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An increasing number of battery-electric vehicles (BEVs) have bidirectional charging technology that provides new benefits to motorists, homeowners, and power grid operators. A web-based survey investigates the willingness of over 300 Americans to pay for added bidirectional charging features, namely, vehicle-to-load (V2L), vehicle-to-home (V2H), and vehicle-to-grid (V2G) technologies, along with their expected use frequency. Summary statistics suggest that Americans are willing to pay (WTP) an average of $280 and $776 on top of the price of a new car for V2L and V2H, respectively. About 51.3% would let their power company discharge their vehicle via V2G during grid emergencies if compensated and guaranteed a minimum battery level. Interval regression and ordered probit equations explain how demographics, travel patterns, and attitudinal variables affect the response variables, including WTP for bidirectional charging features and expected reliance on technology. The statistically and practically significant relationships suggest that adults over 34 have lower WTP values for V2L and V2H, and those in households with more vehicles plan on more bi-directional charging, as expected. The findings have implications for policymakers, manufacturers, and stakeholders involved in the BEV ecosystem, informing their decision-making processes related to integrating and commercializing bidirectional charging technologies. These models may even help power grid planners understand who is likely to adopt V2G technology, enabling them to aggregate and shift BEV loads to help manage the grid in parallel and isolation.

This work simulates the long-term land use and transportation impacts of upzoning in the region of Seattle, Washington at the parcel level, using UrbanSim and an agent-based travel demand model (SoundCast). Scenario results are compared to illuminate differences in a Business-as-Usual (BAU) future versus two upzoning scenarios that upzone all single-family-use residential parcels in the region or just those in the City of Seattle to 3 dwelling units per parcel. Under BAU, affordability falls 20% and rents rise 6% between 2014 and 2050, as the regional population grows to 5.7 million. With regional upzoning, 68,400 more dwellings are added, with rents 10% lower compared to the BAU future. The housing affordability index falls from 75.3 to 67.31, a significant improvement over the BAU scenario's sharper fall to 60.6. If only the City of Seattle upzones, only 3,400 additional units are predicted, but rents are 5.2% lower than the BAU case. [ABSTRACT FROM AUTHOR]

Parking spots are a premium commodity, especially in dense downtown settings, so this study examines the service impacts of shared autonomous vehicles (SAVs) parking in legal on- or off-street locations when idle across Travis County in Austin, Texas. Using an agent-based activity-based travel demand model with dynamic traffic simulation, two restricted-parking strategies for SAVs were simulated. SAVs either found the nearest available parking spot or the lowest-cost spot (via a tradeoff of parking fees and distance-based costs). Two comparisons were conducted to analyze the impacts of these strategies. First, two restricted parking strategies were compared, where SAVs park without competition with private human-driven vehicles (HVs) for parking locations. Second, a more realistic analysis compared two SAV parking strategies with a scenario where SAVs remain idle in place. Private HVs in all scenarios and strategies of this comparison park at the closest designated location unless they opt for private parking. Using a supply of 8,400 aggregated parking locations in Austin, this study simulated fleet performance under different trip demands, with SAV fares of $0.62 per kilometer ($1 per mile) plus a $1 fixed pickup fee with dynamic ridesharing permitted. Parking costs were negligible in both SAV parking search strategies applied to the Austin network because of the region's provision of mostly free parking. Requiring SAVs to park on designated on- and off-street parking locations and parking lots (restricted parking) also increased parking costs for HV drivers by up to 22% since SAVs occupied some free parking spaces, especially in the least-cost parking search strategy. [ABSTRACT FROM AUTHOR]

This paper optimizes the assignment of shared automated vehicles under users' uncertain departure times. Automated vehicles can drive themselves, so no staff are needed to relocate vehicles in the one-way carsharing system. To optimize fleet placement and use, a twophase solution method was established. Phase 1 strategically distributes vehicles across stations using a system optimization approach, while Phase 2 tracks vehicle movements via an agent-based simulation model. Phase 1 solutions serve as inputs to Phase 2 simulations. Using a fleet size of roughly 10,000 vehicles, case study applications were run across the Austin, Texas region's six-county network. In the base case setting, results suggest that system profits are optimized when vehicle rental is priced at $1.28=km ($0.8=mi). The number of proactive vehicle relocations falls 9.8% if the relocation operation cost rises from $0.096=km ($0.06=mi) to $0.32=km ($0.2=mi). Average per-trip profit is $10.60 when using high-cost vehicles, and $11.60 when using low-cost vehicles. Results from a 3-h simulation show an average person-trip length of 25 km (15.6 mi), with 29.6 min of average driving time. When a 24-h day was simulated, the vehicle-occupied time and vehicle-distance traveled were 4 h and 200 km (125 mi) per vehicle-day, respectively. The low coefficient of variation of satisfied demand across 30 demand scenarios suggests the robustness of the two-phase solution method. [ABSTRACT FROM AUTHOR]

Advances in information technologies and vehicle automation have birthed new transportation services, including shared autonomous vehicles (SAVs). Shared autonomous vehicles are on-demand self-driving taxis, with flexible routes and schedules, able to replace personal vehicles for many trips in the near future. The siting and density of pick-up and drop-off (PUDO) points for SAVs, much like bus stops, can be key in planning SAV fleet operations, since PUDOs impact SAV demand, route choices, passenger wait times, and network congestion. Unlike traditional human-driven taxis and ride-hailing vehicles like Lyft and Uber, SAVs are unlikely to engage in quasi-legal procedures, like double parking or fire hydrant pick-ups. In congested settings, like central business districts (CBD) or airport curbs, SAVs and others will not be allowed to pick up and drop off passengers wherever they like. This paper uses an agent-based simulation to model the impact of different PUDO locations and densities in the Austin, Texas CBD, where land values are highest and curb spaces are coveted. In this paper 18 scenarios were tested, varying PUDO density, fleet size and fare price. The results show that for a given fare price and fleet size, PUDO spacing (e.g., one block vs. three blocks) has significant impact on ridership, vehicle-miles travelled, vehicle occupancy, and revenue. A good fleet size to serve the region's 80 core square miles is 4000 SAVs, charging a $1 fare per mile of travel distance, and with PUDOs spaced three blocks of distance apart from each other in the CBD. [ABSTRACT FROM AUTHOR]

Before shared automated vehicles (SAVs) can be widely adopted, they are anticipated to be implemented commercially in confined regions or fixed routes where the benefits of automation can be realized. SAVs have the potential to operate in a traditional transit corridor, replacing conventional transit vehicles, and have frequent interactions with riders and other vehicles sharing the same right of way. This paper microsimulates SAVs' operation on a 6.5-mile corridor to understand how vehicle size and attributes of such SAV-based transit affect traffic, transit riders, and system costs. The SUMO (Simulation of Urban MObility) platform is employed to model microscopic interactions among SAVs, transit passengers, and other traffic. Results show that the use of smaller, but more frequent, SAVs leads to reduced passenger waiting times but increased vehicle travel times. More frequent services of smaller SAVs do not, in general, significantly affect general traffic due to shorter dwell times. Overall, using smaller SAVs instead of the large 40-seat SAVs can reduce system costs by up to 4% while also reducing passenger waiting times, under various demand levels and passenger loading factors. However, the use of 5-seat SAVs does not always have the lowest system costs., Author(s): Huang Yantao [1]; Kara M. Kockelman (corresponding author) [1]; Long T. Truong [2] 1. Introduction Automated vehicles (AVs) and shared mobility will fundamentally change the future traffic pattern, by [...]