Occupational Health and Job Satisfaction Assessment of Bus Rapid Transit (BRT) Drivers

Several studies have focused on ergonomics of commercial and urban bus drivers; however, there exists a dearth of research on BRT drivers. This study was conducted to investigate the factors affecting the BRT drivers\u27 mental health and satisfaction. The study was carried out on 171 BRT drivers in Tehran, Iran. The required data were collected through two questionnaires. The Classification and Regression Tree (CART) and Hierarchical clustering (HC) was used to extract factors affecting mental health and satisfaction of BRT drivers. The important factors affecting BRT drivers\u27 mental health were: dispute with passengers, depression, BMI, criminal behaviours of passengers, driver\u27s retirement conditions, driver\u27s family conditions, fatigue and the rostering. In addition, the most important factors affecting driver satisfaction were: bus repairs, driver\u27s seat and the sound inside the cabin. Possible practical application includes: creating a counseling and psychotherapy unit and improving the quality of buses and repairment

Exploring interacting effects of risk factors on run-off-road crash severity: An interpretable machine learning model joint with latent class clustering

Run-Off-Road (ROR) crashes are frequent and pose a significant risk of injury and fatality. Given the complexity of their mechanisms and the interaction of multiple factors, this study aims to comprehensively investigate the factors influencing the severity of ROR crashes, which have been understudied. Furthermore, addressing the current methodological challenge in machine learning (ML) crash modeling, this study proposes an approach to tackle unobserved heterogeneity in ML. An interpretable ML joint with prior latent class clustering is implemented. The significant risk factors and interactions are interpreted using SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes ROR crash records, traffic, and geometric data from main suburban freeways in Iran collected over a 5-year period. The key interacting factors associated with severe ROR during adverse weather (cluster 1) are: co-occurrence of low congestion and higher speed variation; low congestion, nighttime darkness and rollover; roadway departure by buses and mini-buses and rollover occurrence; vehicle departure and collision with fixed objects. Moreover, the critical interactions for nighttime condition (cluster 2) are: curve sections combined with longitudinal slope; inside shoulder width <1.5 m and a hit median concrete NewJersey barrier. The risky interactions for crashes occurred in curves (cluster 3) are: departing in two-lane sections in low congestion conditions; vehicles collisions with the median concrete NewJersey barriers. The findings of this study enhance comprehension of the significant effects of interactions under various conditions, offering valuable insights for policymakers. Additionally, recommendations are offered to mitigate the risk of severe ROR crashes

Proposing New Methods to Estimate the Safety Level in Different Parts of Freeway Interchanges

Since attention to the safety of traffic facilities including freeway interchanges has been increased during recent years, accident prediction models are being developed. Simulation-based surrogate safety measures (SSMs) have been used in the absence of real collision data. But, obtaining different outputs from different SSMs as safety indicators had led to a complexity of using them as the collision avoidance system basis. Additionally, applying SSM requires trajectory data which can be hardly obtained from video processing or calibrated microsimulations. Estimating safety level in different parts of freeway interchanges through a new proposed method was considered in this paper. Fuzzy logic was applied to combine the outputs of different SSMs, and an index called no-collision potential index (NCPI) was defined. 13608 calibrated simulations were conducted on different ramps, weaving, merge, and diverge areas with different geometrical and traffic characteristics, and NCPI was determined for every case. The geometrical and traffic characteristics formed input data of two safety estimator models developed by Artificial Neural Network and Particle Swarm Optimization. Ten freeway interchanges were investigated to calibrate the simulations and to ensure the validity of the fuzzy method and accuracy of the models. Results showed an appropriate and accurate development of the models

Data mining approach to model bus crash severity in Australia

Introduction: Buses are different vehicles in terms of dimensions, maneuverability, and driver\u27s vision. Although bus traveling is a safe mode to travel, the number of annual bus crashes cannot be neglected. Moreover, limited studies have been conducted on the bus involved in fatal crashes. Therefore, identification of the contributing factors in the bus involved fatal crashes can reduce the risk of fatality. Method: Data set of bus involved crashes in the State of Victoria, Australia was analyzed over the period of 2006–2019. Clustering of crash data was accomplished by dividing them into homogeneous categories, and by implementing association rules discovery on the clusters, the factors affecting fatality in bus involved crashes were extracted. Results: Clustering results show bus crashes with all vehicles except motor vehicles and weekend crashes have a high rate of fatality. According to the association rule discovery findings, the factors that increase the risk of bus crashes with non-motor vehicles are: old bus driver, collision with pedestrians at signalized intersections, and the presence of vulnerable road users. Likewise, factors that increase the risk of fatality in bus involved crashes on weekends are: darkness of roads in high-speed zones, pedestrian presence at highways, bus crashes with passenger car by a female bus driver, and the occurrence of multi-vehicle crashes in high-speed zones. Practical Applications: The study provides a sequential pattern of factors, named rules that lead to fatality in bus involved crashes. By eliminating or improving one or all of the factors involved in rules, fatal bus crashes may be prevented. The recommendations to reduce fatality in bus crashes are: observing safe distances with the buses, using road safety campaigns to reduce pedestrians’ distracted behavior, improving the lighting conditions, implementing speed bumps and rumble strips in high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas

Evaluating Safety Issues for Taxi Transport Management

Taxi drivers face many problems every day including safety issues. The tendency to quickly transport passengers to their destinations for more income has resulted in dangerous driving behaviors leading to traffic violations. So, taxi drivers need appropriate support and training programs to improve safety and reduce the risk of crashes. Implementing different support and safety training programs requires an effective management system. There is a dearth of research on the safety issues of taxis from the perspective of taxi organization managers. This study aims to evaluate the safety issues of taxi transport management through a case study of the Tehran Taxi Organization. A questionnaire survey was conducted with 22 regional managers and 20 transportation specialists of the Tehran Taxi Organization. Issues related to taxi drivers, roads and road users, vehicles, and management systems were evaluated in the questionnaire. Participants determined the relevance level and priority ranking of each question. The level of agreement was then tested using the Kendall concordance test. According to the results, the use of GPS was selected as the best in-vehicle monitoring system that can be used to evaluate drivers in the fleet. Participants believed that passengers’ loading and unloading had the most risk for taxi users. The start-inhibit technology to detect open doors was unanimously evaluated as an efficient technology for taxi safety. With respect to educating taxi users, starting education in schools had the most relevance and priority. Recommendations for increasing the safety of taxis include the use of GPS in taxis to monitor and evaluate drivers, receiving crash reports from police and submitting monthly safety assessment reports, flexibility in drivers’ working hours’ schedule, providing training on drivers fatigue management, and evaluating drivers’ health