Traffic-related air pollution in urban areas contributes significantly to commuters' daily PM2.5 exposures, but varies widely depending on mode of commuting. To date, studies show conflicting results for PM2.5 exposures based on mode of commuting, and few studies compare multiple modes of transportation simultaneously along a common route, making inter-modal comparisons difficult. In this study...

Nowadays one of the main priorities for railways administrations and operators is the reduction of energy consumption, due to its impact on CO2 emissions and economic costs. This is especially important on high speed lines, in expansion in many countries, given that very high levels of consumption are involved. Energy saving strategies focused on traffic operation can be applied in the short te...

Future railway applications, such as train-side collision avoidance, virtual coupling or autonomous train driving demand reliable and accurate train localization. We focus on exclusive onboard train localization without additional way-side infrastructure. Common approaches for onboard train localization are based on measurements of a global satellite navigation system (GNSS). Well-known methods...

Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarsegrained result, i.e. distinguishing abnormal driving behaviors from normal ones. To improve drivers’ awareness of their driving habits so as to prevent potential car accidents, we need to consider a finegrained monit...

Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then transfer to the real environment. In this paper, we propose a novel realistic translation network to m...

Arguments for the importance of contextual factors in understanding human performance have been made extremely persuasively in the context of the process control industries. This paper puts these arguments into the context of the train driving task, drawing on an extensive analysis of driver performance with the Automatic Warning System (AWS). The paper summarises a number of constructs from ap...

Predictions of driver’s intentions and their behaviors using the road is of great importance for planning and decision making processes of autonomous driving vehicles. In particular, relatively short-term driving intentions are the fundamental units that constitute more sophisticated driving goals, behaviors, such as overtaking the slow vehicle in front, exit or merge onto a high way, etc. Whil...