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Open Access Publications from the University of California

Recent Work

The Transportation Sustainability Research Center fosters research, education, and outreach so that transportation can serve to improve economic growth, environmental quality and equity. Co-Directors are Dan Kammen, the Class of 1935 Distinguished Professor of Energy at UC Berkeley, Tim Lipman, PhD, and Susan Shaheen, PhD. The groups participating in this effort are the:

University of California Transportation Center
University of California Energy Institute
Institute of Transportation Studies
Energy and Resources Group
Center for Global Metropolitan Studies
Berkeley Institute of the Environment

Cover page of Do Incentives Make a Difference? Understanding Smart Charging Program Adoption for Electric Vehicles

Do Incentives Make a Difference? Understanding Smart Charging Program Adoption for Electric Vehicles

(2022)

Climate change and environmental problems have spurred new strategies to reduce fossil fuel consumption in transportation. Two important strategies include a rapid transition to green energy and the replacement of internal combustion vehicles with electric vehicles (EVs). However, the increasing demand for electricity by EVs, especially from time-dependent green sources of energy (e.g., solar, wind), will likely overload the grid at peak hours. Rather than build costly infrastructure improvements for distribution and generation, smart charging programs for EVs could defer charging to off-peak times and better match demand with supply. Yet, little is currently known about people’s willingness to participate in a program and relinquish control of charging to a third party.

Cover page of Understanding California wildfire evacuee behavior and joint choice making

Understanding California wildfire evacuee behavior and joint choice making

(2022)

For evacuations, people must make the critical decision to evacuate or stay followed by a multi-dimensional choice composed of concurrent decisions of their departure time, transportation mode, route, destination, and shelter type. These choices have important impacts on transportation response and evacuation outcomes. While extensive research has been conducted on hurricane evacuation behavior, little is known about wildfire evacuation behavior. To address this critical research gap, particularly related to joint choice-making in wildfires, we surveyed individuals impacted by the 2017 December Southern California Wildfires (n = 226) and the 2018 Carr Wildfire (n = 284). Using these data, we contribute to the literature in two key ways. First, we develop two latent class choice models (LCCMs) to evaluate the factors that influence the decision to evacuate or stay/defend. We find an evacuation keen class and an evacuation reluctant class that are influenced differently by mandatory evacuation orders. This nuance is further supported by different membership of people to the classes based on demographics and risk perceptions. Second, we develop two portfolio choice models (PCMs), which jointly model choice dimensions to assess multi-dimensional evacuation choice. We find several similarities between wildfires including a joint preference for within-county and nighttime evacuations and a joint dislike for within-county and highway evacuations. Altogether, this paper provides evidence of heterogeneity in response to mandatory evacuation orders for wildfires, distinct membership of populations to different classes of people for evacuating or staying/defending, and clear correlation among key wildfire evacuation choices that necessitates joint modeling to holistically understanding wildfire evacuation behavior.

Cover page of Willingness of Hurricane Irma evacuees to share resources: a multi-modeling approach

Willingness of Hurricane Irma evacuees to share resources: a multi-modeling approach

(2022)

Recent technological improvements have greatly expanded the sharing economy (e.g., Airbnb, Lyft, and Uber), coinciding with growing need for transportation and sheltering resources in evacuations. To understand influencers on sharing willingness in evacuations, we employed a multi-modeling approach across four sharing scenarios using three model types: 1) four binary logit models that capture each scenario separately; 2) a multi-choice latent class choice model (LCCM) that jointly estimates multiple scenarios via latent classes; and 3) a portfolio choice model (PCM) that estimates dimensional dependency. We tested our approach by employing online survey data from 2017 Hurricane Irma evacuees (n=368).

The multi-model approach uncovered behavioral nuances undetectable with a single model. First, the multi-choice LCCM and PCM models uncovered scenario correlation, specifically willingness to share for both transportation scenarios and both sheltering scenarios. Second, the multi-choice LCCM found three classes – transportation sharers, adverse sharers, and interested sharers. Transportation sharers were more likely to be female, lower-income, and residents of Southwest Florida compared to adverse sharers. Interested sharers were more likely to be male, long-time residents, and higher-income compared to adverse sharers. Third, families with children were unwilling to share regardless of the model, while spare capacity (i.e., seatbelts, spare beds) had a positive but somewhat insignificant influence on sharing. Fourth, experienced home sharers were more willing to share shelter in the binary logit and PCM models. We suggest that local agencies consider holistic sharing mechanisms across resource types and time (i.e., before, during, and after a hurricane evacuation).

Cover page of Mobility on Demand: State of the Industry Practitioner Census, Fall 2021

Mobility on Demand: State of the Industry Practitioner Census, Fall 2021

(2021)

The Transportation Sustainability Research Center (TSRC) at the University of California, Berkeley is pleased to present the results of the Mobility on Demand (MOD) State of the Industry Practitioner Census. The global pandemic has led to a challenging period for the transportation sector. Nevertheless, the industry has shown resilience and innovation. This industry outlook provides information on MOD and Mobility as a Service (MaaS) developments throughout the United States (U.S.) and highlights some industry changes in response to the pandemic.

Cover page of Impacts of Transportation Network Companies on Vehicle Miles Traveled, Greenhouse Gas Emissions, and Travel Behavior Analysis from the Washington D.C., Los Angeles, and San Francisco Markets

Impacts of Transportation Network Companies on Vehicle Miles Traveled, Greenhouse Gas Emissions, and Travel Behavior Analysis from the Washington D.C., Los Angeles, and San Francisco Markets

(2021)

Transportation Network Companies (TNCs) like Lyft, Uber, and their global counterparts have expanded around the world over the past decade and have changed the way that people travel around cities and regions. The individual mobility benefits provided by TNCs have been clear. Passengers can summon a vehicle quickly via smartphone from almost anywhere to take them almost anywhere, with advance communication on estimated wait time, travel time, and cost. TNCs may also provide users with added mobility benefits, especially for those living in areas where public transit service is infrequent or non-existent. However, the growing popularity of TNCs has forced important questions about their impacts on the overall transportation network. While past research has focused on many different aspects of TNC impacts, including their effects on travel behavior, modal shift, congestion, and other topics, there are still many important questions. This report advances the understanding of TNC effects on vehicle miles traveled (VMT), greenhouse gas (GHG) emissions, and personal vehicle ownership. The research also explores key questions regarding the impact of pooled TNC services, Lyft Shared rides and uberPOOL, and further investigates how TNCs alter the use of other transportation modes, including public transit.

Cover page of Developing Transportation Response Strategies for Wildfire Evacuations via an Empirically Supported Traffic Simulation of Berkeley, California

Developing Transportation Response Strategies for Wildfire Evacuations via an Empirically Supported Traffic Simulation of Berkeley, California

(2021)

Government agencies must make rapid and informed decisions in wildfires to safely evacuate people. However, current evacuation simulation tools for resource-strapped agencies largely fail to compare possible transportation responses or incorporate empirical evidence from past wildfires. Consequently, we employ online survey data from evacuees of the 2017 Northern California Wildfires (n=37), the 2017 Southern California Wildfires (n=175), and the 2018 Carr Wildfire (n=254) to inform a policy-oriented traffic evacuation simulation model. We test our simulation for a hypothetical wildfire evacuation in the wildland urban interface (WUI) of Berkeley, California. We focus on variables including fire speed, departure time distribution, towing of items, transportation mode, GPS-enabled rerouting, phased evacuations (i.e., allowing higher-risk residents to leave earlier), and contraflow (i.e., switching all lanes away from danger).

 

We found that reducing household vehicles (i.e., to 1 vehicle per household) and increasing GPS-enabled rerouting (e.g., 50% participation) lowered exposed vehicles (i.e., total vehicles in the fire frontier) by over 50% and evacuation time estimates (ETEs) by about 30% from baseline. Phased evacuations with a suitable time interval reduced exposed vehicles most significantly (over 90%) but produced a slightly longer ETE. Both contraflow (on limited links due to resource constraints) and slowing fire speed were effective in lowering exposed vehicles (around 50%), but not ETEs. Extended contraflow can reduce both exposed vehicles and ETEs. We recommend agencies develop a communication and parking plan to reduce evacuating vehicles, create and communicate a phased evacuation plan, and build partnerships with GPS-routing services.

Cover page of Advanced Air Mobility: Demand Analysis and Market Potential of the Airport Shuttle and Air Taxi Markets

Advanced Air Mobility: Demand Analysis and Market Potential of the Airport Shuttle and Air Taxi Markets

(2021)

Advanced air mobility (AAM) is a broad concept enabling consumers access to on-demand air mobility, cargo and package delivery, healthcare applications, and emergency services through an integrated and connected multimodal transportation network. However, a number of challenges could impact AAM’s growth potential, such as autonomous flight, the availability of take-off and landing infrastructure (i.e., vertiports), integration into airspace and other modes of transportation, and competition with shared automated vehicles. This article discusses the results of a demand analysis examining the market potential of two potential AAM passenger markets—airport shuttles and air taxis. The airport shuttle market envisions AAM passenger service to, from, or between airports along fixed routes. The air taxi market envisions a more mature and scaled service that provides on-demand point-to-point passenger services throughout urban areas. Using a multi-method approach comprised of AAM travel demand modeling, Monte Carlo simulations, and constraint analysis, this study estimates that the air taxi and airport shuttle markets could capture a 0.5% mode share. The analysis concludes that AAM could replace non-discretionary trips greater than 45 min; however, demand for discretionary trips would be limited by consumer willingness to pay. This study concludes that AAM passenger services could have a daily demand of 82,000 passengers served by approximately 4000 four- to five-seat aircraft in the U.S., under the most conservative scenario, representing an annual market valuation of 2.5 billion USD.