Dr. Yantao Huang's Research
Understanding the Impacts of Autonomous Vehicles on Long-distance Travel Choices across Texas
As autonomous vehicles (AVs) become increasingly available over the coming years, their travel, trade, emissions, cost, and other implications need to be anticipated. This study gathers new survey data to explore American’s preference for traveling a long distance (over 75 miles, one-way) in an AV, and how AVs can impact people’s travel choices, such as travel mode, trip purpose, travel party size, departure time of day, trip frequency and overnight stay decisions. Synthesized households and persons of the entire US from ACS and PUMS data are used to simulate the long-distance travel demand by AVs.
Electric Vehicle Charging Station Locations: Elastic Demand, Station Congestion, and Network Equilibrium
The issue of long-duration battery charging makes charging-station placement and design key for battery-only electric vehicle (BEV) adoption rates. This work uses genetic algorithms to identify profit-maximizing station placement and design details, with applications that reflect the costs of installing, operating, and maintaining service equipment, including land acquisition. Fast electric vehicle charging stations are placed around a congested city’s network subject to stochastic demand for charging under a user-equilibrium traffic assignment. BEV users’ station choices consider endogenously-determined travel times and on-site charging queues. The model allows for congested-travel and congested-station feedback into travelers’ route choices under elastic demand and BEV owners’ station choices, as well as charging price elasticity for BEV charging users.
Shared Autonomous Vehicle Fleets to Serve Chicago's Public Transit
Shared fully-automated vehicles (SAVs) will provide different services in the future, including door-to-door (D2D) service, first-mile last-mile (FMLM) connections to transit stations, and low-cost public transit service. This study leverages the agent-based simulator POLARIS to analyze the deployment of the D2D, FMLM, and low-cost transit SAV services for a 5% population sample of the Greater Chicago region.
Simulating Impacts of Shared, Automated, Electric Vehicles on Public Transit Networks and the Electric Power System
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 study 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.
What Will Autonomous Trucking Do to U.S. Trade Flows? Application of the Random-Utility-Based Multi-Regional Input-Output Model
This study anticipates changes in U.S. highway and rail trade patterns following widespread availability of self-driving or autonomous trucks (Atrucks). It uses a random-utility-based multiregional input-output (RUBMRIO) model, driven by foreign export demands, to simulate changes in freight flows among 3109 U.S. counties and 117 export zones, via a nested-logit model for shipment or input origin and mode, including the shipper’s choice between autonomous trucks and conventional or human-driven trucks (Htrucks). Different value of travel time and cost scenarios are explored, to provide a sense of variation in the uncertain future of ground-based trade flows.
The Rise of Long-Distance Trips, in a World of Self-Driving Cars: Anticipating Trip Counts and Evolving Travel Patterns Across the Texas Triangle Megaregion
The Texas Triangle megaregion contains Texas’ largest cities and metropolitan areas, and thereby most of the state’s economic and social activities. This study anticipates the impacts of self-driving, full automated or “autonomous” vehicles (AVs), shared AVs (SAVs), and “autonomous” trucks (Atrucks) on travel across this important megaregion using year 2040 land use (and network) forecasts. Various Statewide Analysis Model (SAM) data are leveraged to anticipate the impacts of AVs’, SAVs’ and Atrucks’ impacts on destination and mode choices. A travel demand model with feedback is implemented to forecast changes in vehicle-miles traveled (VMT), congestion, and travel patterns across the megaregion.
