The feasibility of real-time drowsiness detection using commercially available, off-the-shelf, lightweight, wearable electroencephalogram (EEG) sensors is explored. While EEG signals are known to be reliable indicators of fatigue and drowsiness, they have not been used widely due to their size and form factor. However, the use of lightweight wearable EEGs alleviates this concern. Spectral analysis of EEG signals from these sensors using support vector machines (SVMs) is shown to classify drowsy states with high accuracy. The system is validated using data collected on 23 subjects in fresh and drowsy states. An accuracy of 81% is obtained at a per-subject level and 74% in cross-subject validation using SVM with radial basis kernel. Using a temporal aggregation strategy, the cross-subject validation accuracy is shown to improve to 87%. The EEG signals are also used to characterise the blink duration and frequency of subjects. However, classification of drowsy states using blink analysis is shown to have lower accuracy than that using spectral analysis.

This study presents a novel decentralised hierarchical global energy management control strategy for a group of connected four-wheel-drive hybrid electric vehicles (HEVs) in urban road conditions. In the higher level controller, signal phase and timing information and the optimal cruising velocity are combined to generate the target velocities for the HEVs. A model predictive control framework that focuses on the tracking of the target velocity and the associated desired control variable for every individual vehicle is proposed for the prediction of the optimal velocity that compromises fuel economy, mobility and safety. In the lower level controller, a dynamic programming problem is formulated that utilises the predicted velocity for the global energy management optimisation of every individual HEV. Simulation results validate the advantages of the proposed higher and lower level controllers.

Train trajectory optimisation plays a key role in improving energy saving performance and it is currently receiving increasing attention in railway research because of rising energy prices and environmental concerns. There have been many studies looking for optimal train trajectories with various different approaches. However, very few of the results have been evaluated and tested in practice. This study presents a field test of an optimal train trajectory on a metro line to evaluate the performance and the practicability of the trajectory with respect to operational energy computation. A train trajectory optimisation algorithm has been developed specifically for this purpose, and a field test of the obtained trajectory has been carried out on a metro line. In the field test, the driver controls the train in accordance with the information given by a driving advisory system, which contains the results of the train trajectory optimisation. The field test results show that, by implementing the optimal train trajectory, the actual energy consumption of the train can be significantly reduced, thereby improving the operational performance. Moreover, the field test results are very similar to the simulation results, proving that the developed train kinematics model is effective and accurate.

The use of traffic conflicts is gaining acceptance as a proactive approach to studying road safety. A traffic conflict involves a chain of events in which at least one of the involved road-users performs some sort of evasive actions to avoid a potential collision. Pedestrian evasive actions are normally manifested by changes in the walking behaviour which is expressed through variations in their speed profile. This paper investigates the automatic detection of pedestrian evasive actions in a computer-vision framework. The study proposes a new measure for detecting pedestrians undertaking evasive actions based on permutation entropy (PE). PE is a robust approach for discovering dynamic characteristics of a time-series. In the current context, it reveals the degree of abnormality in the walking pattern by identifying the deviations from the normal free walking. The methodology is applied and validated using video data from an intersection in Shanghai, China. Results show that the PE-based indicator has a high potential to identify and measure the severity of conflicts that involve pedestrian evasive actions compared to traditional time-proximity measures (e.g. time-to-collision and post-encroachment-time). This research finds many applications in the modern transportation infrastructure monitoring, studying pedestrian crossing behaviour and developing safety programs for vulnerable road-users.

Road rage is a serious psychological issue affecting traffic safety, which has attracted increasing concern regarding driving anger intervention. This study proposed a method for driving anger identification based on physiological features. First, 30 drivers were recruited to perform on-road experiments on a busy route in Wuhan, China. The drivers’ anger could be inducted on the study route by elicitation events, e.g. vehicles weaving/cutting in line, jaywalking, traffic congestion and waiting at red light if they want to finish the experiments ahead of basic time for extra pay. Subsequently, significance analysis was used to determine that five physiological features including heart rate, skin conductance, respiration rate, the relative energy spectrum of θ and β bands of electroencephalogram were effective for driving anger identification. Finally, a linear discriminant model was proposed to identify driving anger based on the optimal thresholds of the five features which were determined by receiver operating characteristic (ROC) curve analysis. The results show that the proposed model achieves an accuracy of 85.84% which is 7.95 and 5.71% higher than the models using back propagation neural network and support vector machine, respectively. The results can provide theoretical foundation for developing driving anger detection devices based on physiological features.

Train integrity whilst in service establishes the foundation for railway safety. This study investigates train integrity detection which reliably deduces whether the train consists remain intact. A switching linear dynamic system (SLDS) based train integrity detection method is proposed for Global Navigation Satellite System (GNSS) based train integrity Monitoring System (TIMS) using the relative distance, velocity and acceleration of the locomotive and the last van. There, Expectation Maximisation (EM) algorithm estimates the parameters of SLDS model while the Gaussian Sum Filter infers train integrity state. After that, to cope with false detection and misdetection, a verification procedure and train parting time estimation are designed. The approach is evaluated with both field trials and simulated data. Results show that the false alarm rate and misdetection rate of SLDS-based integrity detection approach are 0 and 0.09% respectively, which proves better than the estimated train length based detection model and Hidden Markov Model (HMM).