One effective strategy of cooperative vehicle-highway systems with connected and automated vehicles (CAVs) is speed harmonisation, which dynamically adjusts vehicle speed recommendations to reduce speed differentials. Speed harmonisation can be applied to potentially improve mobility and safety. This study documents the development of an innovative vehicle control platform and experimental design to conduct speed harmonisation field experiments. In our experiment, a fleet consisting of three CAVs and five probe vehicles was deployed on a roadway segment that experiences daily recurring traffic congestion. The objective of the CAVs was to regulate traffic upstream of the bottleneck, so that vehicles move with uniform speed, thereby creating a steadier flow of traffic. The performance measures (e.g. travel time, fuel consumption, and speed oscillations) of the probe vehicles were analysed. To evaluate the effectiveness of speed harmonisation on smoothing the flow of traffic, these measures were compared between the probe vehicles that followed the CAVs. The results show the potential that CAVs have in reducing the oscillatory behaviour of traffic.

Variable speed limits (VSLs) have a potential to improve mobility and safety of motorway through harmonisation of traffic flow. However, the success of VSL is highly dependent on drivers’ compliance to the displayed speed limits. Emerging technologies in the field of connected autonomous vehicles (CAVs) is likely to revolutionise the way VSL will be operated in the near future. VSL can be integrated with CAVs where CAVs will automatically obey the displayed speed limits. However, 100% penetration rate of CAVs is a long-term goal. At the initial stage of deployment, CAVs will coexist with manually driven vehicles on motorways. This study explores ways to implement VSL under a mixed traffic condition where CAVs and manually driven vehicles coexist in the traffic stream. A hybrid approach combining particle swarm optimisation with proportional-integrated-derivative is proposed to improve VSL implementation under a range of mixed traffic conditions. The simulation results showed that the proposed method successfully achieved the desired speed limits and rapidly stabilised the system at the initial and transient points at penetration rates of 50 and 75%, whereas the method encountered serious overshoots at lower penetration rates.

Traditional traffic monitoring systems are mostly based on road side equipment (RSE) measuring traffic conditions throughout the day. With more and more GPS-enabled connected devices, floating car data (FCD) has become an interesting source of traffic information, requiring only a fraction of the RSE infrastructure investment. While FCD is commonly used to derive historic travel times on individual roads and to evaluate other traffic data and algorithms, it could also be used in traffic management systems directly. However, as live systems only capture a small percentage of all traffic, its use in live operating systems needs to be examined. Here, the authors investigate the potential of FCD to be used as input data for live automated traffic management systems. The FCD in this study is collected by a live country-wide FCD system in the Netherlands covering 6–8% of all vehicles. The (anonymised) data is first compared to available road side measurements to show the current quality of FCD. It is then used in a dynamic speed management system and compared to the installed system on the studied highway. Results indicate the FCD set-up can approximate the installed system, showing the feasibility of a live system.

Early detection of drowsy driving is an important issue for driving safety. Quantitative electroencephalography (EEG) is an attractive method for detecting brain activity changes. However, further study is still needed to evaluate the feasibility of wearable devices that can detect drowsy driving in real-world settings. This study sought to determine whether convenient EEG recording locations are sensitive in detecting brain activity changes associated with drowsy driving and to characterise these EEG changes. Twenty-two healthy adult subjects were recruited to participate in a car-following task using a driving simulator. EEG data were recorded from four locations, two frontals (Fp1, Fp2) and two temporals (T3, T4) of the brain while driving. The results showed that the increase of δ activity, decrease of θ and α activity and a decrease of spectral edge frequency at 90% were found in the drowsy state compared to the alert state (paired t-tests, p < 0.05). Effect sizes for EEG changes were larger at the temporal locations compared to frontal locations. This suggests temporal locations can be feasible recording locations for wearable monitoring devices to detect drowsy driving.

This study presents a traffic violation detection and reporting system, that does not rely on costly infrastructure or the presence of law enforcement. It relies solely on broadcast messages among vehicles and reports delivery to the transportation authority. A vehicle is modelled as an automaton (in the computational sense) that has its own state and has a read access to the state of other automata of other vehicles in a constant size neighbourhood. The common traffic rules and communication rules make the program of these automata that guide the transitions of the vehicles in space and time. By observing the transitions of the vehicles in their neighbourhood, a vehicle can decide if these comply with the traffic rules encoded in the system. Whenever a transition is not performed according to the program, a violation occurs. These violations are reported and witnessed to the transportation authority by the vehicles in the neighbourhood, which act as witnesses and reporters. The system preserves privacy and allows no false positives.

For traffic flow models, calibration and validation are essential. Cellular automaton (CA) models are a special class of models, describing the movement of vehicles in discretised space and time. However, the previous work on calibration and validation does not discuss CA models systematically. This study calibrates and validates a stochastic CA model. The authors use a simple CA model, which only has two important parameters to be calibrated. The methodology for optimisation is to minimise the relative root mean square error between two properties: the averaged velocity and the variation of velocities in a platoon at a given density. Three different sites are used as cases to show the methodology, for which different types of data (video trajectories or GPS data) are available. The authors find that the best model parameters vary for the different locations. This may result from various driving strategies and potential tendencies. Thus, it is concluded that for CA models, various traffic flow phenomena need to be simulated by various parameters.

Pedestrian-to-vehicle communications, where pedestrian devices transmit their position information to nearby vehicles to indicate their presence, help to reduce pedestrian accidents. Satellite-based systems are widely used for pedestrian positioning, but have much degraded performance in urban canyon, where satellite signals are often obstructed by roadside buildings. The authors propose a pedestrian positioning method, which leverages vehicular communication signals and uses vehicles as anchors. The performance of pedestrian positioning is improved from three aspects: (i) channel state information instead of received signal strength indicator (RSSI) is used to estimate pedestrian-vehicle distance with higher precision. (ii) Only signals with line-of-sight path are used, and the property of distance error is considered. (iii) Fast mobility of vehicles is used to get diverse measurements, and Kalman filter is applied to smooth positioning results. Extensive evaluations, via trace-based simulation, confirm that (i) fixing rate of positions can be much improved. (ii) Horizontal positioning error can be greatly reduced, nearly by one order compared with off-the-shelf receivers, by almost half compared with RSSI-based method, and can be reduced further to about 80 cm when vehicle transmission period is 100 ms and Kalman filter is applied. Generally, positioning performance increases with the number of available vehicles and their transmission frequency.

Two of the most critical requirements for the development of license plate recognition (LPR) systems are a comprehensive database of license plate images and a testing procedure to evaluate the system. This study addresses both issues through the LPR system test (LPRST), which establishes the first performance benchmark for the LPR system. There are three important parts in the study: (i) the comprehensive, structured and labelled license plate image database is established, which is openly available to researchers; (ii) the first technical evaluation protocol for the LPR system is established, in which the testing technology has a corresponding relation to the testing image label; (iii) the LPRST is the first open technical performance evaluation of commercial systems, which identify the strengths and weakness of the existing systems, and the future areas of research in the field of LPR.

As the delta-v-based injury models used to evaluate intelligent driving systems are always fitted with European or American crash database, they cannot achieve wide application in those countries where limited in-depth analysed crash data are recorded. An injury model which is based on easily accessible information, is urgently needed. In this study, a deformation depth based injury risk model is proposed to overcome the limitation of delta-v. First, a correlation between the vehicle deformation depth and occupant injury risk is verified from the aspects of retrospective safety assessment and stiffness cluster analysis using German in-depth accident study and national automotive sampling system–crashworthiness data system. Furthermore, injury risk-deformation functions are regressed for different stiffness clusters using the crash data. The fitting accuracy reaches 97%, higher than the existing literature. A novel safety benefit assessment simulation platform is built with the regressed injury risk model. Based on this platform, an autonomous emergency braking system is evaluated. Only 1% error of the safety benefit exists between the proposed model and the delta-v based one.

A tandem intersection is an unconventional intersection design that possesses the important property of increasing the traffic capacity using pre-signals and sorting areas. However, the operational efficiency of the lanes in the sorting areas may be affected by driver unfamiliarity and confusion. This study evaluates the effect of the tandem control on the saturation flow rate using field data. Statistical analyses were conducted to identify the difference in the saturation flow rate of the approach lanes when the tandem control was open and closed. A saturation flow rate adjustment model for tandem control was established in accordance with three factors: unequal distribution of traffic, red light violations at the pre-signal, and incomplete discharge of vehicles at the sorting area. The results indicate that the tandem control decreases the saturation flow rate of the approach lanes in the sorting area. Most of the observed reductions are caused by the first factor, i.e. the unequal distribution of traffic. The other two factors, red light violations and incomplete discharge, can be controlled by using appropriate traffic signs, markings, and signal timing design.