Examining road safety impacts of Green Light Optimal Speed Advisory (GLOSA) system.

Mobility and environmental benefits of Green Light Optimal Speed Advisory (GLOSA) systems have been reported by many previous research studies, however, there is insufficient knowledge on the safety implications of such an application. For safe deployment of GLOSA system, it is most critical to identify and address potential safety issues in the design process. It can be argued that implementation of GLOSA system can improve safety by reducing traffic conflicts associated with the interrupted traffic flow at signalised intersections. However, more research findings are needed from field and simulation based studies to evaluate the impacts on safety under a variety of real-world scenarios. As part of the LEVITATE (Societal Level Impacts of Connected and Automated Vehicles) project under European Union's Horizon 2020 Programme, the main objective of this study is to examine the safety impacts of GLOSA under mixed traffic compositions with varying market penetration rates (MPR) of connected and automated vehicles (CAVs). A calibrated and validated microsimulation model (developed in Aimsun) of the greater Manchester area was used for this study where three signalised intersections in a corridor were identified for implementing GLOSA system. An improved algorithm was developed by identifying the potential issues/limitations in some of the GLOSA algorithms found in literature. Behaviours of CAVs were modelled based on the findings of a comprehensive literature review. Safety analysis was performed through processing the simulated vehicular trajectories in the surrogate safety assessment model (SSAM) by the Federal Highway Administration (FHWA). The surrogate safety assessment results showed small improvement in safety with the GLOSA implementation at multiple intersections in the test network only at low MPR (20%) scenarios of CAVs, as compared to the respective without GLOSA scenarios. No or rather slightly lower improvement in safety was observed with GLOSA implementation under mixed fleet scenarios with 40 % or higher 1st Generation or 2nd Generation CAVs, as compared to the respective scenarios without GLOSA. The implementation of GLOSA system was also found to have some impact on the traffic conflict types (although not consistent across all MPR scenarios), where rear-end conflicts were found to decrease while a slight increase was observed in lane-change conflicts.

Network-wide safety impacts of dedicated lanes for connected and autonomous vehicles.

Cooperative, Connected and Automated Mobility (CCAM) enabled by Connected and Autonomous Vehicles (CAVs) has potential to change future transport systems. The findings from previous studies suggest that these technologies will improve traffic flow, reduce travel time and delays. Furthermore, these CAVs will be safer compared to existing vehicles. As these vehicles may have the ability to travel at a higher speed and with shorter headways, it has been argued that infrastructure-based measures are required to optimise traffic flow and road user comfort. One of these measures is the use of a dedicated lane for CAVs on urban highways and arterials and constitutes the focus of this research. As the potential impact on safety is unclear, the present study aims to evaluate the safety impacts of dedicated lanes for CAVs. A calibrated and validated microsimulation model developed in AIMSUN was used to simulate and produce safety results. These results were analysed with the help of the Surrogate Safety Assessment Model (SSAM). The model includes human-driven vehicles (HDVs), 1st generation and 2nd generation autonomous vehicles (AVs) with different sets of parameters leading to different movement behaviour. The model uses a variety of cases in which a dedicated lane is provided at different type of lanes (inner and outer) of highways to understand the safety effects. The model also tries to understand the minimum required market penetration rate (MPR) of CAVs for a better movement of traffic on dedicated lanes. It was observed in the models that although at low penetration rates of CAVs (around 20%) dedicated lanes might not be advantageous, a reduction of 53% to 58% in traffic conflicts is achieved with the introduction of dedicated lanes in high CAV MPRs. In addition, traffic crashes estimated from traffic conflicts are reduced up to 48% with the CAVs. The simulation results revealed that with dedicated lane, the combination of 40-40-20 (i.e., 40% human-driven - 40% 1st generation AVs- 20% 2nd generation AVs) could be the optimum MPR for CAVs to achieve the best safety benefits. The findings in this study provide useful insight into the safety impacts of dedicated lanes for CAVs and could be used to develop a policy support tool for local authorities and practitioners.

Developing personalised braking and steering thresholds for driver support systems from SHRP2 NDS data.

Examining the relationships between the factors associated with the crash development enabled the realisation of driver support systems aiming to proactively avert and control crash causation at various points within the crash sequence. Developing such systems requires new insights in personalised pre-crash driver behaviour with respect to braking and steering to develop crash prevention strategies. Therefore, the current study utilises Strategic Highway Research Program 2 Naturalistic Driving Studies (SHRP2 NDS) data to investigate personalised steering and braking thresholds by examining the last stage of a crash sequence. More specifically, this paper carried out an in-depth examination of braking and steering manoeuvres observed in the final 30 s prior to safety critical events. Two algorithms were developed to extract braking and steering events by examining deceleration and yaw rate and another developed and applied to determine the sequence of the manoeuvres. Based on the analysis, thresholds for detecting emerging situations were recommended. The investigation of driver behaviour before the safety critical events, provides valuable insights into the transition from normal driving to safety critical scenarios. The results indicate that 20% of the drivers did not react to the impending event suggesting that they were not aware of the imminent safety critical situation. Future development of Advanced Driver Assistance Systems (ADAS) can focus on individual drivers' needs with tailored activation thresholds. The developed algorithms can facilitate driver behaviour and safety analysis for NDS while the thresholds recommended could be exploited for the design of new driver support systems.