policy brief

Charging-as-a-Service is an Innovative Business Model that Could Help with California’s Vehicle Electrification Goals

Abstract

Access to electric vehicle (EV) charging infrastructure is critical to advancing California’s EV adoption goals. The California Energy Commission has projected the state needs “nearly 1.2 million” chargers by 2030 “to meet the fueling demands of 7.5 million passenger plug-in electric vehicles.” Currently, California has about 152,000 publicly available EV chargers.

Innovative asset ownership models, like charging-as-a-service (CaaS), could help overcome some of the barriers to deploying and maintaining charging infrastructure. For example, CaaS providers could procure, install, maintain, and replace charging equipment for subscription customers. To better understand how CaaS solutions could expand EV use and charging access, this researchers conducted semi-structured interviews with 13 CaaS companies, electric utilities, and customers to identify the perceptions, challenges, and opportunities of the CaaS business model in addressing charging station needs in California.

policy brief

California’s High-Speed Rail Yields the Greatest Accessibility Gains to the Most Vulnerable Communities

Abstract

A major criticism of California’s high-speed rail project is that it will mainly serve urban elites and that low-income people and people of color likely won’t be able to afford the fares. Also, the project may benefit the middle-income group the least since the proposed station locations, usually in or near city centers, will probably serve high- and low-income populations better than middle-income families. Besides these arguments, however, there are very few studies that have analyzed the equity impacts of California’s high-speed rail project. Current studies have either focused on benefits to California residents as a whole with little consideration to the specific opportunities for how high- speed rail will improve the lives of marginalized groups; or only studied the disproportionate adverse impacts received by marginalized groups.

research report

Enhanced Perception with Cooperation between Connected Automated Vehicles and Smart Infrastructure

Abstract

This project showcased how advanced infrastructure data supports connected automated driving systems in perceiving their surroundings cooperatively. The UCLA Mobility Lab established a smart intersection on the UCLA main campus, collecting infrastructure LiDAR data and combining it with sensor and global navigation satellite system data for research on cooperative perception. It also examined the system’s resilience to data spoofing attacks via the V2X channel from a compromised onboard unit (OBU), evaluating different attack scenarios to understand emerging security risks in V2X-based cooperative perception technologies.

presentation

Sustainable Freight 2025 Progress Report

presentation

Supporting Infrastructure for Zero-Emission Trucks in California: A Data-Driven Simulation Approach Using an ALNS-VRP Framework

presentation

Data-Driven Modeling for Public Truck Charging Infrastructure

presentation

Supporting Infrastructure for Zero‐Emission Trucks in California: A Data-Driven Simulation Approach Using an ALNS-VRP Framework

published journal article

Transitioning Ridehailing Fleets to Zero Emission: Economic Insights for Electric Vehicle Acquisition

Abstract

Under California’s Clean Miles Standard (or SB 1014), transportation network companies (TNCs) must transition to zero-emission vehicles by 2030. One significant hurdle for TNC drivers is the electric vehicle (EV) acquisition and operating costs versus an internal combustion engine (ICE) vehicle. This study therefore evaluates net TNC driving earnings through EV acquisition pathways—financing, leasing, and renting—along with EV-favoring policy options. Key metrics assessed include (1) total TNC income when considering service fees, fuel costs, monthly vehicle payments, etc., and (2) the time EVs take to reach parity with their ICE counterparts. Monthly comparisons illustrate the earning differentials between new/used EVs and gas-powered vehicles. The analyses employs TNC data from 2019 to 2020 suggesting that EV leasing is optimal for short-term low-mileage drivers; EV financing is more feasible for those planning to drive for TNCs for over two years; EV rentals are only optimal for higher mileages, and they are not an economical pathway for longer-term driving. Sensitivity analyses further indicate that EV charging price discounts are effective in shortening the time for EVs to reach cost parity over ICEs. Drivers may experience a total asset gain when reselling their TNC vehicle after two to three years.

published journal article

A Case for Race and Space in Auto Ownership Modeling: A Los Angeles County Study

Abstract

Auto ownership behavior is driven by complex relationships that can vary dramatically across different traveler groups and communities. Differences in auto ownership among racial groups have been of particular interest, given ongoing efforts to advance equity in transportation outcomes. There are a number of studies documenting racial disparities in auto ownership associated with racial and ethnic residential clustering, termed “automobile mismatch.” Yet, these differences in auto ownership behavior by race and residential location are virtually never considered in models of travel behavior, despite calls for the consideration for race in transportation planning and decision making. This study aims to bridge the gap between understandings of the connections between race and space and transportation outcomes, using Los Angeles County as a case study. A series of auto ownership model specifications are used to investigate statistical connections between the racial and ethnic categories of residents, and neighborhoods, revealing systematic variations across racial and spatial dimensions. The composite model, which includes racial and spatial indicators, outperforms the base model, suggesting that the inclusion of race and space explains significantly more information on variations in auto ownership and provides a superior fit to the data. Our findings also suggest that the exclusion of racial and spatial indicators may lead to overestimation of certain effects, and may completely misrepresent the importance of certain household, individual-level, and built environment effects in explaining auto ownership preferences. Given the increasing attention to equity and representation in transportation outcomes, models that exclude considerations for race and space may be poorly positioned to support meaningful transportation equity analyses.

preprint journal article

Political Preferences and Transport Infrastructure: Evidence from California's High-Speed Rail

Abstract

We study how political preferences shaped California’s High-Speed Rail (CHSR), a largetransportation project approved by referendum in 2008. Voters’ support responded significantly to the projected economic gains in their tract of residence, as measured by a quantitative model of high-speed rail matched to CHSR plans. Given this response, a revealed-preference approach comparing the proposed network with alternative designs identifies strong planner’s preferences for political support. The optimal politically-blind design would have placed the stations nearer to California’s dense metro areas, where it was harder to sway votes, thus increasing the projected economic gains.