Analysing the impacts of parking price policies with the introduction of connected and Automated Vehicles

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

Impacts of on-street parking regulations on cooperative, connected, and automated mobility: a traffic microsimulation study

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

How can on-street parking regulations affect traffic, safety, and the environment in a cooperative, connected, and automated era?

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

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

Examining parking choices of connected and autonomous vehicles

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

How can on-street parking regulations affect traffic, safety, and the environment in a cooperative, connected, and automated era?

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

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

Analysing the impacts of parking price policies with the introduction of connected and automated vehicles

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.</p

The short-term impacts of cooperative, connected, and automated mobility on passenger transport, Deliverable D6.2 of the H2020 project LEVITATE

The aim of the LEVITATE project is to prepare a new impact assessment framework to enable policymakers to manage the introduction of cooperative, connected, and automated transport systems, maximise the benefits and utilise the technologies to achieve societal objectives. As part of this work, the LEVITATE project seeks to forecast societal level impacts of cooperative, connected, and automated mobility (CCAM).The aim of this report is to provide an analysis of the short-term impacts, described in Deliverable 3.1 (Elvik et al.,2019), of different passenger car transport sub-use cases (policy interventions). The short-term impacts analysed include travel time, vehicle operating cost, and access to travel. Based on several discussions with the stakeholder reference group (SRG) including city officials and industry professionals, a list of key interventions, termed sub-use cases (SUCs), were selected to be tested through different applicable methods. These include road use pricing (rup), provision of dedicated lanes on urban highways, parking price policies, parking space regulations, automated ride sharing, and green light optimal speed advisory (GLOSA). For assessing the travel time impact, mesoscopic and microscopic simulation as well as Delphi method have been used. The Delphi method was also used to estimate impacts on vehicle operating cost and access to travel. Road Use Pricing was modelled through mesoscopic simulation using the full-scale city-level model of Vienna. All other sub-use cases were analysed through microscopic simulation method using Manchester network for Dedicated Lanes, Automated Ride Sharing and GLOSA, Leicester network for Parking Space Regulations, and Santander model for testing Parking Price Policies.CAVs deployment was tested from 0 to 100% with 20% increments under all applicable sub-use cases. The behaviours of CAVs were defined based on an extensive literature review performed as part of the LEVITATE project. Two types of connected and automated vehicles (CAV) were included in the analysis, 1st Generation CAVs and 2nd Generation CAVs, where 2nd generation CAVs were assumed to have improved driving characteristics and enhanced cognitive capabilities, which will lead to shorter time gaps as compared to the 1st generation CAVs and human-driven vehicles (HDV).Overall, results from the policy interventions tested under passenger transport provided useful insights with regard to their implications and short-term impacts. Regarding the impact on travel time, the findings from different assessment methods were in line for the majority of studied policy measures. The implementation of a static road use pricing strategy in the city of Vienna indicated more consistent benefits due to static pricing with respect to the average travel time, while the impact due to dynamic road use pricing implementation was found difficult to predict due to the added complexity in traffic operation. The responses from majority of the Delphi study participants also indicated similar trends, suggesting that the introduction of city tool policies would positively impact travel time.Findings from the microsimulation results of provision of a dedicated lane for CAVs on urban highways indicated maximum travel time savings under innermost lane configuration and at moderate market penetration rate (MPR) of CAVs (60% HDVs, 40% CAVs). The experts’ opinions in this regard also indicated maximum travel time reduction under the innermost lane placement scenario. Parking management was identified as one of the key areas of interest by SRG. In this regard, various on-street parking space regulations were tested, including replacing parking lanes with driving lanes, cycle lanes, public spaces, pick-up drop-off areas, and removing half of the on-street parking. Results indicated positive impacts on traffic operation when parking spaces were replaced with driving lanes, cycle lanes, and public spaces, compared to replacement with pick-up/drop-off areas and removing half of the parking spaces. In addition to parking space replacement interventions, a parking pricing policy was also tested in the inner-city domain to evaluate the impact of various parking strategies on travel time. Under all the tested parking price schemes, on average, the results showed an increase in travel time with respect to no policy intervention (baseline) scenario. It was identified that the right policy decision on parking pricing is critical in avoiding negative impacts on traffic.Under passenger transport, an automated ridesharing service was also analysed in one of the study networks which showed an increase in travel time due to congestion caused by the empty pick-up trips and circulating behaviour of shared vehicles (using low capacity and secondary roads). It was found that benefits from such services can only be obtained with an increased willingness to share.With regard to connectivity, the Green Light Optimal Speed Advisory system was tested on a busy corridor in the Manchester network with three signalized intersections, sufficiently apart for GLOSA implementation. All CAVs were assumed to be GLOSA equipped. Results exhibited reduction in number of stops and travel time with GLOSA application as compared with No-GLOSA (baseline) scenario. Maximum travel time savings can be achieved when applied at multiple intersections or at corridor level.The findings from Delphi study showed that introduction of automated vehicles are expected to increase operating cost in the short term which can expected to be reduced with higher MPR while access to travel was indicated to progressively increase with increasing MPR of automated vehicles. Automated ride sharing services were foreseen to have a significant impact in reducing vehicle operating costs and increasing access to travel.Overall, the results provide some important messages for city departments of governments to manage potential consequences due to the introduction of CAVs in the transport system. The findings from different policy interventions, tested in this deliverable, exhibit that increasing MPR of CAVs solely may not have positive impacts and the right policy measures are critical for achieving positive impacts (e.g., travel time savings) with the introduction of CAVs. The results also indicate the importance of the transition phase to full fleet penetration of CAVs

Examining parking choices of connected and autonomous vehicles

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