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 the Greater 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 customers' preference for individual and shared rides with Willingness to Share (WTS) was investigated to gain a better understanding of 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.

Self-reported driver behaviour has long been a tool used by road safety researchers to classify drivers and to evaluate the impact of interventions yet the relationship with real-world driving is challenging to validate due to the need for extensive, detailed observations of normal driving. This study examines this association by applying the large UDRIVE naturalistic driving study data involving 96 car drivers, comprising 131,462 trips and 1,459,110 km travelled over a duration of 32,096 hours, to compare individual questions and composite indicators based on the Driver Behaviour Questionnaire with real world driving. Self-reported speed behaviour was compared to the measured values under urban and highway conditions. Generalised Linear Mixed Models were developed to examine the relationships between the observed speed behaviours with DBQ errors and violations scores in conjunction with traffic and environmental factors. Drivers' self-reported data on speed selection seldom aligned with their real-world behaviour and there were no meaningful differences between many of the response categories. The DBQ violations and errors scales showed a highly significant correlation with driving speed indicators however they had a low explanatory power compared to other traffic situational and driving factors. Overall, the study highlights the need to validate self-reported driving data against the accuracy and relevance to real-world driving. Self-reports of driving behaviour have long been a tool in road safety research and evaluation yet responses on speed selection are commonly inaccurate and may have little relation with real-world driving.

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.

On-street parking is a commonly used form of parking facility as part of transportation infrastructure. However, the emergence of connected and autonomous vehicles (CAVs) is expected to significantly impact parking in the future. This study aims to investigate the impacts of on-street parking regulations for CAVs on the environment, safety and mobility in mixed traffic fleets. To achieve this goal, a calibrated and validated network model of the city of Leicester, UK, was selected to test the implementation of CAVs under various deployment scenarios. The results revealed that replacing on-street parking with driving lanes, cycle lanes, and public spaces can lead to better traffic performance. Specifically, there could be a 27–30% reduction in travel time, a 43–47% reduction in delays, more than 90% in emission reduction, and a 94% reduction in traffic crashes compared to the other tested measures. Conversely, replacing on-street parking with pick-up/drop-off stations may have a less significant impact due to increased stop-and-go events when vehicles pick-up and drop-off passengers, resulting in more interruptions in the flow and increased delays. The paper provides examples of interventions that can be implemented for on-street parking during a CCAM era, along with their expected impacts in order for regional decision-makers and local authorities to draw relative policies. By replacing on-street parking with more efficient traffic measures, cities can significantly improve mobility, reduce emissions, and enhance safety.

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.

Growing research attention is focusing on Automated Vehicle (AV) technologies, promising significant safety benefits. An in-depth understanding of human driving will play an important role in determining the most acceptable AV behaviour, supporting passenger comfort and thus the adoption of the technology, but also the optimal prediction of the behaviour of the surrounding traffic. The current study examined 1740 real-world motorway pull-out manoeuvres (pull-out distance, speed differential with the leading vehicle, manoeuvre duration, pull-out comfort zone) under different conditions. The results highlighted the significant impact of the surrounding traffic and the driving characteristics on or before the manoeuvre initiation point, which reflected the overtaking strategy selected. The findings can inform the design of automated overtaking systems that resemble human driving and thus encourage their uptake; in addition, they can assist the intention prediction for lane keeping assistance systems in order to optimise the system's response to cutting in and pull-out manoeuvres.

Future user acceptance will be a requirement for the AVs to accomplish their estimated safety benefits, highlighting the importance of acceptable driving behaviour. This study aims to investigate the parameters that affect the acceptability of highly automated overtaking. 237 respondents participated in a video based online survey, rating different motorway flying overtaking scenarios based on their preferences. The scores were analysed using a variety of methods (statistical tests, Principal Component Analysis, Linear Mixed Models). Long pull-out distances and manoeuvre duration values, as well as lower speeds were preferred by the participants, with some limited impact of the driving situation. Overall, behaviour simulating an average, cautious human driver is likely to positively influence acceptability and suggests the value of further research on context-adaptive automated driving to account for subjective risk perception. These findings can contribute towards user-centred systems that assist or autonomously perform overtaking manoeuvres, supporting their uptake and thus the realisation of their safety benefits.

It is known that parking prices can affect multiple characteristics such as traffic flow, delays, and congestion. Connected and autonomous vehicles (CAVs) do not need drivers and may return to the origin, if necessary, avoiding parking fees. However, if the destination area is not near the origin, it may not be economically viable to return. Hence, in the present study, four scenarios were tested to find the optimal parking strategy: (i) enter and park inside 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. Different parking prices were used to determine the suitable option. The ‘Balanced’ scenario with multiple parking choices was found to be better compared to other scenarios, where the flow and travel distance were moderately (-19 and -26.3%) affected. Emissions were reduced significantly with CAVs.

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.

External human–machine interfaces (eHMIs) have the potential to benefit AV–pedestrian interactions. The majority of studies investigating eHMIs have used relatively simple traffic environments, i.e., a single pedestrian crossing in front of a single eHMI on a one-lane straight road. While this approach has proved to be efficient in providing an initial understanding of how pedestrians respond to eHMIs, it over-simplifies interactions which will be substantially more complex in real-life circumstances. A process is illustrated in a small-scale study (N = 10) to rank different crossing scenarios by level of complexity. Traffic scenarios were first developed for varying traffic density, visual complexity of the road scene, road geometry, weather and visibility conditions, and presence of distractions. These factors have been previously shown to increase difficulty and riskiness of the crossing task. The scenarios were then tested in a motion-based, virtual reality environment. Pedestrians’ perceived workload and objective crossing behaviour were measured as indirect indicators of the level of complexity of the crossing scenario. Sense of presence and simulator sickness were also recorded as a measure of the ecological validity of the virtual environment. The results indicated that some crossing scenarios were more taxing for pedestrians than others, such as those with road geometries where traffic approached from multiple directions. Further, the presence scores showed that the virtual environments experienced were found to be realistic. This paper concludes by proposing a “complex” environment to test eHMIs under more challenging crossing circumstances.

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.

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.

This study aims to investigate the mobility impacts of on-street parking regulations for Connected and Automated Vehicles (CAVs) under mixed traffic fleets. A calibrated and validated network model of the city of Leicester in the UK was selected to test the implementation under various deployment scenarios. The modelling results indicated that replacing on-street parking with driving lanes, cycle lanes and public spaces can potentially lead to better traffic performance (27% to 30% reduction in travel time, 43% to 47% reduction in delays) compared to the other tested measures. The less significant impact of replacement with pick-up/drop-off points is due to increased stop-and-go events while vehicles pick-up and drop-off passengers, consequently leading to more interruptions in the flow and increased delays. The paper provides examples of interventions that can be implemented for on-street parking during the implementation of CAVs for regional decision-makers and local authorities.

The global effort to make automated vehicles a common reality on the roads of the world is intensifying in recent years and the challenges of automated driving are being investigated in ever more detail and variety. Among the foremost promises of using automated vehicles (AVs) in daily commute is their assumed benefit on road safety, which should offer additional safety to the most vulnerable road users (pedestrians, cyclists), as well as other vehicles. In this article we aim to derive a functional relationship (dose-response curve) between the proportion of automated vehicles on the road (penetration rate) and the expected accident numbers/fatalities of interactions with vulnerable road users. Our approach is built upon two fundamental components: Firstly, based on an analysis of current accident causes, we can make a projection of which causes of accidents between cars and vulnerable road users could ideally be mitigated by AVs and which not. Secondly, for the accidents that are not mitigated, we still assume a potential for reduction of accident occurrence and accident severity, based on the assumed reaction time of an automated vehicle, compared to the reaction time of a human driver. Based on statistics, braking distances and reaction times, as well as the power model, we derive an estimate of the potential reductions in accidents and fatalities in the presence of automated vehicles, ultimately expressed as a relationship between the proportion of automated vehicles (penetration rate) and the accident numbers/fa-tality rates of accidents between motorized vehicles and vulnerable road users.

Automation in urban freight transport is an important milestone for city logistics, but it is challenging due to the complex traffic situations. 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. 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. 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.

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.

Introduction: The number of road fatalities have been falling throughout the European Union (EU) over the past 20 years and most Member States have achieved an overall reduction. Research has mainly focused on protecting car occupants, with car occupant fatalities reducing significantly. However, recently there has been a plateauing in fatalities amongst 'Vulnerable Road Users' (VRUs), and in 2016 accidents involving VRUs accounted for nearly half of all EU road deaths. Method: The SaferWheels study collected in-depth data on 500 accidents involving Powered Two-Wheelers (PTWs) and bicycles across six European countries. A standard in-depth accident investigation methodology was used by each team. The Driver Reliability and Error Analysis Method (DREAM) was used to systematically classify accident causation factors. Results: The most common causal factors related to errors in observation by the PTW/bicycle rider or the driver of the other vehicle, typically called 'looked but failed to see' accidents. Common scenarios involved the other vehicle turning or crossing in front of the PTW/bicycle. A quarter of serious or fatal injuries to PTW riders occurred in accidents where the rider lost control with no other vehicle involvement. Conclusions: Highly detailed data have been collected for 500 accidents involving PTWs or bicycles in the EU. These data can be further analyzed by researchers on a case-study basis to gain detailed insights on such accidents. Preliminary analysis suggests that 'looked but failed to see' remains a common cause, and in many cases the actions of the other vehicle were the critical factor, though PTW rider speed or inexperience played a role in some cases. Practical Applications: The collected data can be analyzed to better understand the characteristics and causes of accidents involving PTWs and bicycles in the EU. The results can be used to develop policies aimed at reducing road deaths and injuries to VRUs. (C) 2021 National Safety Council and Elsevier Ltd. All rights reserved.

The assistance and autonomous performance of overtaking manoeuvres can offer significant safety benefits. The impact of driving context on perceived risk emphasises the benefits of using contextual information to adjust the manoeuvring behaviour. This paper follows a mixed approach, addressing two main objectives: identifying factor combinations related to overtaking crashes (objective risk) and exploring their relationship to perceived risk. Factor combinations were extracted from a multi-year dataset, acquired from the UK in-depth study RAIDS (Road Accident In-depth Studies). Selected factors were used to create motorway overtaking scenarios with different manoeuvring behaviour (pull-out distance, manoeuvre duration, speed) and driving context (day/night, overtaking car/truck), while 237 participants assessed their impact on perceived risk through an online survey. The findings highlight the strong impact of manoeuvre characteristics on perceived risk, mediated or intensified by the driving context. Long pull-out distance and short manoeuvre duration time were preferred; under night conditions, short pull-out distances were perceived as riskier compared to daytime, while the opposite effect appeared for high speed, which was considered safer. The results can inform future research on motorway overtaking safety perception and acceptability, as well as the design of systems that assist or autonomously perform overtaking. Specifically, they can be used as guidelines for incorporating context related information to adjust overtaking behaviour according to user preferences and create a positive passenger experience.

The aim of the Capri project is to deliver a pilot scheme for the use of automated and connected passenger transport ‘pods on demand’ (PODs) as a mobility service in ‘campus’ locations such as airports, hospitals, business parks, shopping and tourist centres. These areas may be entirely privately owned or comprise elements of both off-road and public highway usage. Four trials have taken place during the Capri project; the first in an off road site closed to the public, two further on private off-road land that are open to the public, and the fourth trial operating as dual mode with the pod demonstrating use in public areas both off-road and on a privately owned road. Future use cases and development will aim to operate on public highways in addition to private ones. A vehicle running on the public highway has to be approved for such use and must comply with the range of legislation controlling the construction of vehicles for public road use and their subsequent usage on the roads. Overall, one of the key project aims is to assess how the vehicles can be designed and operated safely in compliance with all relevant regulation, in the short term, for the purposes of the trials, and in the longer term to support a commercial environment where vehicles can be manufacture-ready and sold in substantial volumes both nationally and internationally. The first aim of this deliverable is to provide an overview of current legislation that relates to the approval for use of pod style vehicles on public roads and to examine how closely the Capri pod complies with it. The second aim is to look to the future and examine options for design or regulatory change that will enable pod style vehicles to be type approved (necessary for dual purpose or on-road pods) while still ensuring safety. The authors would also like to acknowledge the comprehensive review of regulation related to automated vehicles that is currently being undertaken by the Law Commission. This report does not seek to replicate this review but will draw on relevant aspects where appropriate. Furthermore, the authors note that discussions regarding regulation of automated vehicles are developing rapidly, therefore this report intends only to capture the most accurate information available at the time of writing and does not guarantee future accuracy.

Technical development in the field of vehicle automation is progressing rapidly, enhancing the expectation that connected and automated transport systems (CATS) will be introduced in increasing numbers over the next decade. The foreseen implementation of CATS has raised high expectations in terms of safety, environment, society and economic growth. The Horizon 2020 project Levitate will investigate the potential impacts of CATS, through an innovative multi-disciplinary impact assessment framework and will incorporate the method within a new webbased policy support tool to enable city and other authorities to forecast impacts of CATS on urban areas. The objective of this study is to provide a review of future impacts of CATS, through an extensive targeted review of recent literature on the impacts of connected and automated urban transport, passenger vehicles and freight transport, as well as a comprehensive analysis build on the knowledge gained through existing European level research.