Goals and impacts
Mobility of people and goods is the lifeline of the modern city. In planning for future urban mobility, European cities like Manchester and Vienna have set goals in which future mobility should contribute to a cleaner city environment, to easier, more comfortable, more cost-effective travel within the city, and to a better, more inclusive society with equal travel opportunities for all social groups. ‘Smart mobility’ - where various types of vehicles in the city, such as passenger cars, urban transport vehicles, freight vehicles, are connected to information systems that help them to navigate more efficiently and safely through city traffic – is seen as one of the prime movers of the transition towards smart cities. Within LEVITATE, important goals for future mobility have been identified for the environment, mobility, and for society & economy. A literature study has identified the direct, systemic and wider impacts that smart mobility may have on the city traffic network, and how these impacts are mutually connected.
In LEVITATE, several methods—including a literature study, microsimulation, meso-simulation, system dynamics model, and a Delphi survey—have been used to study the expected impacts of the increasing presence of first- and second-generation automated vehicles in city traffic on the domains of environment, mobility, and society and economy (see Appendix A). The major studied impacts in these domains include for example congestion, emissions, energy efficiency, access to travel, modal split, total kilometres travelled, parking space, road safety, public health, vehicle operating costs.
Within LEVITATE, first-generation automated vehicles have been defined as vehicles with limited sensing and cognitive ability, long following gaps, earlier anticipation of lane changes than human driven vehicles and longer time in give way situations, whereas second generation automated vehicles have been defined as having advanced sensing and cognitive ability, data fusion usage, confident in taking decisions, small following gaps, earlier anticipation of lane changes than human driven vehicles and less time in give way situations (Roussou et al., 2021b). The technical definition of vehicle parameters describing these two generations are given in Appendix B.
LEVITATE has also estimated the additional impacts of specific policy interventions (termed ‘sub-use cases’) such as automated urban shuttle services, or hub-to-hub freight transport, on these domains. These estimated effects are presented as effects over and above the effect resulting from the increasing presence of automated vehicles anticipated as part of Cooperative, connected and automated mobility (CCAM).
Approach to summarizing LEVITATE results
The goal of this Deliverable is to summarize the more detailed results presented in D5.2-D5.4 in order to provide an overview of the main expected trends for each impact. To quantify the impacts expected from an increasing penetration rate of connected and automated vehicles in the total vehicle fleet as well as the implementation of an automated urban shuttle system (AUSS), four methods (Appendix A) were used: microsimulation, mesosimulation, system dynamics and Delphi. Within each method, AUSS sub-use cases were defined and quantified for a number of sub-use case scenarios. To summarize these results, for each sub-use case an average (where applicable) is taken of its scenarios in order to derive an average percentage change for the respective sub-use case. For some impacts both the Delphi method and either micro- or meso-simulation have been used; for these impacts, only the simulation results are reported in this synthesis due to these being considered the more rigorous methods within LEVITATE.
In this synthesis the impacts are presented in overview tables that distinguish between a (natural) baseline development, i.e. the expected development of impacts as the share of automated vehicles as proportion of all traffic increases to 100%, and an intervention-based development, i.e. the expected development of the same impact when both the intervention (or sub-use case concerning automated urban transport) and increasing automated vehicles in total traffic are at work. Thus the percentage changes are reported across increasing market penetration rates of CAV throughout the entire vehicle fleet in the urban network, as used throughout LEVITATE.
The impacts are expressed as a percentage change from the first stage of the baseline scenario which starts out at 0% penetration of automated vehicles and a starting value taken as the neutral reference point (zero percentage change). Thus, the development of impacts (expressed as percentages that indicate decrease or increase from the initial impact) under the baseline indicates the sole expected effect of increasing CAV penetration in total traffic. The development of impacts under the intervention-based condition indicates the expected effect of the combination of the intervention (introduction automated urban transport) and the growing automation.
Since the automated urban transport – or automated urban shuttle services - studied in this report only make up a small part of the total traffic network, the combined impacts of the intervention and the growing automation on the total network can be expected to make a relatively small difference compared to the baseline development of increasing automation. Thus, in the studied SUCs it is the background changes in total traffic that tend to be the dominant influence and the intervention has a relatively small impact.
Findings
With the reservation in mind that the estimated (percentage) impacts are very much dependent upon specific models, assumptions and studied city networks and have limited generalisability, the following summary of main findings for WP5 can be presented:
- Increasing penetration levels of connected and automated vehicles in the urban city area are estimated to have positive impacts on the environment (less emissions, higher energy efficiency), on society and economy (improved road safety, public health, and lower vehicle operating costs) and on mobility (more access to travel and less congestion).
- For the road safety, emissions, and congestion impacts substantial positive effects have been estimated at relatively low levels of CAV penetration (emissions - 17-40% reduction; road safety - 9% to 10% improvement - and congestion - 11 to 12% reduction at 20% to 40% CAV). These initial positive impacts increase significantly (double or even triple) at higher CAV penetration levels where 60 to 100% vehicles are automated.
- For a number of other impacts, such as access to travel, equal accessibility of transport and public health, positive effects have been estimated at higher levels of automation (60% of vehicles automated) and these impacts remain stable at higher stages of automation (60 to 100% of all vehicles automated); at penetration levels of 60% automated vehicle, access to travel substantially improves (19%), equal accessibility of urban transport improves (14%), and general public health in the city improves (4%).
- The effects on parking space and the kilometres travelled present us with more complexity and uncertainty.
- For parking different methods have estimated different trends for the demand for parking space under growing vehicle automation. Regarding demand on parking space the Delphi method predicts less demand with increasing levels of CAV and no AUSS. The additional impact of on demand AUSS is small, even suggesting that it offsets some of the positive CAV effects. System dynamics on the other hand predicts a growing demand for parking space with increased levels of CAV penetration. On demand AUSS is estimated to slightly reduce this demand.
- For mobility effects (expressed as kilometres travelled) the apparent increase of kilometres travelled under various market penetration rates could be assumed to be a favourable impact since it signifies both an increase of completed trips and decreased delay time in the city network. However, at the same time a possible downside may be that more kilometres travelled use up more energy, may shift trips from public transport to private vehicles, may cause more exposure to traffic safety risks, and may use up more public space.
- As the penetration levels of first- and second-generation CAVs increase, the point-to-point automated urban shuttle service (AUSS) is estimated to generate further benefits for the city in terms of an additional increase in energy efficiency (5 to 6% improvement on the baseline), better access to travel (5 to14 % improvement on the baseline), further improvement of the public health (2 to 8 % improvement on the baseline), and a further lowering of vehicle operating costs (5 to 11 % improvement on the baseline).
- The point-to-point and on-demand automated urban shuttle service (AUSS) have no apparent additional effect on the amount of travel and the kilometres travelled in network. The primary effects are the result of increased penetration levels of first and second generation CAVs.
- Compared with baseline, the on-demand sub use case of automated urban transport is associated with shorter travel time, better access to travel and less congestion. The experts’ expectations for additional benefits of on-demand AUSS in terms of access to travel, parking space, public health, shared mobility, and vehicle operating costs, are below the expectations for point to point AUSS.
- The AUSS scenarios regarding dedicated shuttle lanes and varying on-demand fleet capacities had little additional impact on the quantified results
Strengths and limitations of LEVITATE
The following observations pertain to strengths and limitations of research within WP5 LEVITATE. A potential strength of the LEVITATE project is that both smart city transport policy interventions and the associated impacts have been selected by a diverse group of stakeholders. A wide variety of impacts were studied at the same time and the project tried to capture interdependencies. The best available methods - microsimulation, mesosimulation, Delphi, and other complementary methods such as system dynamics and operations research - were used to study and quantify the expected impacts of mobility interventions intended to support CAV deployment and sustainable city goals. Within LEVITATE project these impacts provide essential input for developing a practical Policy Support Tool for city policy makers. Finally, a strong point of LEVITATE is that a consistent framework for assessing impacts across the project was used so that impacts may be more comparable across all use cases.
Concerning limitations of the present LEVITATE studies it should be pointed out that there are general scientific difficulties in predicting impacts of connected and automated mobility due to uncertainties about propulsion energy, future capacity of power grids, employment, development of costs, and about the behaviour and acceptance with regard automated vehicles. The results of the models in LEVITATE are dependent upon specific assumptions which limit the generalisability of these results. Currently, there are no large fleets of CAVs in use in traffic, so it was not possible to actually measure the specific vehicle characteristics for the modelling purposes. The simulation models used examined only two CAV profiles (first generation versus second generation); future work may extend the number of driving profiles. The safety results of the microsimulation did not include crashes where vulnerable road users are involved.
Policy recommendations
Based on the findings of WP5 and recent literature on automated urban transport and mobility, the report provides a number of policy recommendations. The recommendations are focused on the new role of public authorities in managing future urban transport and mobility, the importance of strategic plans and agendas, the prevention of unwanted side-effects, decision criteria for future projects, integration of shuttle services with public transportation, clear communication, further developing existing guidelines and lists impacts for future urban transportation development plans.
LEVITATE has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824361.
