This session covers various aspects of shared automated and autonomous vehicles including operations, performance, willingness-to-pay and operational policies.
Shared Autonomous Vehicle Fleet Performance: Impacts of Parking Limitations and Trip Densities
Haonan Yan, University of Texas, AustinShow Abstract
Kara M. Kockelman (firstname.lastname@example.org), University of Texas, Austin
Krishna Murthy Gurumurthy, University of Texas, Austin
This study micro-simulates 2% and 5% of the region’s 9.5 million daily person-trips and 20% of trips in the central Twin Cities with shared autonomous vehicles (SAVs) in the 7-county Minneapolis–Saint Paul region using MATSim to appreciate the effects of different trip-making densities and curb-use restrictions. Results suggest the average SAV in this region can serve at most 30 person-trips per day with less than 5 minutes average wait time, but generating 13% more vehicle-miles traveled (VMT). With dynamic ride-sharing (DRS), SAV VMT fell, on average, by 17% and empty VMT (eVMT) fell by 26%. Compared to idling-at-curb scenarios, parking-restricted scenarios generated 8% more VMT . Relying on 52 mi/gallon hybrid electric SAVs is estimated to lower travelers’ energy use by 21% and reduce tailpipe emissions by 30%, assuming no new or longer trips. A 106 mi/gallon equivalent battery-electric fleet does much better by lowering energy use by 64%.
Shared Autonomous Vehicle Fleet Operations For First-Mile Last-Mile Transit Connections With Dynamic Ride-Sharing
Yantao Huang, University of Texas, AustinShow Abstract
Kara M. Kockelman (email@example.com), University of Texas, Austin
Venu Garikapati, National Renewable Energy Laboratory (NREL)
Shared automated vehicles (SAVs) have the potential to promote transit ridership by providing efficient first-mile last-mile (FMLM) connections through reduced operational costs to fleet providers as well as lower out-of-pocket costs to riders. To help plan for a future of integrated mobility, this paper investigates the impacts of SAVs serving FMLM connections, as a mode that provides flexibility in access/egress decisions and is well coordinated with train station schedules. To achieve this objective, a novel dynamic ride-sharing (DRS) algorithm was introduced to match SAVs with riders while coordinating the riders’ arrival times at the light-rail station to a known train schedule. Microsimulations of SAVs and travelers throughout two central Austin neighborhoods show how larger service areas, higher levels of SAV demand, and longer arrival times between successive trains require larger SAV fleet sizes and higher SAV utilization rates to deliver close traveler wait times. Four-person SAVs appear to perform similar to 6-seat SAVs but will cost less to provide. Using a DRS algorithm tightly coordinated with train arrivals (every 15 minutes) delivers 87% of travelers to the station in time to catch the next train, while uncoordinated SAV assignments result in just 57% of travelers arriving in time to catch the next train.
System Level Policies For Pooled Autonomous Vehicles
Yves Räth, Eidgenossische Technische Hochschule ZurichShow Abstract
Miloš Balać, Eidgenossische Technische Hochschule Zurich
Sebastian Hörl, IRT SystemX
Kay Axhausen, Eidgenossische Technische Hochschule Zurich
With the introduction of autonomous vehicles (AV), new operating schemes for public transport services will become available. Autonomous Transit on Demand (AToD) is an attractive alternative to the current modes of transportation. It is a station-based service that allows passengers' pooling in AVs and direct connections from any PUDO (pick-up and drop-off) station to any other without detours or dynamic ride sharing. A minimal necessary headway of 15 minutes ensures the occurrence of pooling. In this paper, the impact of AToD on the modal share for the city of Zurich, Switzerland, and its surrounding area is modeled using an open-source, agent-based transport simulation framework MATSim. Different operating areas, pricing schemes, and cordon charges are tested on their potential to make use of the benefits of the new mode while avoiding an overflow of AVs in the urban core. Parking demand, as well as an increase of vehicle kilometers traveled can become significant challenges. A pricing scheme that charges the base cost of the AToD relative to the accessibility of the current public transport service is a promising solution to increase the accessibility of the rural areas while maintaining a high modal share for PT in the city center.
Willingness-to-pay for Shared Automated Mobility Using an Adaptive Choice-Based Conjoint Analysis during the COVID-19 Period
Amirreza Nickkar, Morgan State UniversityShow Abstract
Young-Jae Lee (YoungJae.Lee@morgan.edu), Morgan State University
Hyeon-Shic Shin, Morgan State University
Due to recent technological developments, utilizing shared autonomous vehicles (SAVs) has become an interesting topic for both academia and the market. Gaining insight into preferences and priorities for using the next generation of shared mobility was the primary motivation of this study. The goal is to explore the willingness-to-pay (WTP) for a shared mobility service using a detailed user preference analysis for alternative automation type and incentive and policy options. The data was collected between March to May 2020 during the COVID-19 lockdown period in the United States via an online survey and analyzed by the Adaptive Choice-Based Conjoint Analysis (ACBC) method. The results of this study show that walking time to the pickup location is the essential attribute for respondents in choosing a shared mobility service, followed by travel time. Respondents distinctly preferred fully automated vehicles (SAVs) over shared human-driven vehicles (SHVs). Moreover, respondents indicated less willingness-to-share their trips with other riders compared to the previous survey analysis done before the COVID-19 period.
Impacts of Holding Area Policies on Shared Autonomous Vehicle Operations
Richard Twumasi-Boakye, Ford Motor CompanyShow Abstract
Xiaolin Cai, Ford Motor Company
Chetan Joshi, PTV Group
James Fishelson, Ford Motor Company
Andrea Broaddus, Ford
Shared mobility has an important role in supporting existing transportation options in cities. However, when not deployed carefully, shared services may have operational inefficiencies such as low occupancies and increased deadheading. One reason is the spatio-temporal variance in the distribution of urban trip demand which may lead to an unbalanced fleet displaced in cities thus are unable to serve requested trips. Strategically citing holding areas (depots for dispatching fleets) could help improve fleet performance. Therefore, this paper considers shared autonomous vehicle (SAV) fleet operations by modeling the impacts of different holding area policies on service performance. Modeling and comparing multiple holding area policies for tactically deploying SAVs is novel and the insights from this paper can inform service providers on how to cite holding areas for improved performance. We first set up a model of SAV fleet with pooling in the City of Toronto, with 27,951 total SAV trip requests across a 16-hour period. We then integrate six holding area policies estimated using different spatial clustering methods, centralized positioning, and existing taxi stands. Findings indicate that using agglomerative clustering with weighted fleet distribution results in a superior SAV fleet performance by over 30% compared with the worse performing policy, with increased served demand as well as reduced deadheading and waiting times. A single holding area at a high trip density location yields efficient service performance at lower fleets but struggles to serve sparse demand. This method may suffice for operating SAV services within a small geofence with high trip densities.
DISCLAIMER: All information shared in the TRB Annual Meeting Online Program is subject to change without notice. Changes, if necessary, will be updated in the Online Program and this page is the final authority on schedule information.