Travel Demand

The NCST develops and assesses strategies to promote multi-modal travel and reduce car dependence in urban, suburban, and rural settings for both passenger and goods movement, as well as the potential of these strategies to improve accessibility to jobs, housing, and other activities for disadvantaged populations. Studies have evaluated strategies for shifting travel from solo driving to more efficient, low-carbon modes, including transit, walking and biking, and “new mobility” services, such as ride-hailing, bike-sharing, and micro-transit. Other studies have addressed the implications for vehicle-miles-traveled (VMT) of “logistics sprawl,” automated cars, and highway capacity. 

How Do People Receive Information About Public Transit?

  • Principal Investigator Kari E. Watkins, Ph.D.
  • University of California, Davis
This research aims to explore how both transit riders and non-riders access public transit information for the purpose of planning and taking trips on transit
Project Status
In Progress

Impacts of Remote/Hybrid Work and Remote Services on Activity and Transportation Patterns

  • Principal Investigator Giovanni Circella, Ph.D.
  • University of California, Davis
This project will greatly improve the understanding of the impacts of remote/hybrid work and other remote services and inform State and planning agencies by shedding light on the complex ways remote activities affect short‐term daily routines (e.g., telecommuting vs. commuting trips, travel mode choice, and spatial/ temporal trip distributions) and long‐term choices (vehicle choice, residential location and real estate development), and will help understand the impacts on vehicle miles traveled (VMT) and transportation‐based greenhouse gas (GHG) emission impacts. 
Project Status
In Progress

Implementing and Evaluating Machine Learning Algorithms for Bikeshare System Demand Prediction

  • Principal Investigator Mehdi Azimi, Ph.D.
  • Texas Southern University
This research project will develop models for Houston bikeshare system demand prediction at the station level by leveraging data on station activities. Accurate prediction of bikeshare demand has the potential to transform the way these systems are managed and integrated into urban transportation networks, leading to improved efficiency, customer satisfaction, and sustainability.
Project Status
In Progress

Incorporating Infrastructure and Vehicle Technology Requirements, Changes in Demand, and Decarbonization Policies' Considerations into Freight Planning

  • Principal Investigator Miguel Jaller, Ph.D.
  • University of California, Davis
This research aims to develop an equitable and sustainable freight‐oriented land use methodology to support future planning activities, facilitate the integration of freight activity across urban, suburban, and rural areas, and facilitate the transition of heavy‐ and medium‐duty vehicles toward zero‐emission. The project will analyze freight distribution patterns considering supply and demand and estimate social, environmental, and labor impacts in different communities.
Project Status
Complete

Investigating Transportation Decarbonization through Transit and Rideshare Electrification: A Scenario Analysis with Large-Scale Models

  • Principal Investigator Mehdi Azimi, Ph.D.
  • Texas Southern University
This project utilizes the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) workflow to evaluate potential outcomes of electrification policies, specifically for transit and rideshare systems. This will be accomplished by harnessing a large-scale agent-based activity-based transportation modeling tool designed for the Houston Metropolitan Area.
Project Status
In Progress

Investigation of the Abilities and Limitations of Travel Demand Modeling in Informing Decision-Making

  • Principal Investigator Susan Handy, Ph.D.
  • University of California, Davis
This project will investigate the capabilities and limitations of Travel Demand Models by delineating their ability to accurately forecast project outcomes and determining how susceptible model analyses are to bias. The findings inform policymakers of the opportunities and limitations in relying on TDMs as regulatory tools.
Project Status
In Progress
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