Shared, automated and electric mobility and transport pricing Currently, the allocation of congested road space does not take into account drivers’ valuation of time. Some shared transportation solutions (such as carsharing and ridesourcing) however contain elements of dynamic pricing, where the cost of using the service increases in times of congestion. A widespread move to automated and shared mobility would induce fleet operators to introduce some form of congestion pricing. With electric fleets, pricing would also need to take into account the need to balance the electric grid, and to manage scarce space for reloading stations. • With electric cars, pricing should take into account the need to balance the electric grid • Operators of automated and shared fleets will introduce some form of congestion pricing• Some shared transportation solutions contain elements of dynamic pricing • The allocation of congested road space does not take into account drivers’ valuation of time The most widely used approach to allocate road space in times of congestion is “rationing by queuing”: roughly speaking, road space is allocated on a “first come, first served” basis, independently of an individual’s value of time. The alternative (distance based charging differentiated according to the current levels of congestion) would imply “rationing by pricing”, but is not applied widely. – link to value of time We will argue here that shared transport modes offer the potential for a more rational approach to transport pricing but that they also raise new specific challenges when it comes to pricing. In carsharing systems, congestion can occur at the point of vehicle access. The current business practice is first-come/first-served but “dynamic pricing” could be an option for the future (Le Vine et al., 2014). Whether dynamic pricing of carsharing can reduce overall congestion on the road network depends on the extent to which periods of peak overall traffic correspond to periods of peak demand for shared cars. But even if prices for carsharing do not change with demand, users who drive during the peak hours (and thus experience longer travel times) already have to pay more (Le Vine et al., 2014). Thus, a wider use of carsharing would to some extent “privatise” time- and place-differentiated road pricing. Until now, the market segment that has showed the highest consistency in the application of market pricing is the so-called Transportation Network Companies (TNCs) or ridesourcing companies. One important characteristic of these services, which differentiates them from the traditional taxicab market, is the use of dynamic pricing or “surge pricing”: during periods of peak demand, prices increase to balance supply and demand. In concrete terms, the purpose of “surge pricing” is to provide incentives to drivers to accept drive requests when demand increases, for instance due to poor weather (Shaheen et al., 2015a). Whilst “surge pricing” is, strictly speaking, a simple move from “rationing through queuing” to “rationing through prices”, it has turned out to be one of the more controversial and unpopular aspect of the TNC’s business models. Other platforms work with incentive schemes that are not framed explicitly as ‘surge pricing’, but that do provide similar incentives for drivers. For instance, drivers can earn extra points when accepting drives in peak periods, and these points determine which drivers will be contacted first when demand is low . In the future, the rise of automated vehicles will raise new challenges for transport pricing.Indeed, as the opportunity cost of travel time in automated vehicles will become very low, important increases in the monetary cost of private travel will be needed to avoid new induced travel. If automated vehicles are part of a shared fleet, then the operators of the fleet are likely to apply ‘surge pricing’ during peak hours to improve the fleet’s performance and decrease waiting times. Moreover, operators may gain by offering differentiated services: high quality services at a premium price to travellers with a high opportunity cost of time and basic services at lower prices to travellers with a low opportunity cost of time (Kockelman and Chen, 2016). In such schemes, pricing will also penalize “trips that incur more relocation miles (and thereby increase subsequent trip wait times) and incentivize trips that coincide with strategic relocation (and thereby decrease subsequent trip wait times)” (Kockelman et al., 2016). A widespread use of shared automated vehicles (SAVs) will thus lead to some form of ‘privatised’ congestion charging. An additional complication arises if these SAVs use electric powertrains (SAEVs). In the case of electric vehicles, dynamic pricing of electricity is needed to provide incentives to charge the batteries of the vehicles when overall demand on the electricity grid is low (or even to provide electricity from the batteries to the grid when demand is high) (Joskow and Wolfram, 2012). Operators will include these elements in their own pricing schemes, and may face trade-offs between offering transport services and vehicle-to-grid services. As the peak hours in traffic fall during the peak periods for electricity demand, this will create complex new interactions between transport and electricity pricing (see Kockelman and Chen, 2016 and Kockelman et al., 2016). Still another complication is that a widespread use of SAEVs require large parking areas for charging (Kockelman et al., 2016), even if the net effect of SAEVs remains a decrease in the demand for parking space (Joskow and Wolfram, 2012). Parking lots (both normal and for charging) will therefore also need to be subject to “smart pricing” – highlighting that the pricing of distance travelled will need to be coordinated with the pricing of multiple other services. In summary, the uncertainty concerning the net impacts of shared mobility solutions and of automated vehicles implies that correct pricing of transport will become more important in the future rather than less important. The correct pricing of all transport modes according to their social costs will ensure that society will be able to capture the benefits of these innovations, while avoiding the possible disadvantages. Joskow, P.L. & Wolfram, C.D., 2012. Dynamic Pricing of Electricity, American Economic Review, American Economic Association, vol. 102(3), pages 381-85, May. Kockelman, K.M. & Chen D. (2016), Management Of A Shared, Autonomous, Electric Vehicle Fleet: Implications Of Pricing Schemes, Proceedings of the 95th Annual Meeting of the Transportation Research Board (January 2016) and under review for publication in Transportation Research Record Kockelman, K.M., Chen D. & Hanna, J. (2016) Operations of a Shared, Autonomous, Electric Vehicle (SAEV) Fleet: Implications of Vehicle & Charging Infrastructure Decisions. Proceedings of the 95th Annual Meeting of the Transportation Research Board (2016), and under review for publication in Transportation Research Part A (2015). Le Vine, S., Zolfaghari A. & Polak, J. (2014), Carsharing: Evolution, Challenges and Opportunities, 22th ACEA Scientific Advisory Group Report https://www.acea.be/uploads/publications/SAG_Report_-_Car_Sharing.pdf Shaheen, S., Chan, N., Bansal, A. & Cohen, A. (2015a), Shared Mobility: Definitions, Industry Developments, and Early Understanding Bikesharing, Carsharing, On-Demand Ride Services, Ridesharing, Shared-Use Mobility http://innovativemobility.org/?project=shared-mobility-definitions-industry-developments-and-early-understanding