Automated driving technology is emerging. Yet, little is known in the literature about when automated vehicles will reach the market, how penetration rates will evolve and to what extent this new transport technology will affect transport demand and planning. This study uses scenario analysis to identify plausible future development paths of automated vehicles in the Netherlands and to estimate potential implications for traffic, travel behaviour and transport planning on a time horizon up to 2030 and 2050. The scenario analysis was performed through a series of three workshops engaging a group of diverse experts. Sixteen key factors and five driving forces behind them were identified as critical in determining future development of automated vehicles in the Netherlands. Four scenarios were constructed assuming combinations of high or low technological development and restrictive or supportive policies for automated vehicles (AV …in standby, AV …in bloom, AV …in demand, AV …in doubt). According to the scenarios, fully automated vehicles are expected to be commercially available between 2025 and 2045, and to penetrate the market rapidly after their introduction. Penetration rates are expected to vary among different scenarios between 1% and 11% (mainly conditionally automated vehicles) in 2030 and between 7% and 61% (mainly fully automated vehicles) in 2050. Complexity of the urban environment and unexpected incidents may influence development path of automated vehicles. Certain implications on mobility are expected in all scenarios, although there is great variation in the impacts among the scenarios. Measures to curb growth of travel and subsequent externalities are expected in three out of the four scenarios.
Efforts have been made in the last few decades to provide new urban transport alternatives. One of these is carsharing, which involves a fleet of vehicles scattered around a city for the use of a group of members. At first, part of the research effort was put into setting up real life experiments with vehicle fleets and observing the performance of major private operators. In the meantime, with the growth of this alternative and the need to better plan its deployment, researchers started to create more advanced methods to study carsharing systems’ planning issues. In this paper, we review those methods, identifying gaps and suggesting how to bridge them in the future. Based on that review we concluded that carsharing demand is difficult to model due to the fact that the availability of vehicles is intrinsically dependent on the number of trips and vice versa. Moreover, despite the existence of carsharing simulation models that offer very detailed mobility representations, no model is able to characterise accurately the supply side, thus hindering the cost-benefit assessment that is fundamental to justify investment in this transport alternative, in particular those that are being endorsed by the European Union. More complex, however, is the operation of the emerging one-way carsharing systems, where a vehicle may be dropped off at any station, which adds uncertainty as to the location where vehicles can be picked up. Several optimisation approaches have been proposed to mitigate this problem but they are always limited in scope and leave other aspects out for model simplification purposes. Some simulation models have also been developed to study the performance of this type of carsharing system, but they have not included ways of balancing the vehicle stocks.
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