Public discourse and commercial interest in the emergence of connected, automated, and autonomous vehicles has increased dramatically in recent years. After decades of research and development on vehicle automation, these technologies have reached a stage where they are now becoming available on a wide commercial scale. Much of the dialogue about vehicle automation suggests that this technology has several benefits, including improved safety and reduced congestion. However, the potential energy impacts of vehicle automation are highly uncertain, and range from a huge net energy savings, to a dramatic net energy increase. Given the significant impact of the transportation sector on energy consumption and greenhouse-gas emissions, it is imperative that we develop a research agenda and policy framework to ensure that these emerging technologies can also meet transportation energy and climate goals. This meta-analysis of the literature summarizes results on the potential travel demand, energy, and climate impacts of vehicle automation, and discusses key considerations for policymakers.

The nexus of autonomous vehicle (AV) and electric vehicle (EV) technologies has important potential impacts on our transportation systems, particularly in the case of shared-use vehicles. There are natural synergies between shared AV fleets and EV technology, since fleets of AVs resolve the practical limitations of today’s non-autonomous EVs, including traveler range anxiety, access to charging infrastructure, and charging time management. Fleet-managed AVs relieve such concerns, managing range and charging activities based on real-time trip demand and established charging-station locations, as demonstrated in this paper. This work explores the management of a fleet of shared autonomous (battery-only) electric vehicles (SAEVs) in a regional discrete-time, agent-based model. The simulation examines the operation of SAEVs under various vehicle range and charging infrastructure scenarios in a gridded city modeled roughly after the densities of Austin, Texas.

Results indicate that fleet size is sensitive to battery recharge time and vehicle range, with each 80-mile range SAEV replacing 3.7 privately owned vehicles and each 200-mile range SAEV replacing 5.5 privately owned vehicles, under Level II (240-volt AC) charging. With Level III 480-volt DC fast-charging infrastructure in place, these ratios rise to 5.4 vehicles for the 80-mile range SAEV and 6.8 vehicles for the 200-mile range SAEV. SAEVs can serve 96 to 98% of trip requests with average wait times between 7 and 10 minutes per trip. However, due to the need to travel while “empty” for charging and passenger pick-up, SAEV fleets are predicted to generate an additional 7.1 to 14.0% of travel miles. Financial analysis suggests that the combined cost of charging infrastructure, vehicle capital and maintenance, electricity, insurance, and registration for a fleet of SAEVs ranges from $0.42 to $0.49 per occupied mile traveled, which implies SAEV service can be offered at the equivalent per-mile cost of private vehicle ownership for low mileage households, and thus be competitive with current manually-driven carsharing services and significantly cheaper than on-demand driver-operated transportation services. The availability of inductive (wireless) charging infrastructure allows SAEVs to be price-competitive with non-electric SAVs (when gasoline prices are between $2.18 and $3.50 per gallon). However, charging SAEVs at attendant-operated stations with traditional corded chargers incurs an additional $0.08 per mile compared to wireless charging, and as such would only be price-competitive with SAVs when gasoline reaches $4.35 to $5.70 per gallon.

Understanding the processes that guide fleet management by corporations is key to assessing the potential role corporations could play in the transition towards a more sustainable mobility system, and to drawing operational policy conclusions accordingly. Building on the information collected through 44 interviews with decision-makers from 22 large organisations in the Paris region, we reach a much deeper understanding of the fleet management processes of large organisations. We find that the prospects for global optimisation of the corporate car fleet – from the perspectives of both purchase behaviours and daily operations –depend on the solutions that corporations can find to tackle, on the one hand, the complexity of the decision-making processes for car fleet acquisition (e.g. through implementing car policies) and, on the other hand, the shortcomings of information on fleet use and fleet costs (e.g. through deploying monitoring and tracking technologies). In order to discuss the outlook for electric vehicles and car-sharing services in corporate car fleets, we analyse the specific barriers to, and drivers of, their adoption, and report on some of the ‘good practices’ revealed by our survey.

This paper presents the concept and key specifications of an urban transport system with two market segments of on-demand shared transport services for the road-based components, and a simulation based model that examines accessibility and resulting mobility outputs in a European capital city. The model bases its analysis in synthetic transport demand data that specifies the detailed departure and arrival times and locations of passengers as well as their socio-demographic characteristics and mobility profile (travel agenda). The model also encompasses a new dispatching algorithm with real time information about the location of shared fleets and the traffic condition. An efficient assignment of requests is performed ensuring parameterised quality standards for the clients of both types of service. The model is tested under different configuration scenarios for the city of Lisbon, measuring the main outcomes in terms of mobility.