Conventional automatic headlight devices determine whether to switch vehicle headlights on or off according to the brightness of the environment. These devices cannot automatically switch on headlights during daytime unless the headlights are required in specific road conditions (e.g. mountainous or foggy areas), thus compromising the safety of road users and drivers. This study develops a light-emitting diode (LED) headlight system that integrates the global positioning system (GPS) and a controller area network (CAN) bus communication interface. This system uses the GPS system to analyse the geographic location and then determines whether to switch on the LED headlight. To reduce the system complexity, a CAN bus is used to limit the number of control wires required. The LED headlight driver also uses an interleaved boost converter that has low conduction losses and input ripple currents, which improves the conversion efficiency of the headlight drivers and extends the battery lifetime. The feasibility of the proposed technology is verified using experimental results.

With the rapid development of the global economy, the energy crisis and the deterioration of the ecosystem are becoming more serious. In this context, the development of electric vehicles has received the attention of all countries. Research on charging and changing facilities has a very important significance for the future comprehensive promotion of electric vehicles. In this study, the authors first analyse the advantages and disadvantages of different charging electric network modes. Then, they introduce distribution planning requirements of electric vehicles’ changing stations, and propose an optimisation model meeting various requirements when selecting station sites. This model aims to minimise overall construction and transportation costs, and meet the charging demand of drivers. Changing stations’ service types and operating characteristics of the substation act as constraint conditions. Finally, they solve the model based on the improved graph algorithms. It not only calculates the optimisation location of the changing stations, but also obtains the corresponding optimal substation access scheme. The research of this study can significantly guide planning and construction of electric vehicles’ charging and battery switch stations.

Once a road is with a high traffic density over its loading, the traffic management has to face the fact that how to alleviate the traffic congestion and how to prevent new traffic jams from being happening in the neighbourhood. This study presents a new control strategy for alleviating the traffic congestion. For this purpose, the authors employ VISSIM to simulate and analyse of an urban traffic congestion problem. In addition, two kinds of traffic light orders are involved. On the other hand, an urban traffic network with three kinds of intersection is used to evaluate their new congestion control policy. Finally, they also demonstrate three scenario cases low, middle, and heavy traffic flow works to compare with normal regular traffic light control rules, the multiline control strategies and their new control policy. Simulation results show that their new proposed traffic control policy is the best one among the others. It is worthy of note that their new proposed policy not only alleviates successfully the congestion problem but also prevents new traffic jams from being happening in the neighbourhood.

Travel time is considered the most useful travel related information as it is the best indicator of the level of service on the road stretch and is completely understandable to all users. Various technologies for measuring traffic flow parameters provide the optimal background for the implementation of data fusion schemes to gain the maximum accuracy from the combination of the available data. The objective of the data fusion is to gain knowledge of predicted departure based travel time from the two outdated accurate measurements. In this paper a new and simple algorithm is proposed for short-term highway travel time prediction by fusing direct travel time measurements estimated by vehicle reidentification, indirect travel time estimated by the extrapolation of spot speed measurements and additional qualitative data in terms of the level of service. The proposed algorithm has been in operation on the A1 highway in Slovenia for more than two years and has shown robust behaviour in the real world environment. The algorithm is capable of providing short-term travel time prediction in real time with a 9 % better accuracy than the presently used travel time prediction algorithms.

Drivers are typically faced with two competing challenges when following a preceding vehicle: they need to leave sufficient space in front to ensure safety, while doing so the probability of cut-ins by other vehicles increases as the car-following gap (CFG) becomes large. Therefore, a strategic CFG that addresses both challenges becomes critical. This study proposes a method to address the problem through an overall objective function of CFG and velocity considering the safety hazard and the probability of cut-ins by other vehicles. Based on this, seeking the strategic CFG translates to finding the optimal solution that minimises the overall objective function. With the support of field data, the method along with concrete models are instantiated and application of the method is elaborated. The method presented in this study can be used to enhance traffic safety and improve traffic management in a connected vehicle environment that promises cooperative adaptive cruise control and cooperative crash avoidance systems.

To be competitive, supply chains must be able to analyse and to evaluate, in real-time, critical differences between the short-term planned actions and the actual performed actions generating states of undesirable or unacceptable risk. The authors propose in this study, a method for monitoring the dynamic evolution of risk associated to delays affecting the transportation operations. They use the failure mode and effects analysis around failure scenarios and they evaluate risk using probability and cost. A failure scenario probability is estimated dynamically in discrete points based on event occurrences during the process execution. Delay risk is then assessed on-line and preventive actions can be undertaken before execution ended. The implementation of this method is performed by monitoring a delivery process facing risks associated to delays in the transport stream.

This study is focused on a decentralised intelligent transportation systems with distributed intelligence based on classification techniques. The rationale behind this architecture is to offer a fully distributed, flexible and scalable system. The architecture encompasses the entire process of capture and management of available road data, enabling the generation of services to promote transportation efficiency. Besides that, thanks to the embedded classification techniques, the system is capable of predicting and reacting to certain events, facing them in an appropriate way. The aim of this work is to demonstrate how the system works in two different real-world use cases. To achieve this objective, how the architecture acts to deal with some incidences is proven. In addition, both use cases serve to show the effective communication between the different components of the system. Besides this, this work demonstrates the fundamental role played by the artificial intelligence techniques working in the system. The well-known C4.5 algorithm has been used for the accurate prediction of traffic congestion and pollution level. The authors explain in this work the reasons for using this classification technique, and the previous experiments performed.

Developments in the area of intelligent transportation system in India are constrained due to the difficulty in accurate automated data collection. Many existing automated sensors may not be accurate under Indian traffic conditions due to their heterogeneity and less lane discipline, resulting in erroneous data. Thus, there is a need to develop tools and estimation schemes that can address these errors and still be able to generate reasonably accurate results. The present study addresses this issue, considering the real-time estimation of speed and density. A dynamical systems approach using the Kalman filtering technique was developed. The implementation was done using data fusion where location-based and spatial traffic variables were used. The estimated values were compared with field data and it was observed that the proposed method was reasonably accurate in the presence of erroneous data.