Valuation of Travel Attributes for Using Automated Vehicles as Egress Transport of Multimodal Train Trips

摘要

In the recent years many developments took place regarding automated vehicles (AVs) technology. In fact AVs are expected to become available on the market in the next decades. It is however unknown to which extent the share of the existing modes will change as result of AVs introduction. To the best of our knowledge this study is the first where traveller preferences for AVs are explored and compared to existing modes. Thereby its main objective is to position AVs in the transportation market and understand the sensitivity of travellers towards some of their attributes. Because there are no fully-automated vehicles currently on the market, we apply a stated preference choice experiment where we explore the role of classic instrumental variables such as different travel time components and travel cost. In our study we focus on positioning AVs in the context of last mile transport at the activity-end in multimodal train trips. We can conclude that first class train travellers on average prefer using an automated vehicle as egress transport between train station and final destination, compared to using other egress modes. Second class train travellers on average prefer the use of bicycle and bus/tram/metro as egress mode instead of automated vehicles. Especially for first class train passengers, implementing AVs as last mile transport therefore has potential. Second, sensitivity of travellers for in-vehicle time is considerably higher for an automatically driven AV, compared to a manually driven AV. As consequence, the willingness-to-pay for a certain travel time reduction in an automatically driven AV is considerably higher, compared to a manually driven AV. Despite theoretical advantages of using travel time more efficiently in an automatically driven AV, it might be that psychological concepts, like attitudes, play a role here. Since automated driving is a very new and innovative way of transportation, the classic instrumental attributes like travel time might not tell the whole story.