Traffic Safety Research Articles

Texting while driving is a visual problem influenced by phone viewing angle and working distance in young individuals

PurposeTo investigate the impact of smartphone viewing distance and angle on reaction times. DesignA prospective, self-controlled, single-center study. MethodsParticipants engaged in a driving simulation facing a large screen with a simulated brake pedal. They were tasked to stop the simulation once recognizing the deceleration of upcoming traffic. Tests were conducted without distraction and with a standardized distraction simulating texting while driving (TWD). Smartphone positions varied at distances of 30 cm and 60 cm, and at angles parallel to and 30° below the road plane. Reaction times were measured from the onset of simulated closure to detection. Stopping distances were extrapolated using data from the National Highway Traffic Safety Administration. ResultsNinety-four participants were included with a mean age of 24 ± 2.7 years. The control reaction time was 11.5 ± 4.1 s. Reaction times significantly decreased with smartphone placement at a closer distance of 30 cm parallel (17.0 ± 3.3 s) vs 60 cm parallel (15.4 ± 3.8 s), P < 0.001. A 30-degree downward placement at 30 cm (18.6 ± 4.0 s) and 60 cm (17.9 ± 3.6 s), further decreased reaction time compared to parallel phone positioning, P < 0.001. Extrapolating to stopping distances based on real-world data, smartphone distractions placed at 30 cm 30° below the dashboard had the greatest effect, resulting in a 3 times increase of stopping distance compared to the control, 1201 vs 394 ft respectively, P < 0.001. ConclusionTWD significantly delays reaction time in young participants. Both the distance and viewing angle of a smartphone significantly influences reaction times during driving simulations. The greatest delays are observed when the smartphone is positioned closer to the user and at a 30-degree angle which we hypothesize is due to vision blur from increased accommodation, loss of stereopsis, and fixation with the peripheral retina.

Modeling the trajectories of a group of unmanned aerial vehicles based on the ADRRT-Connect algorithm in the problem of placing seismic sensors

Optimal route planning is a key aspect of successfully delivering geosensors using unmanned aerial vehicles (UAVs) to geographically distributed targets. The optimality of the route depends on the time required to plan the route and execute the flight to a target or group of targets, considering traffic safety and dynamic constraints. The trajectory calculation algorithm must always find a route, if one exists, and if it is impossible to find one, inform the operator about this circumstance. This article proposes a method based on a set of algorithms that ensure efficient distribution of targets among group members based on information about the targets and characteristics of the unmanned aerial vehicles used. The method involves constructing optimal routes considering minimizing path length, avoiding sharp turns and loops, as well as avoiding obstacles and avoiding collisions with other unmanned aerial vehicles within the group. To test the proposed method, software was developed using the ROS/Gazebo en-vironment. The proposed method is a comprehensive solution to the problem of optimal route planning for UAVs with an emphasis on efficiency, safety, and efficiency in the context of geosensor delivery using unmanned aerial vehicles.

Road Traffic Crash-Related Musculoskeletal Injuries in Bafoussam, Cameroon: Exploring the Gaps in Physiotherapy Access and Utilization: A pilot study

Background: Road traffic crashes worldwide result in significant morbidity and mortality, with musculoskeletal injuries often neglected in primary management. This study aims to investigate the prevalence and management of Road Traffic Crash-Related Musculoskeletal Injuries (RTCMI) in Bafoussam, Cameroon. Methods: We conducted a prospective cross-sectional study in Bafoussam's hospital emergency units from January to June 2024. Using an exhaustive sampling, we enrolled 120 road traffic traumatic victims who provided informed consent to participate in the study. We excluded patients unable to communicate due to trauma status. Data analysis employed Excel 2016 with a 95% confidence interval. Results: Our study revealed a RTCMI prevalence of 73%, with fractures being the most common injury (80%). Notably, physiotherapy was not prescribed in primary management, and only 11% received Physiotherapy prescriptions in secondary care. A mere 17% of respondents requested physiotherapy, citing prescription failure (100%), lack of knowledge (83.6%), preference for traditional treatment (21.9%), and high costs (2.7%) as barriers. Conclusion: This study highlights the need for further research into RTCMI management and physiotherapy integration in Cameroon. Our findings suggest significant gaps in prescription, awareness, and accessibility, emphasizing the importance of scaling up this study to inform healthcare policy and practice.

Fuzzy Petri Nets for Traffic Node Reliability.

Self-driving cars are one of the main areas of research today, but it has to be acknowledged that the information from the sensors (the perceptron) is a huge amount of data, which is now unmanageable even when projected onto a single traffic junction. In the case of self-driving, the nodes have to be sequenced and organized according to the planned route. A self-driving car in Hungary would have to be able to interpret more than 70,000 traffic junctions to be able to drive all over the country. Besides the huge amount of data, another problem is the issue of validation and verification. For self-driving cars, this implies a level of complexity using traditional methods that calls into question the economics of the already existing system. Fuzzy Petri nets provide an alternative solution to both problems. They allow us to obtain a model that accurately describes the reliability of a node through its dynamics, which is essential in perception since the more reliable a node is, the smaller the deep learning mesh required. In this paper, we outline the analysis of a traffic node's safety using Petri nets and fuzzy analysis to gain information on the reliability of the node, which is essential for the modeling of self-driving cars, due to the deep learning model of perception. The reliability of the dynamics of the node is determined by using the modified fuzzy Petri net procedure. The need for a fuzzy extension of the Petri net was developed by knowledge of real traffic databases.

Feasibility of LoRa‐based Infrastructure‐to‐Vehicle (I2V) communication for road weather information systems

SummaryA road‐weather information system (RWIS) is an attractive application in the context of intelligent transportation systems (ITSs), which is used where climatic conditions and meteorological events worsen traffic safety. The primary goal is to significantly reduce potential accidents by sending early warning messages to prompt immediate action. For this purpose, road weather information stations are placed at critical points on the highways. In addition to responsible transportation agencies, enabling direct warning message transfer from roadside infrastructure to vehicles can significantly improve the effectiveness of a typical RWIS application. Dedicated short range communication (DSRC) and cellular vehicle to everything (C‐V2X) technologies are extensively used for data exchange between vehicles (V2V) and between vehicles and road infrastructure (V2I or I2V) under the umbrella term of vehicle‐to‐everything (V2X) communications. Latency and coverage are key performance indicators of the vehicular network that determine how drivers react to risks in adverse road and weather conditions. V2X‐based connectivity mainly aims to offer low latency in message delivery, but it is not possible to obtain a wide‐range coverage due to significant propagation loss in the dedicated 5.9 GHz ITS frequency band. In this study, we presented a long range (LoRa)‐based RWIS application that uses I2 V communication at sub‐GHz bands. Our main contribution is proposing a novel method for RWIS implementations, where the LoRa gateways run in a half‐duplex way to collect information from road weather information stations in the uplink channel and then broadcast warning messages to vehicles in the downlink channel. To validate the applicability of the proposed method, we presented a feasibility study that includes transmission range and latency analysis. Our theoretical calculations' results validate the more extended transmission range and applicability of LoRa connectivity on RWIS applications, especially for common scenarios like black ice where the climate risks do not occur suddenly. We also showed that our methodology would be beneficial due to the ability to offer wide coverage, especially in the areas where cellular base stations have not yet existed or V2X road infrastructure has not yet been established.

자전거 교통안전표지 시인성 개선을 위한 연구

In South Korea, personal mobility devices such as bicycles have recently gained attention due to air pollution concerns and eco-friendly policies.. However, as of 2023, bicycle paths in Seoul only account for 15% of all roads, and more than 10% of bicycle accidents in 2022 occurred while cyclists were walking or crossing the sidewalk. Since bicycles are legally prohibited from riding on sidewalks, there is an increasing need to improve bicycle traffic safety signs to ensure safe riding. This study assessed the condition of various bicycle traffic safety signs on road and surveyed bicycle users to evaluation their visibility when riding. The evaluation criteria included visibility, information usefulness, and color contrast. The findings showed that the effectiveness of signs is influenced by the use of colors and reflective materials that stand out from the surrounding environment. Based on these results, new design direactions were proposed to enhance bicycle safety. The outcomes of this study are expected to serve as foundational direaction of improvements for reducing bicycle-related accidents and promoting the use of bicycles.

Smart tachographs application in monitoring and controlling the Operation of Vehicles and Drivers in „ITS“ Environment

ITS systems enable information transparency, manageability and improved response of the traffic system, so they have the epithet of intelligent. The application of ITS solutions on tachographs, as devices that record all the activities of truck and bus drivers, is defined in the ISO 14813-1:2015 Standard in the context of remote data transmission from tachographs. In view of the growing problems regarding violations of regulations on working hours and mandatory vacations for mobile workers, frequent manipulations and misuse of tachographs, as well as regarding the state of traffic safety and increasing environmental pollution, the EU has set stricter conditions in the field of driving these commercial vehicles in its regulations, in the manner of implementing the ITS system of smart tachographs, which brings significant benefits, especially in terms of the work of control authorities as entities that influence better compliance with regulations and the enforcement of legal regulations.

A CAV-Lead speed advice approach considering local spatiotemporal traffic state near bottlenecks

Bottlenecks of the freeway generated especially by traffic accidents or temporary work zones contribute to significant reductions in system throughput and hinder the efficient traffic operations. It is imperative to take proactive measures to improve traffic state. With the rapid advancements in intelligent transportation, connected and autonomous vehicles (CAVs) have attracted much attention by its speculated capabilities in improving traffic safety and well-organized operational coordination. Therefore, reasonably utilizing the advantages of CAVs is possible to reduce the impact induced by bottlenecks. In this research, we propose a novel algorithm called CAV-Lead to obtain the CAV’s regulated speed under mixed CAVs and human-driven vehicles (HVs) environment to improve the overall utilization of the freeway capacity near bottlenecks. Firstly, we illustrate the basic principle of the CAV-Lead algorithm that takes both microscopic and macroscopic traffic characteristics into account. Then, based on the local spatiotemporal traffic state, the CAV-Lead algorithm is proposed to determine each CAV’s speed under mixed flow. Furthermore, a real-time simulation control framework considering the random behavior of HVs is presented. Moreover, several simulation evaluations including comparisons with basic scenarios and similar research are conducted under various CAV market penetration rates (MPRs). The results demonstrate that the CAV-Lead could improve the traffic performance, especially for the high traffic demand with certain MPRs.

Comparative Assessment of Expected Safety Performance of Freeway Automated Vehicle Managed Lanes

The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature as a tool to improve traffic operation and safety performance as AVs and driver-operated vehicles (DVs) coexist in a mixed-vehicle environment. This paper focuses on investigating the safety impacts of deploying AVMLs on freeways by repurposing general-purpose lanes (GPLs). Four ML strategies considering different lane positions and access controls were implemented in a traffic microsimulation under different AV market adoption rates (MARs) and traffic demand levels, and trajectories were used to extract rear-end and lane change conflicts. The time-to-collision (TTC) surrogate safety measure was used to identify critical conflicts using a time threshold dependent on the type of following vehicle. Rates of conflicts involving different vehicle types for all ML strategies were compared to the case of heterogeneous traffic. The results indicated that the rates of rear-end conflicts involving the same vehicle type as the lead and following vehicle, namely DV-DV and AV-AV conflicts, increased with ML implementation as more vehicles of the same type traveled in the same lane(s). By comparing the aggregated conflict rates, the design options that were deemed to negatively impact traffic efficiency and capacity were also found to negatively impact traffic safety. However, other ML options were found to be feasible in terms of traffic operation and safety performance, especially at traffic demand levels below capacity. Specifically, one left-side AVML with continuous access was found to have lower or comparable aggregated conflict rates compared to heterogenous traffic at 25% and 50% MARs, and, thus, it is expected to have positive or neutral safety impacts.

Applying the Health Belief Model to mobile device distracted driving

Introduction: The advancement of mobile devices has resulted in constant connectivity, but at the expense of traffic safety. The goal of this study was to apply the Health Belief Model to understand the barriers preventing drivers from driving without manipulating their devices, and what they perceived would motivate them to stop driving distracted. Methods: We conducted a nationwide survey of 2,013 U.S. licensed drivers. Participants indicated how much they agreed with or supported 63 statements and concepts pertaining to the Health Belief Model constructs of threats, barriers, benefits, and cues to action related to manipulating devices while driving. Heath Belief Model constructs were compared between distracted drivers, or those who regularly did (during most or all drives in the previous 30 days) one or more mobile device tasks, and non-distracted drivers. Logistic regression evaluated the relationship between Health Belief Model constructs and distracted driver designation. Results: Those who agreed more with threats (odds ratio [OR], 1.61; 95 % confidence interval [CI], 1.27, 2.04) and disagreed more with barriers to stopping (OR, 0.36; 95 % CI, 0.31, 0.41) were more likely to not drive distracted. We identified distracted drivers’ top barriers to avoiding cellphone distractions, as well as barrier categories that were significantly greater for distracted drivers than non-distracted drivers. Distracted drivers felt most strongly that intrapersonal, interpersonal, and policy cues would motivate behavior termination, and more support of technological countermeasures was associated with regularly driving distracted after controlling for support for policy and organizational countermeasures. Conclusions: Simultaneously increasing threat perceptions, targeting the top barriers identified, and implementing policy-, interpersonal-, and technological-based countermeasures may encourage device-free driving. Practical Applications: When designing interventions or programs, state highway safety offices should find new ways to increase threat perceptions and offer solutions to the barriers most cited by distracted drivers.

Driving risk prediction of urban arterial and collector roads using multi-dimensional real-time data

With the objective of predicting driving risks on urban arterial and collector roads, this paper primarily collects and processes vehicle trajectory data and driver-vehicle-road-environment data. Using Entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and K-means, risk scores and levels for the target roads are determined. An original dataset comprising 10,320 samples is established, with risk levels as labels and multi-dimensional data as features. SHapley Additive exPlanations (SHAP) analysis is utilized to extract important features. The performance of four ensemble models are compared, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Stacking. Three data resampling techniques are integrated to address data imbalance. The combination of LightGBM and Deep Convolutional Generative Adversarial Network (DCGAN) outperforms other approaches, showing potential for real-time risk prediction. Besides, DCGAN is employed to update the multi-dimensional features. The models' predictive performance is evaluated under various update ratios using F1-score and training time, with LightGBM demonstrating superior accuracy and efficiency compared to its counterparts (RF, XGBoost, and Stacking). This study develops a driving risk prediction system based on multi-dimensional features, offering a theoretical foundation and technical approach to enhance urban road traffic safety management.

Unveiling the risks of speeding behavior by investigating the dynamics of driver injury severity through advanced analytics

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000–10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

Development of hazard prediction test and interventions for two-wheeled electric vehicle riders in China

Objective This paper aimed to develop a hazard prediction test and enhance two-wheeled electric vehicle (TWEV) riders’ hazard perception and prediction capabilities via interventions by executing two distinct studies. Study 1 aimed to develop and validate a hazard prediction test. Study 2 evaluated the efficacy of two interventions, self-commentary and what happens next (WHN), integrating expert commentary. Method For Study 1, a video-based hazard prediction test was developed through video recording and clipping, with participants categorized into high and low prediction ability groups for experimentation. Data analysis employed the receiver operating characteristic (ROC) curve. Study 2 categorized participants into four groups: self-commentary with licenses (SCL), self-commentary without licenses (SCNL), WHN with licenses (WHNL), and WHN without licenses (WHNNL), for conducting a one-week intervention experiment. Data collected from participants’ pre-intervention, post-intervention, and aftereffect tests were subjected to repeated measures analysis of variance (ANOVA). Results Analysis of the ROC curve indicated the test can distinguish the riders with different hazard prediction levels. ANOVA results demonstrated that the measurement time had a significant positive effect on scores (p < 0.001). Both interventions significantly improved hazard prediction ability (p < 0.05), and the effect persisted one week after administration. The effect of the WHN intervention was significantly greater than the self-commentary method across all time points. Conclusion The hazard prediction test developed in this study could assess riders’ hazard prediction ability, with the identified interventions demonstrating effectiveness in enhancing this ability. These findings suggested potential application in future qualification tests for TWEV riders, contributing to enhanced traffic safety awareness among TWEV riders in China, thus advancing overall traffic safety.

Enhancing work zone crash severity analysis: The role of synthetic minority oversampling technique in balancing minority categories

Road work zones are becoming increasingly common due to the aging infrastructure and the need for capacity enhancement. They present significant safety risks due to narrow lanes, uneven traffic flow, lower speed, and reduced visibility. It is particularly important to understand the role of human behavioral factors in WZ crash injury severity due to difficulty navigating such areas. Furthermore, the crash injury data available is mostly imbalanced, primarily due to the lower incidence of high-cost fatal and severe injuries, and can benefit from the use of emerging analysis techniques. This research study examines a unique dataset comprising 7,855 WZ crashes in Tennessee from 2018 to 2022 as a case study to provide useful insight into the behavioral factors associated with injury severity and how they change after adjusting for the underrepresented fatal and serious injuries within the dataset. The study applies frequentist methods and a machine learning technique enhanced with the Synthetic Minority Oversampling Technique (SMOTE), addressing the data imbalance (relatively fewer fatal and serious injuries) for useful inferences and predictions. The study results indicate that aggressive driving, overspeeding, and drunk driving significantly elevate injury severity. Additionally, after balancing the minority categories of crash injury severity levels, the importance of contributing factors changes. The study offers engineers and data analysts a framework for analyzing imbalanced data, a prevalent issue in crash injury severity analysis. By exploring key behavioral factors responsible for injury severity in WZ crashes, the study provides useful insight and valuable information to traffic safety engineers, transportation agencies, and policymakers to implement enhanced safety measures in WZ design and management, ultimately aiming to mitigate injury severity and to improve overall safety for road users.

Vienna road crashes: a multivariate approach to understanding driver-related risks

Road safety is worldwide researched field where crashes play a causal role and injuries are counted as outcomes. Approaches and techniques are diverse, but with same goal—contribute to safer road environment for everyone included. Drivers of personal vehicles are the most protected traffic group due to protection provided by vehicle and mandatory safety belt in comparison to pedestrians as the most venerable participants. Also, this group of drivers is characterized by the high presence of very young drivers whose psychological characteristics place them in a vulnerable risky subgroup. According to the original data of the traffic police on crashes of personal car drivers in the town of Vienna, since 2010 they have had the highest number of crashes in 2012 and after slow decrease is recorded. Still high in total number of crashes involving only drivers of personal vehicles from a traffic safety point of view this still represents an extremely worrying problem that has not received proper social attention. In response to knowledge gaps, this research is essential to address leading characterizations in crashes with aim to answer what is trend in crash occurrence during 2010–2020 inside Vienna municipality, and what is predicted trend. Is there significant and distinctive difference based on gender and age with conditions under which crashes are occurring influencing different injury degree. Multiple regression undoubtedly points fields for action in statistically based findings providing the most important answer to this research: why there are so many injuries and what is leading cause of them. Answering this question contributes toward higher safety environment for all participants.

ASSESSING THE DYNAMICS OF CHANGES IN ROAD SAFETY INDICATORS IN UKRAINE

Purpose. A study of road traffic in Ukraine shows that despite several legislative and regulatory documents aimed at improving road safety, the total number of road traffic accidents (RTAs) is increasing from 2015 to 2021. According to statistical data, each road traffic accident involves more than one injured person on average. A significant reduction in the accident rate can be achieved by developing a national action programme to improve road safety, which should be based on a systematic analysis of the nature and causes of road accidents. Therefore, the purpose of this paper is to conduct a comprehensive study of the level of road safety in Ukraine based on a systematic approach to assessing road accident data. Methodology. In the course of the research, the methods of regression analysis were used to study the absolute, specific and relative accident rates and the causes of accidents. All calculations were performed using Excel software. Results. This paper studies the international regulatory framework and national legislative documents aimed at improving traffic safety. An analysis of changes in absolute, specific and relative accident rates in 22 regions of the country is carried out. The general trends in accident rates in Ukraine from 2015 to 2021 are identified. The total number of road accidents in Ukraine, with some fluctuations in some years, has been increasing from 2015 to 2021. However, the percentage of road accidents with victims in the total number of accidents is decreasing. The social risk indicator in 2021 decreased by 15.86% compared to 2015. However, this figure remains almost twice as high as in top countries, where even those who died within 30 days of being injured in a road accident are taken into account. The scientific novelty. Based on the methods of mathematical statistics and regression analysis, the methods of estimating absolute, specific and relative accident rates, which take into account the number of victims, injured and killed in road accidents in the study of different regions of Ukraine, have been further developed. The practical significance. The obtained regression dependencies of accident rates can be used to formulate the provisions of an effective national road safety system aimed at significantly reducing the number of people killed and injured in road accidents, as well as reducing the economic losses caused by them.

Developing a Machine-Learning-Based Automatic Incident Detection System for Traffic Safety: Promises and Limitations

Developing a Machine-Learning-Based Automatic Incident Detection System for Traffic Safety: Promises and Limitations

Real-time accident risk identification for freeway weaving segments based on video analytics

Accurately identifying the risk of traffic accidents in real time for specific road sections is crucial for implementing proactive traffic control strategies. The existing studies have focused on real-time prediction of collisions between two vehicles, identification of accident risks on road segments relies heavily on historical accident data. This study proposes a framework for identifying road section accident risks at the granular level by utilizing traffic conflicts as an intermediary. In this framework, vehicles are detected and tracked in real time and the pixel coordinates are then efficiently converted into world coordinates based on the camera projection principle. A binary logit model is constructed to identify road section accident risks using carefully selected macroscopic traffic flow parameters. The proposed framework was validated on two one-sided ramp weaves in Jinan, China, achieving an accuracy of 76.2 %. The findings will serve as a foundation for traffic safety monitoring and proactive traffic control.

Investigation on car-following heterogeneity and its impacts on traffic safety and sustainability

This study proposes a general framework to investigate car-following heterogeneity and its impacts on traffic safety and sustainability. The framework incorporates rigorous driving style classification using a semi-supervised learning technique and a micro-simulation process that includes 66 fine-grained traffic scenarios exhibiting varying degrees of heterogeneity. Validated using two distinct real-world datasets reveals the superiority of S3VM-based classifiers over traditional SVM classifiers in driving style classification. Simulation results show that an increase in driving aggressiveness is correlated with higher safety issues and greater environmental impacts. Further elucidation of these impacts from the mechanism of underlying characteristics of driving behaviour and traffic flow dynamics indicates that less aggressive drivers can lead to the formation of vehicular platoons, thereby encouraging more aggressive drivers to adopt a milder driving style. Importantly, the formation of these platoons is influenced by both the proportion and spatial distribution of less aggressive vehicles. The proposed approach promises advantages in reducing the negative impacts of driving heterogeneity, thus benefiting Intelligent Transportation Systems (ITS) by improving traffic safety and sustainability.

Evaluating the Effectiveness of an Online Gamified Traffic Safety Education Platform for Adolescent Motorcyclists in Pakistan

This study explores the potential of online traffic safety education for adolescent motorcyclists in Pakistan. An e-learning platform, “Route 2 School” (R2S), was developed focusing on traffic knowledge, situation awareness, risk detection, and risk management. Male students (14–18 years) who commute to school by motorcycle were divided into an experimental group (EG) and a control group (CG), both completing pre- and post-measurement questionnaires. The EG showed significant improvement in knowledge, risk detection, and risk management compared to the CG, but not in situation awareness. Participants reported increased traffic safety awareness and suggested adding more interactive elements. The R2S platform’s scores revealed better performance in risk detection and risk management modules than situation awareness. Time spent on modules varied, with situation awareness requiring the most time. Adolescents expressed satisfaction with the platform, acknowledging its role in increasing traffic awareness. This study provides initial insights into the effectiveness of online traffic safety education in Pakistan, highlighting the potential to address the lack of comprehensive traffic safety education in schools. Further research and stakeholder engagement are recommended to integrate such platforms into formal education, potentially reducing traffic-related injuries among adolescent motorcyclists in developing countries.