Deliverable D7.4 / Effects on Mobility, Efficiency and Environment

The Hi-Drive impact assessment was designed around the research questions on the impacts of highly automated driving (AD) supported by technology enablers in specific scenarios and on a European level after their market introduction, and the contribution of the enablers to these impacts. This deliverable presents the work and outcomes of the impact assessment activities in Hi-Drive, namely those related to mobility, efficiency, environmental, and transport system impact assessments, and details the final methodology used in these assessments.
The mobility impact assessment examined the impact of AD on travel quality, travel patterns, and amount of travel. The assessment relied on survey data from the Hi-Drive Common Questionnaires and the Hi-Drive Global Surveys. The findings suggest that automated driving functions (ADFs) are likely to produce significant impacts on mobility. By enabling users to engage in non-driving-related activities (NDRAs), these systems can enhance travel quality, potentially leading to more frequent and longer journeys.
The efficiency and environment-related research questions were addressed by microscopic traffic simulations, an emissions calculation tool, a formula for assessing tractive energy use, and a scaling-up approach based on available external data.
According to the results, travel time per vehicle kilometre travelled (VKT) increased slightly with increasing ADF penetration rate, and the technology enablers showed potential to mitigate the increases in specific conditions. At the European network level over one year, travel time per VKT increased by 0–3% at ADF penetration rates up to 50%. There was hardly any difference between the scenarios where automated driving operated without technology enablers (Baseline Automated Driving Function, BADF) and those where the same systems were supported by enablers (Enabled Automated Driving Function, EADF).
For the environmental impacts, tractive energy use per VKT decreased with ADF in traffic. For CO2 emissions, impacts were mixed, but technology enablers showed potential to offset CO2 increases and further reduce energy use per VKT. At a European level, at ADF penetration rates of up to 50%, impacts on tractive energy use saw a reduction of up to 15%. For CO2 emissions, the network level impacts were between –1% and 1%.
The transport system impacts were estimated using macroscopic demand modelling and a transport model from the state of Bavaria, Germany. The results indicated changes in modal split to be so small that the impact can be considered negligible. This is due to the increase in passenger car units, which pushes people towards other travel modes, being compensated for by a decrease in perceived travel time that pulls people towards automated vehicles. VKT in Bavaria were estimated to increase with driving automation, especially outside the operational design domain (ODD). The decrease in capacity and the route choice decisions led to an almost 5% increase in VKT for manually driven and automated passenger cars and more than 3% for heavy-duty vehicles when the ADF penetration rate was 50%.
Hi-Drive achieved a comprehensive study by addressing first the impacts on single travellers (mobility) and traffic flow (efficiency and environment), and by integrating these findings into a macroscopic model to assess the impact at the transport system level.