All Research Products

To mitigate climate change and air pollution, multiple US states and other countries have been crafting policies aimed at shifting sales from conventional vehicles to plug-in electric vehicles. A key to developing these policies is understanding how financial incentives affect consumers’ decisions to purchase or lease PEVs. To better understand this, researchers at the University of California, Davis, analyzed survey responses from approximately 2,800 California PEV owners.

In this study, researchers from the University of Southern California investigated the adoption of battery electric heavy-duty trucks (BEHDTs) in the short-haul freight movement sector and the drayage industry.

Lithium-ion battery (LIB) advancements include the use of rapidly evolving and chemically diverse batteries as well as larger battery packs, raising concerns about battery production sustainability as well as battery end-of-life (EoL). This study seeks to respond to these concerns by analyzing potential pathways for EoL EV batteries, quantifies flows of retiring EV battery materials, proposes economically and environmentally preferable LIB EoL strategies, and recommends pertinent policies with an emphasis on environmental justice.

A robust rideshare system needs to take uncertainties such as traffic congestion and passenger cancellations into account. In this report, the authors propose a data-driven stochastic rideshare system that integrates those sources of uncertainties.

The adoption of battery electric trucks (BETs) has the potential to significantly reduce greenhouse gas emissions; however, challenges like shorter driving range and lower payload capacity necessitates a better understanding of energy-efficient dispatching of BET fleets. This project addresses that concern through developing an adaptive large neighborhood search algorithm that, through experimenting with a real-world case study, is shown to achieve significant energy consumption reduction.

An increasing diversity of vehicle types, paired with a growing demand for PEVs, has major implications for vehicle miles traveled (VMT), air pollution, and emissions. To better understand what is likely to happen, researchers predict household vehicle preference and VMT by vehicle body and fuel type.

Transit-oriented development—higher-density residential or mixed-use development centered around high-quality transit stations—has emerged as a strategy to reduce greenhouse gases while increasing housing supply. Researchers at the University of California, Davis completed in-depth case studies of 11 California cities to understand their mix of strategies and how they have needed to reconcile sometimes competing policy goals in advancing transit-oriented development.

Researchers at the University of California, Davis collected GPS-based travel diary data from individual micromobility users from 48 cities in the US and examined their travel behavior and micromobility use patterns. This policy brief summarizes the findings from that research and provides policy implications.

CarpoolSim is a new scalable analytical framework designed evaluate the potential performance and impact of intelligent carpooling system (ICS) for regional networks. Designed to be directly integrated into the travel demand modeling process, CarpoolSim uses a two-stage approach: 1) a filtering step with a set of comprehensive filtering conditions, to eliminate unreasonable carpool matches, given spatiotemporal constraints; and 2) an optimization step, to match as many carpools as possible (and eliminate any remaining assignment conflicts).

This research explicitly models taste heterogeneity towards pooled ridehailing. In addition, unlike existing studies either at the person level or employing stated-preference data, a trip-level analysis is performed in connection with revealed preferences, which generates more realistic and relevant implications to policy and practice.

This paper describes a model for developing highly resolved, time-of-day specific electric vehicle charging demand profiles from travel survey data.

This dataset is associated with the research report, "Developing an Efficient Dispatching Strategy to Support Commercial Fleet Electrification," and consists of real-world, typical fleet movements to evaluate the performance of the researchers' proposed dispatching strategy.

This article addresses the energy-efficient dispatching of BET fleets, considering backhauls and time windows.

In partnership with the Southern California Regional Transit Training Consortium (SCRTTC), the California State University at Long Beach expanded potential audiences and offered program support for the Electric Vehicle Transit Bus High Voltage Safety Awareness class, which was previously developed under the National Center for Sustainable Transportation. The course was designed to enhance a technician's basic electrical skills and 2-circuit diagnosis, while teaching students how to work with a Digital Volt-Ohm Meter.

Some field studies have observed reductions in travel time by coordinating freeway ramps with adjacent arterial signals. To advance the investigation, the researchers propose an integrated traffic management strategy that involves variable speed limit, lane change, ramp metering for freeway traffic flow control, and a traffic-responsive signal control scheme for adjacent traffic light intersections.

This paper: (1) uses empirical data and conducts descriptive and comparative analysis using a spatial lag model to analyze the factors influencing average cruising time (ACT) related to parking search, and (2) proposes a novel framework to predict grid-based ACT and to estimate average emission metrics (AEM).

This study contributes to our growing understanding of environmental justice rising from vehicle electrification, underscoring the need for policy frameworks that create a more equitable transportation system.

This study models the active transportation and vehicular travel traveling modes in a multi-modal network problem taking into account TNC services, such as, e-hailing, e-pooling, and express pool. The objective is to analyze passengers' choices among active transportation, solo driving, and TNC services, considering various trade-offs related to health, monetary, time, and inconvenience costs, and their subsequent impacts on traffic network performance.

This dissertation introduces new novel data-driven coordination techniques through intelligent controlled charging applied to drayage trucks operations and indirect controlled charging by proposing innovative pricing strategies for LD, MD, and HD EVs.

In this dissertation, advanced eco-trajectory planning algorithms have been developed to address four challenges.