The Advanced Driver Assistance Systems (ADASs) have been proposed to avoid vehicular accidents by using the inter-vehicle cooperation mechanism, the real-time-based sharing of the road and traffic information, automatic controls of braking and acceleration and so on. Several critical challenges in ADAS are seldom discussed in related studies, including unstable driving in velocity or the road lane, driving assistance without driving path prediction, suddenly happening of human abnormal driving or mechanical vehicle accident and so on. Thus, this study proposes the Adaptive vehicle Collision Defence (ACD) system to minimise the driving threat under critical driving threats. The proposed ACD consists of three phases: the adaptive time-to-collision (TTC) determination phase, the threat probability analysis with colouring phase, and the threat avoidance phase. The main objectives are to minimise the driving threat probability and to achieve active safe driving for the advanced driver assistance systems. Numerical results indicate that the proposed ACD outperforms the compared approaches in TTC probability, driving threat probability, and region boundary.

Railways provide a transport system which is regarded as efficient but which is not immune of risks. The railway environment is also subject to extreme weather conditions including high temperatures, constant exposure to the sun, rainfall and wind. In addition, since it traverses several regions where there is no mobile phone coverage, communication is not possible between trains and people through a central system. Thus, it is worth considering if the development of technological solutions to increase the safety of employees who do maintenance work on the railways, might be a research area of potential value. This is the context in which this study has been undertaken, where an attempt is made to describe a solution based on wearable components for the safety of maintenance staff on the railways. This study outlines some of the challenges that should be taken into account for the solution put forward with regard to aspects of usability linked to people's perceptions of tactile, visual and sound alerts. In addition, there is a description of the solution set out and an assessment of communication devices that are aimed at allowing independence for the network data providers and maintaining the operating system of the whole railway network.

Acquiring real-time traffic information is a basic requirement for dynamic vehicular navigation systems. The majority of the current navigation systems are based on static traffic information. Building on mobile crowdsensing technology, the authors propose context-aware traffic estimation and navigation system (CANS), a context-aware system that can estimate traffic state without any requirement for expensive infrastructure. Using only available equipment, it can provide dynamic navigation service to drivers. The proposed system consists of three main components: local traffic estimation, global traffic aggregation, and navigation. In this system, vehicles estimate local traffic state using vehicular contextual information including speed and acceleration by relying on fuzzy logic, and transmit the information to the urban server. The server integrates the received local traffic information from different vehicles and estimates the global traffic state, providing the traffic-aware navigation system to drivers. CANS performance is evaluated for an urban scenario in a traffic flow in Birjand, Iran. The experiment is conducted for evaluating CANS in both traffic congestion estimation and navigation. The results show an accurate estimation of traffic states along urban roads. Compared with previous approaches, CANS overrides them for its reduced travel time.

Although smart-card data secures collective travel information on public transportation users, the reality is that only a few cities are equipped with an automatic fare collection (AFC) system that can provide user information for both boarding and alighting locations. Many researchers have delved into forecasting the destinations of smart-card users. Such effort, however, have never been validated with actual data on a large scale. In the present study, a deep-learning model was developed to estimate the destinations of bus passengers based on both entry-only smart-card data and land-use characteristics. A supervised machine-learning model was trained using exact information on both boarding and alighting. That information was provided by the AFC system in Seoul, Korea. The model performance was superior to that of the most prevalent schemes developed thus far.

This study focuses on the break-taking behaviour pattern of long-distance freight vehicles, providing a new perspective on the study of behaviour patterns and simultaneously providing a reference for transport management departments and related enterprises. On the basis of global positioning system trajectory data, the authors select stopping points as break-taking sites of long-distance freight vehicles and then classify the stopping points into three different classes based on the break-taking duration. They then explore the relationship of the distribution of the break-taking frequency between the three single classifications and their combinations, on the basis of the break-taking duration distribution. They find that the combination is a Gaussian distribution when each of the three individual classes is a Gaussian distribution, contrasting with the power-law distribution of the break-taking duration. Then, they do experimental analysis to the distribution of the break-taking durations and frequencies, and find that, for the durations, the three single classifications can be fitted individually by an exponential distribution and together by a power-law distribution, for the frequencies, both the three single classifications and together can be fitted by a Gaussian distribution, so that it can validate the above theoretical analysis.

Energy-efficient train operation is of high importance in urban railways. It takes into account both energy saving and punctuality at the same time, which is the goal of the most operators. In this study, the effect of employing variable regenerative energy recovery rate (RERR) for each inter-station was shown in energy-efficient operation improvement. For this purpose, a two-stage optimisation was proposed. In the first stage, which is a mechanical optimisation, optimal speed profiles for a single train were determined by a bi-objective optimisation through a non-dominated sorting algorithm. In this stage, a simulation model for determination of train's performance was developed and utilised. It was shown that variable RERRs eventually led to different energy-time Pareto fronts. In the second stage, the total input energy for a multi-train system was minimised by using obtained optimal speed profiles from the first stage and distributing total travelling time among inter-stations (electrical/electromechanical optimisation). It was shown that optimisation of the net energy – with different values of RERR – instead of consumed energy, can reduce the total input energy of the network. The simulation results, which are based on actual operating data of Mashhad urban railway system, confirm the feasibility and effectiveness of the proposed method.