Shared Automated Vehicles (SAVs) hold great promise for the future of urban mobility. Automated ride-sharing services are expected to alleviate traffic congestion, reduce traffic emissions, and significantly improve road safety by combining advanced connected and autonomous vehicle (CAV) technology with the ride and/or car-sharing concept. These benefits, however, are highly dependent on the deployment concept of the service and environment including network characteristics, CAV technology, traffic compositions, population acceptance, etc.. This study aims to assess the mobility and environmental impacts of introducing a door-to-door automated ride-sharing (ARS) service under different deployment scenarios. Two calibrated and validated city-scale networks with different characteristics were used: a suburban area in Great Manchester (UK) and a city-centre area in Leicester (UK). An optimisation technique for the vehicle routing problem was developed to efficiently operate ARS at a network-level. The preference of customers for individual and shared rides, Willingness to Share (WTS) was investigated to gain a better understanding of the impact of utilisation choice based on the performance indicators (i.e., delay, travel time, speed, kilometres-driven and emissions). The introduction of ARS was investigated under two deployment scenarios: 1) mixed with conventional human-driven vehicles (HDVs) and 2) mixed with HDVs with varying CAV market penetration rates. Findings suggest that introducing ARS can adversely impact mobility and the environment under mixed traffic, especially in suburban areas, and the benefits of an automated ride-sharing system are highly dependent on WTS. The findings will assist local authorities in formulating automated ride-sharing policies to manage the traffic on roads.@en

Raising parking charges is a measure that restricts the use of private vehicles. With the introduction of connected and autonomous vehicles (CAVs), the demand for parking has the potential to reduce as CAVs may not park at ‘pay to park’ areas as they are able to “cruise” or return home. However, it might not be financially feasible for them to return to their origin if the destination region is far away. Therefore, the question is: how could we develop parking policies in the CAVs era? To determine the best parking strategy for CAVs, four scenarios were tested in this paper: (i) enter and park within the destination area, (ii) enter, drop off, and return to the origin, (iii) enter, drop off, and return to outside parking and (iv) enter and drive around. Since real-world parking demand data for CAVs are not available, a simulation model of the road network in Santander (Spain) was employed to collect data on both CAV operations (e.g., conservative versus aggressive behaviors) and parking choices. Multinomial logistic regression model was used to identify the best parking option for CAVs. Performance indicators such as traffic, emissions, and safety were employed to compare the performance of a range of parking alternatives. It was found that the balanced scenario (i.e., combination of all parking choices) performs better with the greatest change in delay (around 32%). With 100% CAV market penetration, traffic crashes were reduced by 67%. This study will help local authorities formulate parking policies so that CAVs can park efficiently.@en

This study aims to quantify the safety impacts of Connected and Automated Vehicles (CAVs) in mixed traffic environments in three calibrated and validated urban road networks including Manchester (UK), Leicester (UK), and Athens (GR). Road safety impacts were investigated through traffic microsimulation techniques combined with application of the Surrogate Safety Assessment Model (SSAM). Behaviours of CAVs were modelled based on a comprehensive literature review and discussions with experts. The estimated number of conflicts, extracted from the microsimulation and SSAM approach, were converted to the number of crashes by using a probabilistic method. Results revealed a significant improvement in case of passenger car fleet scenarios in all three test networks. However, the mixed fleet scenarios involving freight and public transport vehicles showed added complexities due to non-homogeneity in vehicle characteristics. In this context, limitations of microsimulation and SSAM have also been identified while recommendations have been made for methodological improvements. Overall, the findings of this research provide several useful insights by using a practical procedure to estimate safety impacts under mixed traffic environment. Future research and field trials should focus on addressing the challenges of maintaining safety in the early and transition phases of the deployment of CAVs.@en