Abstract

Autonomous vehicles (AV) create new opportunities to traffic planners and policy-makers. In the case of shared autonomous vehicles (SAVs), dynamic pricing, vehicle routing and dispatch strategies may aim for the maximization of the overall system welfare instead of the operator’s profit. In this study, an existing congestion pricing methodology is applied to the SAV transport mode. On the SAV operator’s side, the routing- and dispatch-relevant cost are extended by the time and link-specific congestion charge. On the users’ side, the congestion costs are added to the fare. Simulation experiments are carried out for Berlin, Germany in order to investigate the impact of SAVs and different pricing setups on the transport system. For the pricing setup, where SAV users only pay the base fare and there is no congestion charge added to the user costs, the model predicts an SAV share of 17.7% within the inner-city Berlin service area. The level of traffic congestion increases, air pollution levels decrease and noise levels slightly increase in the inner-city area. The SAV congestion charge pushes users from SAVs to the walk, bicycle and conventional (driver-controlled) private car (CC) mode. The latter effect is avoided by applying the same congestion charge also to CC users. Overall, this study highlights the importance to control both, the SAV and CC mode in order to improve a city’s transport system.

Abstract

In 2008, renewable energy accounted for less than 1% of final energy consumption in the Irish transport sector. In order to increase this share to 10% by 2020 as required under EU directive 2009/28/EC, the Irish government has introduced two specific measures: 10% of the transport fleet is to be powered by electricity by 2020, and an obligation on road transport fuel suppliers that biofuels account for a certain portion of their fuel sales. This study forecasts the impact of these existing measures towards meeting the 10% RES-T target by 2020, focussing on private car transport. The methodology presented is derived from a forecast of private car fuel demand based on a technological stock model of Ireland’s fleet. This paper demonstrates the use of this as a tool firstly as an energy forecasting technique and secondly as a method for evaluating the effects of policy measures on the technological composition and consequent renewable energy demand and related CO2emissions of private cars. Technological scenarios examined in this light are electric vehicles, compressed natural gas vehicles and biofuel blending