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Bus drivers
Introduces the work of the bus driver, who helps people by taking them where they want to go.
Book
Working Conditions Influencing Drivers’ Safety and Well-Being in the Transportation Industry: “On Board” Program
The conditions of work for professional drivers can contribute to adverse health and well-being outcomes. Fatigue can result from irregular shift scheduling, stress may arise due to the intense job demands, back pain may be due to prolonged sitting and exposure to vibration, and a poor diet can be attributed to limited time for breaks and rest. This study aimed to identify working conditions and health outcomes in a bussing company by conducting focus groups and key informant interviews to inform a Total Worker Health® organizational intervention. Our thematic analysis identified three primary themes: lack of trust between drivers and supervisors, the scheduling of shifts and routes, and difficulty performing positive health behaviors. These findings demonstrate the value of using participatory methods with key stakeholders to determine the unique working conditions and pathways that may be most critical to impacting safety, health, and well-being in an organization.
Journal Article
Meet the bus driver = Te presento a los conductores de autobús
Easy-to-follow text, presented in both English and standard Latin American Spanish, introduces beginning readers to a variety of bus drivers, and a helpful picture glossary is included to strengthen vocabulary skills.
Book
Driving behaviors analysis for public transport drivers in Kuwait: a machine learning approach to drivers safety
This research paper addresses the critical concern of evaluating driving behaviors among bus drivers in Kuwait to enhance road safety and prevent accidents. Real driving data from 73 bus drivers working in Kuwait Public Transport Company (KPTC), collected through Teltonika devices, forms the basis of the quantitative analysis. The OPTICS (Ordering Points to Identify the Clustering Structure) algorithm and Expectation–Maximization (EM) clustering were employed using Gaussian Mixture Models (EM-GMM) to classify drivers into distinct behavioral categories. Correlation analyses were then conducted to pinpoint factors influencing risky driving. It was revealed that over speeding is the predominant contributor, accounting for 84.89% of unsafe behaviors. Predictive modeling is undertaken using Gradient Boosted Trees (GBT) and discriminant analysis, with GBT emerging as the most effective, achieving the highest accuracy. Risk indices for each driver cluster are calculated, showing that 28% of drivers exhibit unsafe practices. The probability of accidents for drivers with hazardous tendencies was determined to be 0.772, while the general likelihood of accidents among bus drivers in Kuwait is calculated at 0.318. Surprisingly, no significant correlation is found between age and driving behavior, highlighting the influence of factors such as psychological conditions, fatigue, weather, and road conditions on driving conduct. The findings contribute valuable insights for developing targeted interventions to mitigate risky driving behaviors and enhance overall road safety in the region.
Journal Article
Are we there, Yeti?
\"When Yeti, the school bus driver, takes the class on a surprise trip, everyone wants to know: 'Are we there, Yeti?'\"-- Provided by publisher.
Book
A network flow-based algorithm for bus driver rerostering
Bus driver rostering generates the work plan for a pool of drivers during a planning period of predefined length. This plan, called the roster, must consider the balance between the pressure of costs, the provision of a service of high quality, labour agreements, and the goodwill of the workers. The bus driver rerostering problem occurs during real-time operational planning, when unexpected events—such as non-planned absences of drivers—disrupt the roster. To reconstruct a roster which is originally built in a context of days off schedules for drivers, we propose a reactive methodology based on a multicommodity flow assignment mixed integer linear programming model. The objective is to minimise the number of depot drivers who are assigned to drive and the number of postponed days off, as well as the dissimilarity between the reconstructed and the original roster and the balancing of the workload. The proposed algorithm enables the disrupted roster to be reconstructed at the expense of a relatively small number of changes in drivers’ work and rest periods, while, at the same time, controlling the dimension of the multicommodity flow network. Computational experience based on real-life based instances revealed that the algorithm has the ability to produce reconstructed rosters with few changes to the drivers’ original work assignment, in a short CPU time.
Journal Article
Dog on his bus
Dog loves being a bus driver as he diligently drops off animal passengers at their stops.
Book
Bus Drivers’ Behavioral Intention to Comply with Real-Time Control Instructions: An Empirical Study from China
Developing intelligent bus control systems is crucial for fostering the sustainability of urban transportation. Control instructions are produced in real time by the bus control system; these are important technical commands to stabilize the order in which buses operate and improve service reliability. Understanding the behavioral intention of bus drivers to comply with these instructions will help improve the effectiveness of intelligent bus control system implementation. We have developed a psychological model that incorporates decomposed variables of the theory of planned behavior (TPB) and other influencing variables to explain the micromechanisms that determine bus drivers’ behavioral intention to comply with real-time control instructions during both peak and off-peak-hour scenarios. A total of 258 responses were obtained and verified for analysis. The results showed that the influential factors in the peak- and off-peak-hour scenarios were not identical. Female drivers had greater off-peak-hour behavior intention to comply than male drivers, and there were significant differences in peak-hour behavior intention among drivers of different ages. In both peak and off-peak-hour scenarios, perceived benefit positively and perceived risk negatively affected behavioral intention. Perceived controllability positively affected behavioral intention only during peak hours. Self-efficacy only negatively affected behavioral intention during off-peak hours. Three antecedent variables (i.e., trust, mental workload, and line infrastructure support) influenced drivers’ behavioral intentions indirectly via the decomposed variables of TPB. These results provide profound insights for the improvement and implementation of real-time control technology for bus services, thereby facilitating the development of smart and sustainable urban public transport systems.
Journal Article
Somebody on this bus is going to be famous
During a torrential rainstorm, a school bus goes off the road and, with the driver unconscious, it's up to her passengers to try to rescue each other and go for help.
Book
Evaluating Bus Driver Compliance with Speed Adjustment Commands Under Different Driving Conditions: A Driving Simulator-Based Study
While bus transit plays a critical role in promoting urban transport sustainable development, the phenomenon of bus bunching has brought severe challenges. To alleviate bus bunching, speed control strategies have been widely used to improve the stability of bus headway distribution. However, existing research mainly focuses on developing optimized models with more flexible speed adjustments; a critical yet often ignored fundamental assumption behind these models is that all bus drivers can strictly adhere to the speed instructions issued by the bus dispatch center. To further explore how the compliance of bus drivers affects the implementation of speed adjustment instructions, this study designs a driving simulation experiment under different driving conditions. Modeled after a real bus line in Changsha, China, the designed simulator study incorporates three external variables, weather conditions, road conditions and command types, with behavioral data from 48 professional drivers analyzed via linear mixed-effects models. The results have shown that road conditions and command types emerged as main factors affecting compliance patterns. Specifically, congestion reduced average speeds by 5.1 km/h, especially affecting female drivers who showed 15.9% Command Compliance Index (it has been designed to quantify execution efficiency and will be referred to as CCI hereafter) reduction versus 10.6% for males. Compared to high-speed instructions, the execution efficiency of low-speed instructions increased by 12.3%, with drivers exceeding target speeds during 45.69% of sections to balance speed profiles. It is notable that the fog density had a minimal impact on efficiency, with only about 2% difference in efficiency. Despite standardized operational norms minimizing individual behavioral heterogeneity, significant group-level demographic variations persisted. Male drivers consistently maintained higher compliance with speed adjustment commands across all driving conditions; drivers under 40 and over 50 had a 3.3% higher CCI than middle-aged drivers; and prior bus bunching exposure increased compliance by 3.3%. High-CCI bus drivers strategically balanced headway distribution through controlled overspeeding. These findings provide empirical foundations for optimizing speed control strategies based on road sections. This study explores ways to enhance the attractiveness of public transit and promote sustainable development.
Journal Article