Can the impacts of connected and automated vehicles be predicted?

A huge research effort is going on in order to develop connected and automated vehicles. Small-scale trials of automated vehicles in real traffic are already taking place. Can the societal impacts of a transition to fully connected and automated vehicles be predicted?

The answers to the question is that many of these impacts depend on the policies implemented to regulate the introduction of connected and automated vehicles. This applies particularly to two of the impacts that are difficult to predict: whether vehicle automation will be associated with a transition to electric vehicles, and whether it will be associated with a transition to shared mobility.
It is more likely that automated cars will be electric than that they will have combustion engines. However, to make a transition to electric cars more likely and speed it up, policies favouring electric cars may be necessary. Norwegian experience shows that a transition to electric cars can be stimulated by public policy.

Studies (e.g. Clayton et al. 2020) consistently show that individual use of automated cars is preferred to shared use. If the introduction of connected and automated cars is left to the market, it is likely that individual car ownership will continue at current rates. In that case, traffic is likely to increase, as the generalised cost of travel will be lower in automated cars than in manual cars, chiefly because the value of travel time savings is likely to become lower. Travel time is less burdensome and less wasted if it can be used to work or relax. An increase in traffic will reduce the benefits of connected and automated cars in terms of less congestion, fewer accidents and less emissions.

If this prediction is accepted, policies aimed at maximising the societal benefits of connected and automated cars may, perhaps paradoxically, need to counteract some of the private benefits of these cars. Experience shows that whenever transport becomes cheaper and more convenient, the demand for it increases. In economic terms, the societal benefit of an increase in travel demand is the increase in consumer surplus associated with it. However, as noted, an increase in travel demand increases the external impacts of travel in terms of congestion, accidents and pollution. Estimates of impacts made in LEVITATE suggest that even if there is an increase in traffic volume, there will still be a net gain in travel time, a reduction of accidents and a reduction of pollution. While the reductions are smaller than they would have been without increased traffic volume, they are not eliminated. Thus, all potential impacts remain favourable. In view of this, it is unlikely that policy makers will introduce controversial and often unpopular measures like road pricing or parking restrictions to curb the growth of traffic.

It is concluded that, at the current state of knowledge, it is predicted that connected and automated vehicles will lead to increased travel demand, but nevertheless reduce travel time, make travel time less wasteful, reduce accidents and reduce pollution, including global warming.

Read the whole paper, written by Rune Elvik »

Advances in Connected and Autonomous Mobility – IEEE ITSC 2020

The 2020 annual flagship conference of the IEEE Intelligent Transportation Systems Society will be held in Rhodes, Greece in September. LEVITATE will contribute to this conference with a special session: Advances in Connected and Autonomous Mobility: From Data to Models, Impacts and Enablers for Adoption.

Recent breakthroughs in technology, digital infrastructure, dynamic mapping and big data computing will transform the way we will plan, undertake, interact, make decisions and use our built environment and transportation infrastructure for the movement of people and goods. Connected and Autonomous Mobility (CAM), enabled by vehicle connectivity and automation, cloud computing, artificial intelligence and Internet of Things (IoT), allows unprecedented capability to collect, exchange and analyze large volumes of data to formulate models and tools for optimal decision making at individual, local and city levels and will, therefore, increasingly be revolutionized our economy and society over the next decade. However, to what extent they will disrupt mobility and transport operations is still a subject of research. CAM services are expected to emerge in various forms affecting different user groups and imposing network-level changes on various urban scales. To this end, understanding public acceptance and the levels of adoption (and respective timing) of emerging technologies, devising novel approaches and modeling tools to replicate mixed and CAV (Connected and Autonomous Vehicles) traffic in large-scale urban networks for impact assessment and identifying large-scale procedures and policies for CAV traffic management are the key factors for successful deployment of CAM services.

This special session aims at:

  1. providing the audience with information about the deployment of state-of-the-art vehicular and transportation technologies to CAM in a smart city context;
  2. discussing the latest advancements, existing data, conceptual and modeling hurdles and challenges in both research and practice (particularly on the selected topics below)
  3. identifying potential research gaps and collaboration opportunities between industry and academia.

A taxonomy of potential impacts of connected and automated vehicles

LEVITATE is currently building tools to help European cities, regions and national governments prepare for a future with increasing levels of automated vehicles in passenger cars, urban transport services and urban logistics. The project is preparing a new impact assessment framework to enable policy makers to maximise the benefits of connected and automated transport systems (CATS) and utilise the technologies to achieve societal objectives.

Deliverable 3.1: A taxonomy of potential impacts of connected and automated vehicles

Recently several reports have been published within the LEVITATE project. Deliverable 3.1, A taxonomy of potential impacts of connected and automated vehicles at different levels of implementation focuses on the identification of potential impacts of CATs and indicators that can be used to measure these impacts.

D3.1 provides an inventory and classification of impacts of CATS at different levels of implementation and on different topics such as road safety, mobility and efficiency, environment, economy and society. A distinction is made between direct, systemic and wider impacts. Direct impacts are changes that are noticed by each road user on each trip; Systemic impacts are system-wide impacts within the transport system; and wider impacts are changes occurring outside the transport system, such as changes in land use and employment.  Furthermore, a distinction is made between primary impacts and secondary impacts.

The report highlights that the actual impacts of connected and automated transport systems are unknown and will remain so for a long time. However, potential impacts can be identified, and preliminary estimates can be developed. Most analysts believe that a wide implementation of CATS will improve road safety and possibly the efficiency of traffic operations. However, the reliability of automation technology is currently unknown, and there will most likely be unforeseen and rare events that we cannot be taken into account. With regards to policy making, the report points out that highly uncertain estimates of impacts may serve as the basis for identifying policy interventions to increase the likelihood that impacts will be in the desired direction (e.g., policies that can help prevent the urban sprawl that CATs are expected to increase).

The findings of this deliverable will be key in the further development of the use cases and provide the foundation for subsequent work to look at short-, medium- and long-term impacts.

You can access all the publications and learn more about the project here.