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.@en

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.

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Safety evaluation is a critical aspect through the future stages of automation development. Since there is a lack of historical and generalizable safety data in high levels of Connected and Autonomous Vehicles (CAVs), a possible approach to follow is the microscopic simulation method. Through microsimulation, vehicle trajectories are able to be exported and traffic conflicts to be identified using the Surrogate Safety Assessment Model (SSAM). Therefore, it is crucial to develop techniques in order to analyze conflict data extracted from microsimulation and evaluate crash data aiming to support road safety applications of automation technologies. This paper attempts to propose a safety evaluation approach for estimating crash rate of CAVs through microsimulation. For this purpose, the city center of Athens (Greece) was modelled using the Aimsun Next software paying attention to the calibration and validation of the model using real data of traffic characteristics. Moreover, different scenarios were formulated concerning different market penetration rates (MPRs) of CAVs and two fully automated generations (1st and 2nd generation) were simulated for modelling them. Subsequently, the SSAM software was used in order traffic conflicts to be identified and then converted to crash rate. Analysis of the outputs along with traffic data and network geometry characteristics were then conducted. The results indicated that in higher CAV MPRs, crash rates will be significantly lower as well as when the following-vehicle in the occurred conflict is a 2nd generation CAV. Lane change conflicts caused the highest crash rates compared to rear-end conflicts, which presented the lowest rates.

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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

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

Automation in urban freight transport is an important milestone for city logistics, but it will most likely be challenging due to the complex traffic situations. The aim of the present paper is to provide an insight in the impact assessment method used and the results related to parcel delivery in Vienna. While the parcel volume is soaring due to the popularity of e-commerce–and especially accelerated by COVID, cities are thinking about the future delivery system. Automation and consolidation are expected to bring disruptive changes to the system we know today. By applying analytical methods, we show which impacts at what magnitude we may expect from the changes brought by automation in freight transport. We consider the direct impacts consisting of fleet size, freight mileage and fleet operation costs, as well as the wider impacts consisting of parking space, public health and road safety.@en

New technologies in the transport sector promise to bring about certain benefits, for example related to mobility or safety, but are also expected to cause negative side effects which are in conflict with strategic city objectives. These might be mitigated by appropriate policy interventions that are designed carefully and timely. The Horizon 2020 research project LEVITATE has investigated multiple impacts of connected and automated transport systems, using an integrated multi-method approach, ranging from microscopic and mesoscopic simulations to system dynamics and Delphi panels. In particular, the system dynamics model described in this paper served to assess several systemic and wider impacts that were found difficult or even impossible to assess with the other methods. The main parts of the model are a population sub-model, a simplified traffic demand model distinguishing between three modes of transport, and a model for the use of public space. In order to calibrate the model and get results that are consistent with those of other methods, the input and output data of those simulations have also been incorporated into the system dynamics model. By means of selected example impact variables – modal split, demand for parking space and average commuting distance – the obtained effect of increasing automation as well as of several policy interventions is demonstrated.@en

Connected, Cooperative and Automated Mobility (CCAM) are expected to be introduced in increasing numbers over the next decade based on the rapidly developing capability of modern technologies. The need for policies around the introduction of CCAM is starting to arise, based on the evaluation of the likely impact of different technologies, with the aim to capture the benefits of automation and ensure that new technologies contribute to wider policy objectives. The Horizon 2020 Levitate project aims to investigate the potential short, medium and long term impacts of CCAM, through an innovative multi-disciplinary impact assessment methodology, which will be incorporated within a new web-based policy support tool to enable city and other authorities to forecast impacts of CCAM on urban areas. The aim of the present paper is to provide an insight on the development of the Levitate Policy Support Tool (PST), the use cases, parameters and impacts considered and the methodologies applied for the estimation of relationships and impacts of connected and automated transport systems. This policy support tool will comprise a knowledge and an estimator module and will include forecasting and backcasting systems providing estimates for different types of impacts and allowing comparative analyses. The Policy Support Tool will integrate the methodologies and findings of the Levitate project, in order to develop an overall framework for the assessment of impacts, benefits and costs of CCAM for different automation and penetration levels and on different time horizons, as well as a public toolkit and a decision support system allowing the testing of various policy scenarios on the basis of the needs of relevant stakeholders.@en

Rapid technological advances leave limited margins for the preparation of cities to receive Connected, Cooperative and Automated Mobility (CCAM). The LEVITATE project endeavours to develop an open access web-based Policy Support Tool (PST), that will provide decision makers at all levels with access to LEVITATE methodologies and results. The aim of the PST is to consolidate the outputs of different methods into an overall framework for the assessment of impacts, benefits and costs of CCAM, for different automation and penetration levels and on different time horizons. The PST comprises two modules: the Knowledge and the Estimator module, which includes a forecasting and a backcasting sub-system. The present research provides an insight of the PST, by presenting the studied automation use cases, parameters and impacts of CCAM, the applied methodologies and the online tool.@en

Background: Recent technological advancements in automated vehicles are becoming more popular, especially in developed and emerging markets. These changes are bringing new challenges for the drivers, planners, engineers, and manufacturers. Due to several crash incidents that occurred over the past years involving driverless vehicles, safety concerns of potential users have been raised. One key aspect is to design the system so that the user experience can be improved, especially in terms of safety and reliability. User's viewpoint can also be used to collect feedback about the system based on their experience and interaction with the technology. Public opinion surveys have been frequently used to capture such feedback. In this context, this recently completed survey study investigates the opinion, trust, and expectations of potential users in Belgium, particularly on driverless vehicles' safety performance. Methods: Various safety critical driving scenarios were designed to obtain a more detailed understanding of the public's safety perceptions. Participants were asked to provide their assessment on automated driving systems' safety performance under those scenarios in a stated preference survey. Results: Total 360 people participated in the survey. Those who provided demographic information included 64.1% male and 34.8% female, out of which almost 70% had bachelor’s level education or above. Several demographics such as age, education level, income level, owning a car, driving experience, frequency of driving, and previous experience with automated functions, were found to have a significant association with the public's interest and trust in the safety performance of the system. Overall, most of the respondents were optimistic regarding the system's reliability for making efficient and safe decisions (e.g., avoiding a potential collision) under various critical traffic scenarios. Driving on motorways was the most trusted scenario (90% agreement) for automated vehicle operation. Situations where respondents indicated concern or lesser trust in the technology included the presence of a large number of vulnerable road users (41%), driving in poor visibility due to weather conditions (41.6%), and driving through work zones (46%). Conclusions: The results offer unique challenges to designers, technology developers, planners, and manufacturers about developing such systems and strategies that would lead to the safe and successful adaptation of future automated vehicles. The willingness to pay for getting an automated vehicle was also correlated with trust in the system's safety performance. The study's findings suggest further exploration of users' understanding, trust, and safety expectations of the driverless vehicle technology for successful deployment and adaptation.@en