Location verification has witnessed significant attention in vehicular communication due to the growth in number of location based intelligent transport system (ITS) applications. The traditional cryptography based techniques have been suggested to secure and verify location of vehicles. The traditional techniques increase protocol complexity and computational overhead due to the ad hoc nature of vehicular network environments. In this context, this study proposes two layered location information verification cum security (LIVES) technique based on transferable belief model (TBM). In layer 1, tiles based verification is performed using the concepts of virtual tiles on roads and received signal strength. In layer 2, TBM based verification is performed. Specifically, the belief of the presence of a vehicle on each tiles, and the belief of the presence of a vehicle as neighbour of other neighbouring vehicles are combined as collective belief to attest the location claim of a neighbour vehicle. The performance of LIVES is evaluated with road-based and map-based network environments. The single, mixed and multiple adversary vehicles are considered in both the network environments. The comparative performance evaluations attest the benefits of LIVES as compared with the verification and inference of position using anonymous beaconing and without using LIVES.

A foreground time-spatial image (FTSI) is proposed for counting vehicles in complex urban traffic scenes to resolve deficiencies of traditional counting methods, which are highly computationally expensive and become unsuccessful with increasing complexity in urban traffic scenarios. First, a self-adaptive sample consensus background model with confidence measurements for each pixel is constructed on the virtual detection line in the frames of a video. The foreground of the virtual detection line is then collected over time to form a FTSI. The occlusion cases are then estimated based on the convexity of connected components. Finally, counting the number of connected components in the FTSI reveals the number of vehicles. Based on real-world urban traffic videos, the experiments in this study are conducted using FTSI, and compared in accuracy with two other time-spatial images methods. Experimental results based on real-world urban traffic videos show that the accuracy rate of the proposed approach is above 90% and it performs better than the state-of the-art methods.

Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal–spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

Ground traffic lights are essential for maintaining traffic efficiency and safety at intersections. However, unsignallised intersections are still frequent in actual traffic environments. With the development of new forms of vehicular communication, in-vehicle traffic lights can assist drivers at unsignallised intersections. The authors proposed two types of in-vehicle traffic lights to assist drivers; these corresponded to two-way and all-way stop-controlled intersections. They adopted gap acceptance theory and a first-come-first-served strategy to determine passing priority for the two types of intersections, respectively. They then conducted a driving simulator experiment involving 23 participants, to investigate driver behaviours elicited by the proposed system. They prepared four experimental conditions with combinations of in-vehicle traffic lights and auditory warnings. The authors’ experimental results indicated that in-vehicle traffic lights were associated with significantly longer post-encroachment times and a significantly shorter maximum brake stroke. In terms of eye-gaze behaviours, the percentage of gaze concentration to the road centre area and mean glance durations were deemed acceptable for the avoidance of visual distraction, when in-vehicle traffic lights were presented via a head-up display. Therefore, their analysis of driver behaviours indicates that in-vehicle traffic lights can effectively provide driver assistance at unsignallised intersections.

Detecting and tracking moving vehicles in actual traffic scenes is an embryonic investigation field for smart transportation systems. This study presents the computational paradigm of fuzzy cellular automata (FCA) to manage the sensitive to environmental fluctuations limitation associated with the background subtraction methods for dynamic vehicle tracking. The suggested model extends FCA that is formed with rules supporting least sensitive fuzzy ‘exclusive or’ operation as next case logic to control levels of ambiguity in rule similarly functions. At each step, the refresh of background in frame difference proposals is established according to the number of active cells and fuzzy mapping function; so moving vehicles that their grey level is totally similar to the background grey level are easily identified. Furthermore, an occlusion handling routine based on visual measurement is engaged in discovering the classes of the vehicle occlusions and fragmenting the vehicle from each occlusive class. The empirical outcomes confirm that the suggested method is more accurate and powerful than conventional techniques for real-time vehicle tracking.

The platooning of automated vehicles has potential to significantly benefit road traffic, while its robust performance is less investigated especially considering increasing complexity of interaction topologies. This study presents a decoupled H _∞ control method for automated vehicular platoon to comprehensively compromise multiple performances. The platoon control system is first decomposed into an uncertain part and a diagonal nominal system through the linear transformation, which is motivated by the eigenvalue decomposition of information topology. Based on this almost decoupled system, a distributed H _∞ controller is presented, which can balance the performances of robustness and disturbance attenuation ability. Moreover, a numerical method is given to solve and optimise this controller by using linear matrix inequality approach. Several comparative hardware-in-loop tests of different communication topologies and controllers have been carried out to demonstrate the effectiveness of this method.