In this study, we examine if observed line-level changes in OCTA bus boardings could be partly attributed to AB 60, while controlling for differences in transit supply, socioeconomic variables, gas prices, and the built environment. Using fixed effects panel data models, we analyzed monthly boardings on different OCTA route classifications—local, community, Express, and station link routes—one year before (2014) and two years after (2015 and 2016) AB 60’s implementation.
More systematic coordination between transportation and housing development is increasingly recognized as a promising strategy for creating more sustainable communities. In California, the importance of transportation-housing coordination is reflected in recent legislative efforts to address the state’s long-standing housing affordability crisis. One approach is to encourage higher density affordable housing developments near transit or in similarly transportation-efficient areas, such as locations with low vehicle miles traveled (VMT). However, little is known about how transportation access should be considered in guiding housing development, what challenges can arise from coordinating housing development with transportation, and what the state can do to better deal with these challenges and achieve more equitable residential densification.
During the pandemic, California’s four major rail systems— Bay Area Rapid Transit (BART), San Diego Metropolitan Transit System (MTS), Sacramento Regional Transit (SacRT), and Los Angeles County Metropolitan Transportation Authority (LA Metro)—experienced an average ridership decline of 72 percent between 2019 and 2021. BART had the greatest decrease (87 percent) and MTS the lowest (47 percent). However, ridership changes varied significantly across individual stations, with stations located in the central business district or at the end of lines having the highest ridership losses. Land use, development density, and the pedestrian environment are strongly associated with station-level transit ridership. We examined how these characteristics affect transit ridership pre- and post-COVID and how they differ across station types based on longitudinal data collected between 2019 and 2021 for 242 rail stations belonging to BART, MTS, SacRT, and LA Metro.
Microtransit is a mobility service that dynamically routes and schedules 6- to 20-seat vehicles to serve passengers within a defined region. Microtransit services are similar to ride-pooling services operated by Transportation Network Companies (e.g., Uber, Lyft); however, microtransit services are owned by cities or transit agencies. Integrating microtransit services with traditional fixed-route transit (FRT) has been touted as a means to attract more riders to public transit generally, improve mobility and sustainable transportation outcomes (e.g., reduce greenhouse gasses and local pollutants), and provide better accessibility to disadvantaged travelers. However, few academic studies have evaluated these claims. To address this gap, we surveyed California transit agencies that currently operate or recently operated microtransit services to obtain insights into integration challenges. We also developed an agent- and simulation-based modeling framework to evaluate alternative system designs for integrating FRT and microtransit in downtown San Diego and Lemon Grove, a suburban area in San Diego County.
Much has been written about the potential benefits of electric and connected vehicles. However, one important, but often overlooked, implication of electrifying trucks is that if they are powerful enough (such as the Tesla semi), they can eliminate the moving bottleneck or queuing effect created by slow-moving conventional heavy-duty trucks because electric trucks are much more responsive compared to conventional diesel trucks because electric motors provide maximum torque from a standstill. This could substantially increase road capacity in areas with high commercial truck traffic, especially around major ports or logistics complexes, thus alleviating the need to add new lanes to local freeways.
California aims to replace gasoline and diesel light-duty vehicles (LDVs) with zero-emission LDVs, many of which will be plug-in battery electric vehicles (BEVs) and achieve 100% zero-carbon electricity by 2045. Large-scale plug-in BEV deployment will substantially increase electricity demand, particularly during peak hours (4:00pm to 9:00pm) when renewable energy is in short supply. Popular strategies for charging BEVs with electricity produced from renewable energy include smart charging and creating more energy storage that soaks up renewable energy during the day and dispenses it later when needed. These strategies, however, may not be enough. Consumer acceptance limits smart charging, and increased energy storage capacity is expensive. Another potential strategy involves lowering the overall demand for electricity by shifting BEV trips to electric-powered bicycles (e-bikes). While e-bikes cannot entirely replace BEV trips, they are ideal for short trips (five miles or less). Currently, 64% of US vehicle trips fall into the short trip category.
Health concerns and government restrictions during the COVID-19 pandemic caused a sharp increase in telecommuting (i.e., doing paid work at home or possibly an alternate worksite). In addition to reducing vehicle miles traveled (VMT), decreasing energy use, and lowering emissions of air pollutants and greenhouse gases (GHG), telecommuting may offer numerous other co-benefits, including increasing the worker pool, decreasing time and costs associated with travel, improving work-life balance, and decreasing stress. It may also stimulate greater use of non-motorized and active modes of travel (e.g., walking, biking, taking transit).
There are two operational objectives for optimizing the operation of HOT lanes: (i) maintain free-flow conditions on HOT lanes and (ii) move as many vehicles as possible through HOT lanes to minimize the travel corridor’s total delay. Meeting these objectives will help guarantee trip time reliability of both HOVs and paying SOVs and minimize congestion on general purpose (GP) lanes. The key factor in achieving these objectives is the price charged to SOVs, which determines the percentage of SOVs choosing to use the HOT lanes. This in turn requires operators to adjust the toll fee in response to changing levels of traffic congestion. However, achieving these goals efficiently is contingent upon dynamic pricing strategies where tolls are adjusted in real time in response to traffic levels to maximize the total throughput while preventing queuing on the HOT lanes.
Transportation pricing strategies aim to manage vehicle travel demand, collect revenue, or force drivers to internalize the costs they impose on other persons (e.g., delayed travel time) and physical infrastructure. Pricing strategies include parking pricing, cordon- and area-based congestion pricing, road-usage charges (RUCs), and high-occupancy toll (HOT) lane pricing. These pricing strategies were, however, designed before the advent of ride-sourcing companies (i.e., Transportation Network Companies or TNCs) and automated vehicles (AVs). Hence, the efficacy of existing pricing strategies in a world with TNCs and a future world with AVs is unclear. Moreover, future pricing strategies must consider the behavior of TNC fleet operators in addition to private vehicle drivers.
