-person variables, gender, commuting hours and work-family conflict also had direct influences on intention (i.e., men and those with longer commuting hours and higher work-family conflict reported lower intentions). Substantial support for the mediation model was found, confirming that stressors indirectly influence intentions through effects on the social cognitive components. The implications of the linkage between the social cognition and stressors for developing potential broader interventions focusing on multiple violation behaviours and designing appropriate safety policies to reduce commuting crashes are discussed.

Understanding Traffic Accidents among Young Drivers in Qatar.

Young drivers are generally associated with risky driving behaviors that can lead to crash involvement. Many self-report measurement scales are used to assess such risky behaviors. This study is aimed to understand the risky driving behaviors of young adults in Qatar and how such behaviors are associated with crash involvement. This was achieved through the usage of validated self-report measurement scales adopted for the Arabic context. A nationwide cross-sectional and exploratory study was conducted in Qatar from January to April 2021. Due to the Covid-19 pandemic, the survey was conducted online. Therefore, respondents were selected conveniently. Hence, the study adopted a non-probability sampling method in which convenience and snowball sampling were used. A total of 253 completed questionnaires were received, of which 57.3% were female, and 42.7% were male. Approximately 55.8% of these young drivers were involved in traffic accidents after obtaining their driving license. On average, most young drivers do have some risky driving behavior accompanied by a low tendency to violate traffic laws, and their driving style is not significantly controlled by their personality on the road. The older young drivers are more involved in traffic accidents than the younger drivers, i.e., around 1.5 times more likely. Moreover, a young male driver is 3.2 times less likely to be involved in traffic accidents than a female driver. In addition, males are only 0.309 times as likely as females to be involved in an accident and have approximately a 70% lower likelihood of having an accident versus females. The analysis is complemented with the association between young drivers' demographic background and psychosocial-behavioral parameters (linking risky driving behavior, personality, and obligation effects on crash involvement). Some interventions are required to improve driving behavior, such as driving apps that are able to monitor and provide corrective feedback.

Steering clear: Traffic violations among emerging adults who engage in habitual or casual cannabis use.

While some research has shown that cannabis use can impair driving ability, evidence to the degree and impact of impairment are lacking. This study examined the association between habitual or casual cannabis use and past-year traffic violations among emerging adults (EAs). Data come from the 2012 Canadian Community Health Survey-Mental Health. Respondents (n = 5630) were categorized as: early (15-19 y), middle (20-24 y), and late (25-29 y) EAs. Traffic violations were measured using self-report and cannabis use was measured using the WHO Composite International Diagnostic Interview. The prevalence of traffic violations was higher for males (19.2 %) vs females (9.9 %) and middle (16.2 %) and late (19.4 %) EAs vs early (8.8 %) EAs. The odds of reporting traffic violations were higher for EAs who engaged in habitual [OR = 1.77 (1.17-2.67)] or casual [OR = 1.79 (1.27-2.51)] cannabis use when compared to non-users. Age moderated the association such that higher odds of traffic violations were reported in early EAs who were casual cannabis users and middle EAs who were habitual or casual cannabis users when compared to non-users. Use of other drugs was also a moderator-in the absence vs. presence of other drug use, odds of traffic violations were higher in those who engaged in either habitual or casual use of cannabis. When accounting for the moderating effects of age and drug use, habitual and casual cannabis use resulted in increased odds of a traffic violation. Future research is warranted to explore the robustness of our findings.

Can the effectiveness of driver education be sustained? Effects of driving breaks on novice drivers' traffic violations.

Prior studies have shown that driver education can reduce traffic violations. However, few studies have examined how driving break between driver education and owning a car influences novice drivers' traffic violations. The main objective of this study is to examine the association between driving break and traffic violations. Data from 356,786 drivers with a total of 978,855 violations during their first year of driving were extracted from the Wuhan Traffic Management Bureau. Specifically, we focused on three outcome measures: time length of first traffic violation, severity of first traffic violation, and number of traffic violations in the first year of driving. The results indicated that driving break accelerated the occurrence of the first traffic violation but reduced its severity. The results also showed that driving break was significantly related to an increase in traffic violations during the first year of driving. The detrimental effects of driving break on the time length of first traffic violation and the number of traffic violations in the first year of driving were attenuated in older age groups. The inhibitory effect of driving break on serious violations was stronger in older age groups. The findings support that the effectiveness of driver education will fade over time if one does not consolidate the learned knowledge and skills through practice.

The political cycle of road traffic accidents.

Road traffic accidents mean lost productivity and medical expenditures. We explain trends in traffic accidents as a function of the political cycle using municipal data from Italy. We show that during municipal election years, the accident rate increases by 1.5%, with a 2% increase in the injury rate but no effect on the fatality rate. The effects are stronger in the quarter prior to the election quarter, when the electoral campaign is at its zenith, and in the second quarter after the election for the new elected mayor. We show that this is the result of a decrease in tickets for traffic violations (rate and revenues) during election years. Our results are robustly driven by the municipal political cycle defined in different ways, and their magnitude and direction are not explained by the spillover effects between municipalities. Proximity to a national police station reduces the impact of local elections on injury rates.

Investigating influence factors of traffic violation using multinomial logit method.

Deaths and injuries resulted from road traffic crashes remain a serious problem globally, and current trends suggest that this will continue to be the case in the foreseeable future mainly in developing countries. Among diverse cause of traffic safety challenges, traffic violation has been considered as one of the noticeable contributing factors. The main aim of the study is to identify and evaluate the major traffic violation with related risk factors using multinomial logit model. Traffic violation data of Luzhou were collected from Sichuan Province Public Security Department, China. The study result revealed six major traffic violations, including traffic light violation, illegal parking, wrong-way driving, speeding, and NOT wearing a seat belt. Urban roads classified with congested driving and severe weather conditions were the major risk factors. Among different vehicle types and use, those small car/automobile categories with private purpose use exhibit statistically significant association (p-value < 0.05) with the aforementioned traffic violations. Taking into consideration these risky contributing factors during the development of traffic regulations and enforcement will help to reduce traffic violations and create a smooth/healthy driving condition with improved traffic safety and will also increase the performance of driving in general.

Understanding the Interaction between Cyclists' Traffic Violations and Enforcement Strategies: An Evolutionary Game-Theoretic Analysis.

An evolutionary game-theoretic analysis method is developed in this study to understand the interactions between cyclists' traffic violations and the enforcement strategies. The evolutionary equilibrium stabilities were analysed under a fixed (FPS) and a dynamic penalty strategy (DPS). The simulation-based numerical experiments show that: (i) the proposed method can be used to study the interactions between traffic violations and the enforcement strategies; (ii) FPS and DPS can reduce cyclists' probability of committing traffic violations when the perceived traffic violations' relative benefit is less than the traffic violation penalty and the enforcement cost is less than the enforcement benefit, and using DPS can yield a stable enforcement outcome for law enforcement compared to using FPS; and (iii) strategy-related (penalty amount, enforcement effectiveness, and enforcement cost) and attitudinal factors (perceived relative benefit, relative public image cost, and cyclists' attitude towards risk) can affect the enforcement strategy's impacts on reducing cyclists' traffic violations.