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

Safety impact of dedicated lanes for connected and autonomous vehicles – a traffic microsimulation study

Cooperative, connected and automated mobility (CCAM), in particular, the advancements of CAVs are expected to improve the driving experience, efficiency, and reduce vehicle emission, especially enhancing road safety by removing driver-related errors [1]. It has been recognised that there must be a transition phase in which humandriven vehicles, autonomous vehicles or connected and autonomous vehicles will be operating with mixed traffic for a long period (e.g., [2-6]). Hence, the operation of CAVs in a dedicated lane with an uncomplicated environment has been suggested by many researchers [3,7-8].</p

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

Green Light Optimal Speed Advisory (GLOSA) is a Day 1 C-ITS signage application, enabled by the C-ITS service “Signalised Intersections”. The application utilises traffic signal information and the current position of the vehicle to provide a speed recommendation in order for the drivers to pass the traffic lights during the green phase and therefore, reduce the number of stops, fuel consumption and emissions. The distance to stop, the plans for signal timing and the speed limit profile for the area are taken into account to calculate the speed recommendation displayed to the driver. GLOSA service is provided through ETSI G5 into the on-board computer of the vehicle or via mobile network into a smartphone application. In the era of CAVs, it would be useful for cities, various stakeholders, and transport planners to assess the societal impacts of such an application in an urban area and attempt to evaluate the benefits in relation to the relevant costs.</p

The short-term impacts of cooperative, connected, and automated mobility on passenger transport

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. [...]</p