Search Results (3)

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. Full article

Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message. Full article

Autonomous vehicles, electrification, and ride-sharing appear to be the next big change in the field of mobility. It can lead to safer roads, less congestion, and reduced parking. In this research, we focus on real-life user experiences of a driverless shuttle bus. We are interested to know what kind of perceptions and feelings people have when they travel in an autonomous shuttle bus. Therefore, we apply Harry Triandis´ Theory of Interpersonal Behaviour (TIB), which recognizes that human behavior is not always rational. Human behaviour, and its change, is linked to the intention, the habitual responses, and the situational constraints and conditions. The qualitative data (n = 44) were collected in 2017 by semi-structured interviews in Espoo, Finland. The interviewees were passengers who travelled a predefined route in a driverless shuttle bus. We applied inductive content analysis. The findings were compared in the theoretical framework of TIB. According to the results, a lack of human driver was not a problem for the passengers. They were surprised how safe and secure they felt in the autonomous vehicle. More specifically, passengers´ perceptions were similar to when travelling by a metro or a tram, where a passenger rarely interacts with the driver, or even witnesses the existence of the driver. However, the results suggest that people are much more intolerant of accidents caused by autonomous vehicles than by humans. On a general level, positive attitudes towards autonomous vehicles can be supported by giving people possibilities to try autonomous vehicles in a safe, real-life environment. The decision whether to use a driverless shuttle bus or not correlates highly with the contextual factors. Route and flexibility are the most important reasons for behavioral changes. Full article