Effects of Autonomous Vehicle Ownership on Trip, Mode, and Route Choice

Abstract

Autonomous vehicles (AVs) may significantly change traveler behavior and network congestion. Empty repositioning trips allow travelers to avoid parking fees or share the vehicle with other household members. Computer precision and reaction times may also increase road and intersection capacities. AVs are currently being test driven on public roads and may be publicly available within the next two decades; they therefore may be within the span of 20- to 30-year planning analyses. Despite this time scale, AV behavior has yet to be incorporated into planning models. This paper presents a multiclass, four-step model that includes AV repositioning to avoid parking fees (although incurring additional fuel costs) and increases in link capacity as a function of the proportion of AVs on the link. Demand is divided into classes by value of time and AV ownership. Mode choice—parking, repositioning, or transit—is determined through a nested logit model. Traffic assignment is based on a generalized cost function of time, fuel, and tolls. The results on a city network show that transit ridership decreases and the number of personal vehicle trips sharply increases as a result of repositioning. However, increases in link capacity offset the additional congestion. Although link volume increases significantly, only modest decreases in average link speeds are observed.

Policy and society related implications of automated driving: A review of literature and directions for future research

“…Another study showed that a modal shift of up to 1%, mainly from local public transport (bus, light rail, subway) and bicycle, to drivealone and shared-ride modes could be possible because of the ability to multitask in automated vehicles (Malokin, Circella, & Mokhtarian, 2015). Levin and Boyles (2015) confirmed the possibility of increased modal shift from public transport to automated vehicles especially when these vehicles become widely available to travellers with lower value of time. Lamondia, Fagnant, Qu, Barrett, and Kockelman (2016) focused on possible modal shift from personal vehicles and airlines to automated vehicles for long distance travel using Michigan State as case study.…”

Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions

“…Páez et al (2012) found that people with disabilities who have used a car within the past 12 months are about 28% more likely to desire more leisure activities compared to those who have not (Páez and Farber, 2012) Many companies have announced plans to develop self-driving vehicles, and twelve companies have applied to test self-driving cars in California as of 2016 (Chew, 2016). Vehicle automation has the potential to greatly improve travel by reducing congestion, travel times, crashes, and potentially energy consumption (Anderson et al, 2014;Brown et al, 2014;Harper et al, 2016;Levin and Boyles, 2015;Mersky and Samaras, 2016;Wadud et al, 2016). The ability for smart vehicles to interact with smartphones and act as a taxi service to transport people to their destinations also serves as an advantage, reducing travel costs by almost 75 percent (Litman, 2013).…”

A bi‐level model to optimize road networks for a mixture of manual and automated driving: An evolutionary local search algorithm

“…(), and van Arem, van Driel, and Visser (). This has been utilized in some NDP studies that consider AVs via assumptions of increased capacity in presence of AVs (Chen et al., ; Chen et al., ; Ye & Wang, ) and some other studies of AV impacts using macroscopic traffic assignment models (Levin & Boyles, ; Madadi, van Nes, Snelder, & van Arem, ).…”

“…Although this is not fully established and there is no consensus in the literature yet, there are studies that claim AD will lead to a lower VoTT (Correia, de Looff, van Cranenburgh, Snelder, & van Arem, ; de Looff, Correia, van Cranenburgh, Snelder, & van Arem, ; Le Vine, Zolfaghari, & Polak, ; Milakis, Snelder, van Arem, van Wee, & Correia, ). This can be considered in the route choice component of macroscopic traffic assignment models via differences in generalized travel cost; see, for instance, Levin and Boyles () and Madadi et al. ().…”

“…Note that since static traffic assignments do not model traffic flow dynamics with a high level of details, capacity changes as a result of AD have been incorporated in static traffic assignment models in two different (simplified) manners; the first one is via assigning higher capacity to links that accommodate AVs which is more appropriate when considering dedicated lanes and links for AVs (Chen et al., ; Chen et al., ; Ye & Wang, ). The alternative is to consider an AV‐flow‐specific link travel time that is dependent on both cumulative flow of CVs and AVs on the link, and the ratio of CVs to AVs on the link (Levin & Boyles, ; Madadi et al., ). This approach is more appropriate when considering a mix of traffic (CVs and AVs) on links.…”