Understanding how residents feel about neighborhood changes due to new development along transit corridors (often referred to as transit-oriented development) remains understudied despite growing concerns over displacement and gentrification. Studies that examined these concerns are largely based on analyzing land use, housing values, and socio-economic shifts (i.e., who is moving in and out of neighborhoods), and do not provide conclusive evidence that transit-oriented development (TOD) is linked to neighborhood gentrification and displacement. Prior surveys of residents living near transit indicate a generally positive assessment of TOD in terms of improved walkability and accessibility but also express concerns over pedestrian safety and parking related to increased traffic and new commercial development. However, recent studies counter this relatively positive assessment of TOD, particularly among activists and community organizers in low-income communities of color.
Growing poverty in America’s suburbs challenges their image as single-family residential communities for middle class, predominantly white families. Research shows that suburban areas now have the largest share of households under the poverty line. Since these areas have lower density development and lower levels of public transit service compared to urban areas, living in the suburbs may pose accessibility challenges for low-income households, particularly those without a personal vehicle. To explore housing and transportation issues associated with the suburbanization of poverty, we combined U.S. Census data from Contra Costa County, which has the highest rates of suburban poverty in the San Francisco Bay Area, and online and in-person surveys with individuals who earn less than 80% of the Area Median Income (AMI), around $75,000. This research identifies demographic and external factors that lead low- and moderate-income households to move to suburban areas, accessibility barriers faced by low- and moderate-income suburban households, and how transportation use and transportation and housing costs differ between urban and suburban low-income residents in the Bay Area.
A lack of reliable and affordable transportation options exacerbates socioeconomic inequities for low-income individuals, especially people of color. Universal basic mobility (UBM) programs are a new approach to alleviating financial barriers to travel. These programs provideindividuals with funds to pay for a variety of mobility options such as transit and shared modes (e.g., scooter share, bike share, ridehail). Early results suggest that UBM programs can have a range of positive impacts.
Our research chronicles the emergence of eight UBM programs in the US. Portland, Oregon, was the first to launch a UBM program in 2017 and has hosted two additional UBM programs over the years. There are, or have been, UBM pilots and/or programs in the California cities of Sacramento, Oakland, Los Angeles, and Stockton as well as in Pittsburgh, Pennsylvania. To compare these programs, our research team conducted interviews with city representatives and stakeholders and reviewed reports and other published materials.
Located on the tectonic boundary with multiple active faults, the San Francisco Bay Area is highly vulnerable to earthquakes. The United States Geological Survey (USGS) has estimated a 72% probability of an earthquakewith a magnitude of 6.7 or greater striking the region within the next 30 years. Historical seismic events have demonstrated the profound impact earthquakes can have on transportation systems. During the 1989 Loma Prieta Earthquake, the closure of the San Francisco-Oakland Bay Bridge, a critical transit route for San Francisco commuters, left nearly 400,000 commuters
and approximately 245,000 vehicles daily with limitedalternative routes.
Many small, resource-strapped communities located in areas vulnerable to wildfire don’t have resources to conduct dedicated evacuation studies and many do not consider the impact of background traffic (i.e., normal traffic rather than evacuating traffic) on evacuation. In response, we explored the performance of several generalizable evacuation strategies with background traffic for representative communities in Marin County, including the Ross Valley, Woodacre Bowl, Tamalpais Valley, and an area near Highway 101 and Ignacio Boulevard in Novato (hereafter referred to as ‘Novato Neighborhood’). The strategies we explored include vehicle reduction (i.e., evacuees share a vehicle), phased evacuation (i.e., evacuees in different zones have different departure times), and off-street parking (i.e., street parking is prohibited on a high-fire Red Flag Day to increase overall road capacity in the event of an evacuation). We then tested each strategy using a wildfire-traffic simulation framework.
Shared micromobility (bikesharing and scooter sharing) experienced market growth since 2021, rebounding from the pandemic across markets in the US, Mexico, and Canada. In partnership with the North American Bikeshare and Scootershare Association (NABSA) and Toole Design, researchers at the Transportation Sustainability Research Center (TSRC) at UC Berkeley have collaborated on the data collection and analysis of the shared micromobility industry metrics through a series of annual reports beginning in 2019. This includes a series of operator and agency surveys.1 Most recently, TSRC researchers collaborated on an Operator Survey (n=29) and an Agency Survey (n=52), distributed between January 2023 and June 2023, of all known shared micromobility operators and agencies as part of the 2022 state-of-the-industry report. Similar surveys were deployed in January 2022 and May 2022. These surveys include questions about shared micromobility systems2 operating within those agency jurisdictions and operator markets.
The increasing reliance on transportation network companies (TNCs) and delivery services has transformed the use of curb space. The curb space is also an important interface for bikeways, bus lanes, street vendors, and paratransit stops for passengers with disabilities. These various demands are contributing to a lack of parking, resulting in illegal and double-parking and excessive cruising for spaces and causing traffic disturbance, congestion, andhazardous situations.
Transportation agencies are increasingly relying on tolls to raise revenue and to mitigate congestion, but conventional fixed tolls do not necessarily encourage offpeak use of infrastructure, and high tolls can dampen economic productivity. Dynamically adjusting pricing based on demand can incentivize travelers to avoid peak traffic periods and shift it to other modes, but given the unpredictable nature of traffic, travelers lack the information necessary to accurately predict congestion, so dynamic pricing has minimal effect on demand. Dynamic toll pricing also poses equity concerns for those who lack other travel options, such as access to transit. A simple “futures market” pricing mechanism has the potential to address these concerns—travelers can lock in a price for expected trips by prepaying for future tolls, with the future price increasing as more travelers book an overlapping time slot. To evaluate the effectiveness of a futures market to impact travel demand, trip density, traffic flow, and revenue, this research conducted a sensitivity analysis of elasticity and pricing constraints.
To operate safely, autonomous vehicles (AVs) rely on external sensors such as cameras, light detection and ranging (LiDAR) technology, and radar. These sensors pair with machine learning-based perception modules that interpret the surrounding environment and enable the AV to act accordingly. Perception modules are the “eyes and ears” of the vehicle and are vulnerable to cybersecurity attacks. The most critical and practical threats, however, arise from physical attacks that do not require access to the AV’s internal systems. The risks of these types of attacks are still unknown. To advance the field in this area, we conducted the first ever quantitative risk assessment for physical adversarial attacks on AVs. First, we identified relevant attack vectors, or types of cyber security attacks, targeting AV perception modules. Next, we conducted an in-depth analysis of the stages of an attack. Finally, we used these exercises to identify risk metrics and perform a subsequent computation of risk scores for different attack vectors. Through this process, we were able to quantitatively rank the real-life risks posed by different attack vectors identified in existing research. This analysis provides a framework for comprehensive risk analysis to ensure the safety of AVs on our roadways.
