Goals and impacts
Mobility of people and goods is the lifeline of the modern city. In planning for future urban mobility 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, 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. 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 Cooperative, connected and automated mobility (CCAM).
Given the many uncertainties in prediction, it is obvious that any predicted values are associated with large uncertainty. For the WP7 results, it was decided not to estimate confidence intervals based on the standard error derived from repeated trail runs of models since these intervals would be broad and non-informative. Also, the estimation of these intervals would tend to be biased in itself since the input variables and assumptions in the models are very likely much stronger determinants of predicted values than the variability in sample runs.
Approach to summarizing LEVITATE results
The goal of this Deliverable is to summarize the more detailed results presented in D7.2-D7.4 and to provide an overview of the main expected trends for each selected 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 cooperative and automated freight transport, three primary methods were used: microsimulation, Delphi, and operations research. A number of SUCs related to particular developments in the freight transport sector were defined and these methods were applied to derive estimates of the impacts that these SUCs would have at different penetration rates of CAVs. To summarize these results, for each sub-use case an average (where applicable) is taken of its scenarios to derive an average percentage change for the respective sub-use case.
The impacts are presented as a percentage change from the Baseline scenario at 0% penetration of CAVs, where neither automated freight nor automated vehicles have been implemented in the network. These percentage changes are reported for increasing market penetration rates of automated vehicles throughout the entire vehicle fleet in the network, as used throughout LEVITATE.
The Baseline scenario refers to a “no intervention” scenario which is essentially the expected autonomous development of CAVs from human dependence to human independence. In the Baseline scenarios there is no cooperative and automated freight transport added to the network. The impacts of CAVs on network performance can be established by comparing the Baseline 100-0-0 scenario (100% human-driven/reliant vehicles) to the Baseline 0-0-100 scenario (0% human-driven/reliant vehicles). The specific effect or impact of the cooperative and automated freight transport scenario can be determined by comparing the baseline situation for any given penetration rate with the specific SUC results; the difference between the baseline and the SUC is the added effect created by implementing the specific SUC intervention in the simulated network.
Overall effects of CAVs
Estimating the baseline impacts of an increasing share of connected and automated vehicles (CAVs) for Work Package 7 revealed the following main findings. The results are based on simulations run on the network of Vienna and for all vehicles in the network (including both freight vehicles & private cars).
- The increasing presence of connected and automated vehicles in the urban city area is estimated to have positive impacts on the city environment (less emissions, higher energy efficiency), and city society and economy (less parking space, lower freight vehicle operating cost) and on city mobility (less congestion).
- In Work Package 7, the increasing presence of automated vehicles in the city is estimated to have a temporary negative impact on road safety when penetration rates of automated vehicles are low. The negative impact found is primarily due to interactions between human-driven vehicles and automated vehicles, which are expected to have different driving styles (e.g. AVs adopting different headways) and different capabilities (e.g. human drivers’ longer reaction times) which may lead to an initial increase in risks when many human drivers are still on the road. This result differs from the baseline results found in the road safety impact study (Weijermars et al., 2021) and discussed in WP5 and WP6, primarily due to two factors: 1) differences in the network (Vienna) and 2) the inclusion of freight vehicles. Because less data was available on the driving behaviour of autonomous freight vehicles, some parameters assumed the values of 1st generation CAVs and others were based on assumptions. This led to higher crash rate estimations when freight vehicles were included.
Larger positive impacts on road safety are estimated once human-driven vehicles are replaced and second-generation automated vehicles make up at least 60% of the city’s vehicle fleet. More broadly within LEVITATE, most estimates point to a large reduction in crashes with the introduction of automated vehicles including a small reduction at low penetration rates. At low penetration rates, the balance between the safety of automated vehicles (which are expected to crash less often than human-driven vehicles) and the potential risks of mixed traffic (when human-driven/less advanced automated vehicles are still on the road) is a point of attention for further research.
- The increasing presence of automated vehicles in the city is estimated to have a slightly negative impact on public health when traditional (human-driven) vehicles make up the majority of vehicles, followed by a slightly positive impact at full automation of the vehicle fleet.
Effects of SUCs: automated delivery, consolidation and hub-to-hub transport
Estimating the impacts of an increasing share of CAVs in the total vehicle fleet together with one of the three forms of automated freight transport revealed the following main findings:
- The automated delivery sub-use case is associated with additional benefits for energy efficiency, CO2 emissions, congestion, public health and vehicle operating costs. The night-time-only automated delivery scenarios (see Appendix A) show additional benefits particularly for the two mobility indicators (travel time and congestion), due to less interaction with the larger daytime traffic volumes.
- The automated consolidation sub-use case is associated with additional benefits for energy efficiency, CO2 emissions, congestion, travel time, public health and vehicle operating costs. Compared to automated delivery without consolidation at city hubs (the first sub-use case), further improvements in energy efficiency, operating costs, and a large reduction in total kilometres travelled are expected. This suggests that centrally located city-hubs can help realise a more efficient allocation of resources.
- The hub-to-hub sub-use case is expected to deliver additional benefits for energy efficiency, CO2 emissions, congestion, travel time, public health, and freight vehicle operating costs.
- All three automated freight SUCs are predicted to marginally improve road safety compared to the baseline, particularly at lower penetration rates when less of the remaining vehicle fleet is automated.
- At the higher-level CAV penetration rates (above 80%), all the automated freight delivery SUCs require more parking space than the baseline without automated delivery. The Hub-to-Hub SUC even requires more parking space at 100% CAV penetration compared to the current situation (with 100% human-driven vehicles).
- The sub-use cases of automated delivery, hub-to-hub and especially automated consolidation are predicted positively impact public health. This positive expectation is likely based on the expected additional benefits of these sub-use cases for both road safety and emissions.
- Using data on freight delivery trips in Vianna, it was estimated that compared to manual freight delivery, completely automated delivery and automated delivery with city-hubs will have substantially reduced annual fleet costs (-68%).
Effects of truck platooning on bridges
- The largest effect of truck platooning on simple single span (beam) bridges as modelled in LEVITATE is observed for the criteria of braking forces. For bridges above 80m length, it has been estimated that the braking force is at least double of the baseline scenario.
- According to standard bridge models and standard traffic simulations within LEVITATE, the need for strengthening structural resistance of bridges arises for many existing bridge types and brings with it substantial costs
- For bridge strengthening, a model and guidelines for estimating the costs in relation to the initial construction costs have been developed (D7.3).
- As an alternative to strengthening bridges, intelligent access control can be used to arrange the increase of inter-vehicle distances for the bridge section to meet the code level and prevent. Headway have been recommended and these are presented in LEVITATE D7.3 (Hu et al., 2021b). Forcing an increased inter-vehicle distance by intelligent access control will not diminish the ecological and economic benefits of truck platoons.
Recommendations freight transport
For freight transport several recommendations can be given (Hu et al., 2019):
- Passenger transport and freight transport should seek collaboration (e.g., via automated multi-purpose vehicles)
- Collaborative transportation, supported by city hubs and consolidation centres, are necessary to improve operational efficiency. CCAM, especially automated hub-to-hub transport and automated freight consolidation, will contribute significantly
- Multimodality and synchro modality are important factors to aim towards a sustainable logistic supply chain.
- All the above points require homogenous and shared data among operators, which is perhaps the most difficult challenge due to the competition between service providers and freight operators.
Strengths and limitations of Levitate
The followings observations pertain to strengths and limitations of research within WP7 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 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.
Concerning limitations, it should be pointed out 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. The simulation models used examined only two CAV profiles (first generation vs. second generation ); future work may extend the number of profiles. The safety results of the microsimulation did not include crashes where vulnerable road users are involved.
LEVITATE has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824361.