Road safety impacts of Connected and Automated Vehicles

Connected and automated transport systems (CATS) are expected to be introduced in increasing numbers over the next decades. Moreover, they are expected to have considerable impacts on mobility, safety, the environment and society as a whole. One of the aims of LEVITATE is to forecast these impacts. This article collects the impact of CATS on road safety which has been briefly presented during the last webinar of LEVITATE, as well. Moreover, the article written by Wendy Weijermars (SWOV), Andreas Hula (AIT), Amna Chaudhry (LOUGH), Sasa Sha (LOUGH), Rins de Zwart (SWOV), Celina Mons (SWOV) and Hitesh Boghani (LOUGH) further presents the specific impacts on road safety for the individual sub-use cases and communicates how these expected impacts can be quantified. Conclusions suggest that in normal circumstances, CAVs are expected to have a lower crash rate than human driven vehicles; CAVs make less errors than human drivers, are assumed to respect all traffic rules and are expected to have lower reaction times and less variability in driving behaviour.

Curious about further conclusions drawn in the article? Read the entire article: LEVITATE: Road safety impacts of Connected and Automated Vehicles.
Note: This article has been updated in July 2021 including further research results derived from the sub-use case.

LEVITATE: applying mesoscopic activity chain simulation

The present article focusses on the application of an agent-based mobility simulation model for the city of Vienna which utilizes activity chain descriptions of the simulated agent’s daily objectives. This is done in the context of the goals of project LEVITATE.

It entails a brief description of the model method, the specific  features of the model, the expectable and intended output of the model, its general assumptions as well as detailsmon two specific areas of interest within the project objectives, namely automated urban transport and road use pricing.

LEVITATE webinar on road safety assessment of CATS

On 27 May, the 4th LEVITATE webinar took place with more than 80 participants to share the project’s research results about the impact of automated vehicles on road safety.

The interactive webinar was introduced by Andrew Morris (Loughborough University) who shared general information about Levitate with the audience. The webinar was moderated by Wendy Weijermars (SWOV) while Rins de Zwart (SWOV), Amna Chaudry (Loughborough University) and Andreas Hula (AIT) shared their research results on road safety impacts of connected and automated vehicles (CATS). Based on a poll launched during the webinar, most participants expect a considerable improvement in road safety with the introduction of CATS, but they do not expect that all serious crashes can be prevented. LEVITATE’s two-steps approach in the estimation of impacts determines that which ways the road safety is impacted by the development of CATS, and as a second step, the project tries to quantify these impacts as far as possible with the help of literature review and conducting interviews with stakeholders. The audience was proactive and asked several questions to the panelists which has been answered live or written during the event.

Curious about the presented impacts on road safety and the outcomes of the discussion? Watch the recorded webinar here:

Delphi method to measure impacts of automated vehicles

The Delphi method is a process used to arrive at a collective, aggregate group opinion or decision by surveying a panel of experts. This concept was developed by the RAND Corporation for the military in order to forecast the effects of new military technology on the future of warfare, and then continued to make multiple practical applications of this method (Dalkey & Helmer, 1963). The Delphi methodology is based on a repetitive interview process in which the respondent can review his or her initial answers and thus change the overall information on each topic (Hsu & Sandford, 2007). This presupposes that the participants will be willing to not only give answers on the topics but also to repeat the interview in possibly more than two cycles.

Within LEVITATE, the Delphi method is used to determine all impacts that cannot be defined by the other quantitative methods (traffic microsimulation/system dynamics). Initially, a long list of experts were identified for each use case, and contacted via an introductory mail asking them to express the willingness of participation. Those who responded positively (70 experts) participated in the main Delphi process.

If you want to know more about the Delphi studies conducted in the frame of Levitate, including the list of impacts and result introduction to the Policy Support Tool, read our article from the National Technical University of Athens.

LEVITATE participates in 3rd annual meeting of Robomobile Life

The 3rd annual meeting of the Robomobile Life initiative will take place on 19 and 20 May to explore the role that local authorities (cities, regions) can play in steering the emergence and evolution of automated mobility. LEVITATE will contribute to this workshop along with the Polis secretariat.

Initiated by the French Ministry for Ecological Transition in 2017, the “robomobile life” series of  foresight workshops aims to foster thinking, exploration, questioning, reflection and debate on all subjects and matters related to robomobility. The workshops seek to create a better understanding of the key issues and long-term choices that decision-makers from both the public and private sectors may have to deal with in the coming years and decades, here in France and abroad.

The decade 2021-2030 could see the deployment of the first transport services provided by automated vehicles. This implementation will be gradual, targeted and regulated and should be driven by genuine use cases that meet mobility needs that are poorly or insufficiently covered by the existing offer. These first stages of putting transport automation into service in real conditions will be decisive.

  • To what extent can a city choose the robomobile model that suits it best?
  • What influence will local/regional government stakeholders have on the different socio-technical models associated with automation?
  • What will be the the rules of the game and who will set them?

This third annual meeting aims to provide reflection on these questions. This event has been designed to give participants the possibility to compose their own tailor-made programme. The sessions are independent of each other. An English-speaking track is open to all international attendants and all plenaries will be translated from French to English.

Registration

For further information and to register »

Monetary impacts of connected and automated vehicles

Deliverable D3.3 of LEVITATE deals with converting impacts of connected and automated vehicles to monetary terms. Converting impacts to monetary terms is needed to include them in cost-benefit analyses.

One of the objectives of LEVITATE is to develop a tool for performing cost-benefit analyses of policies designed to maximise the societal benefits of introducing connected and automated vehicles. This tool will be part of the Policy Support Tool developed by LEVITATE. Can everything be converted into monetary terms when it comes to the impact of automated vehicles?

Read Rune Elvik’s article from the Norwegian Institute of Transport Economics »

 

Backcasting city dialogues: Feasible paths of interventions – the case of Vienna

The impact of connected and automated transport systems (CATS) in several areas also has strong implications on a very central question of urban development: Given a certain vision based on a set of quantified policy goals for a city or a region, which supporting role take recommended policy interventions related to CATS to achieve that vision? This article provides a short overview of the backcasting approach applied in LEVITATE that addresses this question.

From a cities’ perspective the advent of connected and automated vehicles (CAVs) is not a strategic goal by itself. Rather, they are welcome if they are able to contribute to the defined smart city goals and have to support a livable city. Improvements in road safety or reductions in the demand for public parking space are promising candidates for such supported goals, with quantitative investigation of impacts currently ongoing in the project. But there are some other impact areas where an increasing market penetration of CAVs (without specific regulations) might be in conflict with the strategic goals of a city: empty AVs avoiding parking fees might increase congestion; the attractiveness of AVs might lead to adverse changes in modal split; acceptance of longer driving distances (due to increased comfort and use of travel time for working) might further increase road traffic and promote urban sprawl. It is therefore essential for cities to integrate the full spectrum of related policy interventions into their considerations to prepare for the era of CATS – right from the start. Some positive impacts might be reinforced and accelerated by the appropriate policies, other desired impacts might occur only if a specific combination of policy interventions is applied – with the appropriate timing – and finally, some unwanted negative impacts might be mitigated by corresponding interventions. These causal relationships, however, are not always as simple and intuitive as it might appear at first sight. A lot of interdependencies – as in every complex system extending over different domains – makes it a necessity to apply a formal approach and consider a set of different methodologies that can support cities in their strategic decisions.

Defining a desirable vision in a quantitative way is the essential starting point for the backcasting process. From that vision the idea is to work backwards, via influencing factors (that are impacting the goals and indicators of the vision), to policy interventions which address these factors and thereby contribute towards the vision. Generating this series of logical links is a central part of the process, as it highlights feasible paths of interventions, steering into the desired direction. The steps in this process are explained in more detail and exemplified for the City of Vienna in the following article by the Austrian Institute of Technology.

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 »

Automated freight transport

In this WP7 article, we focus on some logistic concepts enabled by connected automated transport systems (CATS) and what disruptive changes we can expect from them. Freight transport is one of the three use cases in the LEVITATE project, beside urban transport and passenger cars. The overall goal is:

  • to identify how each area of impact (safety, environment, economy and society) will be affected by the introduction and transition of CATS in freight transport,
  • to assess its impacts, benefits and costs,•to test interactions of the examined impactsof freight transport, and
  • to prioritise considerations for a public policy support tool to help authority decisions.

Have a look at the whole article, written by: Bin Hu (AIT), Maria-Cellen Sawas (AIT), Melitta Dragaschnig (AIT), Clovis Seragiotto (AIT) and Marian Ralbovsky (AIT).

Passenger Cars Microsimulation Sub-use Cases Findings

The Work Package 6 (WP6) of LEVITATE considers the specific case of passenger cars which are used across the transport system so forecasting of impact will consider the use on urban, rural and highway infrastructure. Work undertaken in WP6 is based on the methodology developed in WP3 and the scenarios developed in WP4 to identify and test specific scenarios regarding the impacts of CATS on passenger cars. Findings will complement those of WP5 (Urban transport) and WP7 (Freight transport) and feed into the developing of the LEVITATE Policy Support Tool (PST) in WP8. The aim of this WP6 is to forecast short-, medium- and, long-term impacts of automated passenger cars on safety, mobility, environment, economy and society. The objectives of the WP6 are set as follow:

  • To identify how each area of impact (safety, environment, economy and society) will be affected by the transition of passenger cars into connected and automated transport systems (CATS). Impacts on traffic will be considered cross-cutting across the other dimensions,
  • To assess the short-, medium- and long-term impacts, benefits and costs of cooperative and automated driving systems for passenger cars,
  • To test interactions of the examined impacts in passenger cars, and
  • To prioritise considerations for a public toolkit to help authority decisions.

According to Deliverable 3.1, a taxonomy of potential impacts of connected and automated transport systems (CATS) at different levels of implementation can be classified into three distinct categories: direct impacts refer to the operation of connected and automated transport systems by each user; systemic impacts are system-wide impacts on transport; and wider impacts are societal impacts resulting from changes in the transport system such as accessibility and cost of transport, and impacts like accidents and pollution and changes in land use and employment. In order to estimate and forecast these impacts, appropriate assessment methods have been proposed in LEVITATE such as traffic mesoscopic simulation, traffic microsimulation, system dynamics, Backcasting and Delphi panel method.

A stakeholder reference group workshop was conducted to gather views from city administrators and industry on the future of CATS and possible uses (i.e. use cases) of automated passenger cars, named, sub-use cases. Workshop participants suggested a few new use cases for passenger cars. Those include specific detailed parking related sub-use cases and in-vehicle signage. It was emphasised that in order to have a better future of AVs, parking issues would need to be solved. Within WP6, five sub-use cases have been defined as follows:

  • Road use pricing:
    1. Empty km pricing
    2. Static toll on all vehicles
    3. Dynamic toll on all vehicles
  • Automated ride sharing
  • Parking space regulation:
    1. Parking price
    2. Replace on-street parking with public space
    3. Replace on-street parking with driving lanes
    4. Replace on-street parking with pick-up/drop-off parking
  • Provision of dedicated lanes for AVs on urban highways, and
  • Green Light Optimal Speed Advisory (GLOSA).

This article will be focused on the initial findings by applying the traffic microsimulation for sub-use cases, specifically the initial findings of the provision of dedicated lanes for AVs on urban highways and parking price. It noted that all autonomous vehicles are electric and that they used two main driving profiles (Roussou et al., 2019):

  • Cautious: long clearance in car-following, long anticipation distance for lane selection, long clearance in gap acceptance in lane changing, limited overtaking, no cooperation, long gaps, and
  • Aggressive: short clearance in car-following, short anticipation distance for lane selection, short clearance in gap acceptance in lane changing, limited overtaking, no cooperation, small gaps.

Have a look at the whole article, written by Hua Sha (LOUGH), Hitesh Boghani (LOUGH), Amna Chaudhry (LOUGH), Mohammed Quddus (LOUGH), Andrew Morris (LOUGH), Pete Thomas (LOUGH).