۲٫۱٫ Models rooted in Travel Demand

Levin et al. (2015a) propose a four-step model dividing demand into classes by value of time and AV ownership. AVs are considered as private vehicles. Mode choice is between parking, repositioning, and transit based on a nested logit model. Sta tic traffic assignment uses a generalized cost function of time, fuel, and tolls. Levin (2015b) incorporates dynamic traffic assignment (DTA) with endogenous departure time choices. Thus, the model considers more realistic flow propagation and intersection control options. In addition, it has only studied the case of full autonomous vehicles. Results of static and dynamic assigment prove that using autonomous vehicles improves the capacity of the intersections, but does not reduce significantly the congestion. Auld et al. (2017) used a simulation model (POLARIS) which includes an activity-based model (ADAPTS) and a traffic simulation model. Market penetration is controlled on a regional scale by adjusting road capacity. Results show that capacity and value of time affect significantly vehicle-kilometres travelled (VKT). Kloostra et al. (2017) assumed that autonomous vehicles will change road capacity thanks to ACC technology. Then, they modified road links capacities to simulate theoretical increase in throughput enabled by AV driving behaviour. They distinguished two types of road links: freeways and arterial streets. A static assignment in Emme 4 is realised. In addition, they analysed the impacts on parking operations.