Periodic Reporting for period 2 - Levitate (Societal Level Impacts of Connected and Automated Vehicles)
Período documentado: 2020-06-01 hasta 2022-05-31
A full Technical Report of the project is available at www.levitate-project.eu.
Increasing deployment of connected mobility technologies and the prospect of highly automated vehicles in use has raised the public expectations of major benefits to society. Safety, mobility, transport efficiency and wider societal benefits are all expected once CAVs become widespread. As well as benefits that derive directly from the deployment of these technologies there are also the impacts of services that are enabled by Cooperative, Connected and Automated Mobility (CCAM).
There is very little knowledge about the wider impacts of CCAM on society and on cities that typically will be preparing strategic plans over 20 years ahead. Without guidance from impact forecasts about the changes that may result from increased automation and digital mobility services there may be considerable uncertainty introduced into city planning in relation to the road network, use of active travel and public transport and freight distribution needs.
The Levitate project has therefore been established to develop a new body of evidence to enable cities to identify opportunities where CAVs can support policy goals and also to identify potential negative impacts that cities may need to address through new interventions. The principal objectives are
1 to develop the analytical framework to enable forecasting and backcasting of as wide range of societal impacts;
2 to develop a set of scenarios and baseline conditions for impact assessment;
3 to apply the methods to 15 Use cases and sub-use cases
4 To develop a new web-based tool to enable stakeholders to access the results and customise them to individual cities.
Increasing deployment of connected mobility technologies and the prospect of highly automated vehicles in use has raised the public expectations of major benefits to society. Safety, mobility, transport efficiency and wider societal benefits are all expected once CAVs become widespread. As well as benefits that derive directly from the deployment of these technologies there are also the impacts of services that are enabled by Cooperative, Connected and Automated Mobility (CCAM).
There is very little knowledge about the wider impacts of CCAM on society and on cities that typically will be preparing strategic plans over 20 years ahead. Without guidance from impact forecasts about the changes that may result from increased automation and digital mobility services there may be considerable uncertainty introduced into city planning in relation to the road network, use of active travel and public transport and freight distribution needs.
The Levitate project has therefore been established to develop a new body of evidence to enable cities to identify opportunities where CAVs can support policy goals and also to identify potential negative impacts that cities may need to address through new interventions. The principal objectives are
1 to develop the analytical framework to enable forecasting and backcasting of as wide range of societal impacts;
2 to develop a set of scenarios and baseline conditions for impact assessment;
3 to apply the methods to 15 Use cases and sub-use cases
4 To develop a new web-based tool to enable stakeholders to access the results and customise them to individual cities.
On conclusion of the project the Levitate team had identified the most significant CCAM services with potential to impact on cities at a societal level and these formed the basis of the subsequent impact assessments. The services include automated public transport systems, infrastructure-based technologies such as dedicated CAV lanes or GLOSA, Automated ride-share systems as well as freight applications such as automated delivery and load consolidation.
Following consultation with the city stakeholders, the Levitate team had identified the 23 most important dimensions where impact forecasts were of most value. These were classified into three basic groups (Figure 1)
Direct impacts – observed during a trip
Systemic impacts – observed at network level
Wider impacts – changes observed outside the transport system
A toolbox of forecasting methods was developed that would identify the most significant (Figure 2). To ensure consistency the basic constraints and assumptions were preserved as much as possible across the impact forecasts. Special consideration was given to safety impacts and a new method was developed and utilised to estimate crash reductions with CCAM systems.
There are no production automated vehicles in general use and the operating parameters that would be reflected in a car following model are not known. Therefore, a set of models was developed to simulate first generation vehicles that were less capable than the human (greater safety margin, longer forward headway, lower acceleration) and second generation which exceeded human abilities.
Calibrated network models covering large areas of the cities Manchester, Vienna, Athens, Santander, and Leicester were used in simulations to ensure the impact forecasts were as realistic as possible. Further analysis examined the levels of generalisability of the results to other cities.
On conclusion of Levitate over 3000 individual impact forecasts were made across the 14 sub-use cases, (Figure 3) each of which incorporated several implementation approaches. Six of these were examined in further detail as case studies. A full documentation of each set of forecasts, together with supporting data and synopses was prepared and incorporated in a new Policy Support Tool (ccam-impacts.eu) (PST).
The PST is a major output of the Levitate project. It incorporates all of the project results, deliverables and other documents. Freely available, it most notably enables city stakeholders and others to access the results and to customise them to their own scenarios and policy goals (Figure 4). It incorporates a new cost-benefit analysis tool that enables city stakeholders to estimate cost efficiency for any selected set of interventions in a customisable manner.
Many dissemination activities have been conducted including Webinars (9), workshops and presentations (36), news letters (12), exhibitions (10), conference papers (12) and academic publications (22).
Following consultation with the city stakeholders, the Levitate team had identified the 23 most important dimensions where impact forecasts were of most value. These were classified into three basic groups (Figure 1)
Direct impacts – observed during a trip
Systemic impacts – observed at network level
Wider impacts – changes observed outside the transport system
A toolbox of forecasting methods was developed that would identify the most significant (Figure 2). To ensure consistency the basic constraints and assumptions were preserved as much as possible across the impact forecasts. Special consideration was given to safety impacts and a new method was developed and utilised to estimate crash reductions with CCAM systems.
There are no production automated vehicles in general use and the operating parameters that would be reflected in a car following model are not known. Therefore, a set of models was developed to simulate first generation vehicles that were less capable than the human (greater safety margin, longer forward headway, lower acceleration) and second generation which exceeded human abilities.
Calibrated network models covering large areas of the cities Manchester, Vienna, Athens, Santander, and Leicester were used in simulations to ensure the impact forecasts were as realistic as possible. Further analysis examined the levels of generalisability of the results to other cities.
On conclusion of Levitate over 3000 individual impact forecasts were made across the 14 sub-use cases, (Figure 3) each of which incorporated several implementation approaches. Six of these were examined in further detail as case studies. A full documentation of each set of forecasts, together with supporting data and synopses was prepared and incorporated in a new Policy Support Tool (ccam-impacts.eu) (PST).
The PST is a major output of the Levitate project. It incorporates all of the project results, deliverables and other documents. Freely available, it most notably enables city stakeholders and others to access the results and to customise them to their own scenarios and policy goals (Figure 4). It incorporates a new cost-benefit analysis tool that enables city stakeholders to estimate cost efficiency for any selected set of interventions in a customisable manner.
Many dissemination activities have been conducted including Webinars (9), workshops and presentations (36), news letters (12), exhibitions (10), conference papers (12) and academic publications (22).
Policy Support Tool
The PST is the primary result of the Levitate project and is directed towards policymakers at city level. It provides the entry point for direct access to all of the results of the project and is the first publicly available tool that provides a wide range impact forecasts that are customisable for individual cities. The backcasting function, that enables cities to identify the most relevant interventions to achieve their policy goals, is also unique, operating in a completely integrated manner with the forecasting modules. The PST supports policymaking across city administration functions and provides a mechanism for them to formulate their first responses with a 20-year horizon to the impact of CCAM technologies and services as well as to examine the impact of automation on city mobility provision.
Impact forecasts
The forecasts themselves comprise the first systematic set of impact studies of CCAM technologies and services. Developed with a consistent set of underpinning constraints and operational specifications they are the first set of impact forecasts, covering a wider spread of impact dimensions than any other while addressing the most pressing concerns of cities.
Implications for policymaking
The main conclusions and recommendations from the forecasts and analyses undertaken in the project are:
• Future CCAM services and technologies may have a mixture of positive and negative societal impacts. Policy measures should be based on a full impact assessment in order to identify improved opportunities to achieve city policy goals or set measures to mitigate negative impacts.
• In the early phases of CAV deployment with a mixed fleet of automated vehicles and vehicles with human drivers in the transport system can result in marginal decrease and in some cases increased conflicts and collisions.
• As advanced automated vehicles form the largest part of the vehicle fleet, it is anticipated that crash rates will reduce substantially below the current levels.
• Early generations of automated vehicles, which operate below the level of human driven vehicles are expected to reduce the capacity of cities for traffic.
• Several policy measures that have been examined can bring positive environmental impacts; however, powertrain electrification has an overwhelmingly larger impact on emissions compared to the studied policy interventions
• Commonly any improvement in passenger car mobility through the increased automation will reduce the use of public transport and active travel.
• The Levitate project has shown the benefits of conducting detailed impact forecasts based on a broad spectrum of modelling methods. The methods can be applied to other CCAM interventions and can also be adapted to evaluate real-world trials of CCAM services and technologies.
The PST is the primary result of the Levitate project and is directed towards policymakers at city level. It provides the entry point for direct access to all of the results of the project and is the first publicly available tool that provides a wide range impact forecasts that are customisable for individual cities. The backcasting function, that enables cities to identify the most relevant interventions to achieve their policy goals, is also unique, operating in a completely integrated manner with the forecasting modules. The PST supports policymaking across city administration functions and provides a mechanism for them to formulate their first responses with a 20-year horizon to the impact of CCAM technologies and services as well as to examine the impact of automation on city mobility provision.
Impact forecasts
The forecasts themselves comprise the first systematic set of impact studies of CCAM technologies and services. Developed with a consistent set of underpinning constraints and operational specifications they are the first set of impact forecasts, covering a wider spread of impact dimensions than any other while addressing the most pressing concerns of cities.
Implications for policymaking
The main conclusions and recommendations from the forecasts and analyses undertaken in the project are:
• Future CCAM services and technologies may have a mixture of positive and negative societal impacts. Policy measures should be based on a full impact assessment in order to identify improved opportunities to achieve city policy goals or set measures to mitigate negative impacts.
• In the early phases of CAV deployment with a mixed fleet of automated vehicles and vehicles with human drivers in the transport system can result in marginal decrease and in some cases increased conflicts and collisions.
• As advanced automated vehicles form the largest part of the vehicle fleet, it is anticipated that crash rates will reduce substantially below the current levels.
• Early generations of automated vehicles, which operate below the level of human driven vehicles are expected to reduce the capacity of cities for traffic.
• Several policy measures that have been examined can bring positive environmental impacts; however, powertrain electrification has an overwhelmingly larger impact on emissions compared to the studied policy interventions
• Commonly any improvement in passenger car mobility through the increased automation will reduce the use of public transport and active travel.
• The Levitate project has shown the benefits of conducting detailed impact forecasts based on a broad spectrum of modelling methods. The methods can be applied to other CCAM interventions and can also be adapted to evaluate real-world trials of CCAM services and technologies.