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Driverless or fully automated vehicles (AVs) are expected to fundamentally change how individuals and households travel and how vehicles use roadway infrastructure. The first goal of this study is to develop a modeling framework of activity-constrained household travel in a future multi-modal network with private AVs, shared-use AVs, transit, and intermodal AV-transit travel options. The second goal is to analyze the potential impacts of AVs—including intermodal AV-transit travel—on (a) household-level travel behavior, (b) household travel costs, (c) demand for transport modes, including transit, and (d) vehicle kilometers traveled or VKT. To meet the first goal, we propose and formulate the Household Activity Pattern Problem with AV-enabled Intermodal Trips (HAPP-AV-IT) that incorporates AV deadheading and intermodal AV-transit trips. The modeling framework extends prior HAPP-based formulations that model household-level travel decisions as vehicle (and person) routing and scheduling problems, similar to the pickup and delivery problem with time-windows. To meet the second goal, we apply the HAPP-AV-IT to two case studies and conduct many computational experiments. We use synthetic activity location data for synthetic households and a fictitious medium-size network with a road network, transit network, residential locations, activity locations, and parking locations. The computational results illustrate (a) the critical role that household AV ownership plays in terms of household travel decisions, modal demand, and VKT, (b) that with AVs, deadheading accounts for 30–40 % of vehicle operating distances, (c) that around 10 % of households in the study region benefit from AV-based intermodal trips, and (d) that those 10 % of households see 5 % reductions in household travel costs and 25 % reductions in VKT on average in the most transit friendly scenario. This last finding suggests that intermodal AV-transit trips may exist in a driverless vehicle future, and therefore, transit agencies and transportation planners should consider how to serve this market. We also propose and test a simple heuristic algorithm that quickly solves HAPP-AV-IT problem instances. 

The goal of this study was to analyze the impact of private autonomous vehicles (PAVs), specifically their near-activity location travel patterns, on vehicle miles traveled (VMT). The study proposes an integrated mode choice and simulation-based parking assignment model, along with an iterative solution approach, to analyze the impacts of PAVs on VMT, mode choice, parking lot usage, and other system performance measures. The dynamic simulation-based parking assignment model determines the parking location choice of each traveler as a function of the spatial–temporal demand for parking from the mode choice model, whereas the multinomial logit mode choice model determines mode splits based on the costs and service quality of each travel mode coming, in part, from the parking assignment model. The paper presents a case study to illustrate the power of the modeling framework. The case study varies the percentage of persons with a private vehicle (PV) who own a PAV versus a private conventional vehicle (PCV). The results indicated that PAV owners traveled an extra 0.11 to 1.51 mi compared with PCV owners on average, and the PV mode share was significantly higher for PAV owners. Therefore, as PCVs are converted into PAVs in the future, the results indicate substantial increases in VMT near activity destinations. However, the results also indicated that adjusting parking fees and redistributing parking lot capacities could reduce VMT. The significant increase in VMT from PAVs implies that planners should develop policies to reduce PAV deadheading miles near activity locations, as the automated era comes closer.