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The COVID-19 pandemic has had a significant impact on public transit ridership in the United States, especially for rail transit. Land use, development density, and the pedestrian environment are strongly associated with station-level transit ridership. This study examines how these characteristics affect transit ridership pre- and post-COVID and how they differ across station types based on longitudinal data for 242 rail stations belonging to Bay Area Rapid Transit, San Diego Metropolitan Transit System, Sacramento Regional Transit, and LA Metro between 2019 and 2021. We found overall a 72% decrease in station-level ridership, but changes were not uniform. Station areas with a higher number of low-income workers and more retail or entertainment jobs tend to have lower ridership declines, while areas with a large number of high-income workers, high-wage jobs, and higher job accessibility by transit had more ridership losses. When comparing station area ridership and activity changes based on mobile phone user data, ridership declined more drastically than activity across all four rail systems, which implies that rail transit riders switched to other modes of transportation when accessing the station areas. Given these findings, it is likely that rail transit services oriented toward commute travel, especially core station areas with jobs for higher income workers, will continue to have an uneven recovery, posing critical implications for transit resilience planning and equity in the post-pandemic era. Considering sources of funding other than passenger fares to sustain rail transit, strategizing to reinvent and reinforce downtowns as destinations, and shifting rail transit services to appeal to non-commute travel can be promising strategies to support rail transit.

Peaking on public transit—the concentration of ridership in peak times and directions into and out of central areas—has waxed in the U.S. over the past century, as public transit has lost more mode share at off-peak times, in off-peak directions, and among non-commute trips. A notable pre-pandemic manifestation of this chronic problem was on Bay Area Rapid Transit, the San Francisco Bay Area’s regional heavy rail system. While BART staved off an absolute ridership decline longer than most American transit operators in the mid- and late-2010s, it did so almost entirely due to peak gains in riders offsetting off-peak losses. As a result, the system experienced worsening passenger crowding at some times and places, expanding underutilization of capacity at many others, and the prospect of enormous expenditures to accommodate rising transbay passenger demand. To examine the factors driving transit use in the 2010s, we model peak and off-peak BART trips as a function of station area and system characteristics. We uniquely use origin–destination pairs as the unit of analysis in order to separately measure influences at both ends of the trip. We find that transfers and travel time most influence peak and off-peak BART ridership and that station-area employment and time-competitiveness with driving particularly influence peak patronage. Over time in our models, the associations between ridership and transit travel time weakened, while the associations between ridership and transfers, employment, and time-competitiveness with driving grew stronger. In sum, we find that the peaking problem plaguing public transit systems for decades worsened in the years leading up to the pandemic—on this one nationally significant U.S. transit system, at least—which poses potentially substantial financial challenges in the years ahead.

We examine pre-COVID declines in transit ridership, using Southern California as a case study. We first illustrate Southern California's unique position in the transit landscape: it is a large transit market that demographically resembles a small one. We then draw on administrative data, travel diaries, rider surveys, accessibility indices, and Census microdata for Southern California, and demonstrate a strong association between rising private vehicle access, particularly among the populations most likely to ride transit, and falling transit use. Because we cannot control quantitatively for the endogeneity between vehicle acquisition and transit use, our results are not causal. Nevertheless, the results strongly suggest that increasing private vehicle access helped depress transit ridership. Given Southern California's similarity to most US transit markets, we conclude that vehicle access may have played a role in transit losses across the US since 2000.

Although the COVID-19 pandemic highly impacted transit ridership as people reduced or stopped travel, these changes occurred at different rates in different regions across the United States. This study explores the impacts of COVID-19 on ridership and recovery trends for all federally funded transit agencies in the United States from January 2020 to June 2022. The findings of this analysis show that overall transit ridership hit a 100-year low in 2020. Changepoint analysis revealed that June 2021 marked the beginning of the recovery for transit ridership in the United States. However, even by June 2022, rail and bus ridership were only about two-thirds of the pre-pandemic levels in most metropolitan statistical areas (MSAs). Only in a handful of MSAs like Tampa and Tucson did rail ridership reach or exceed 2019 ridership. This retrospective study concludes with a discussion of some longer-term changes likely to continue to impact ridership, such as increased telecommuting and operator shortages, as well as some opportunities, such as free fares and increased availability of bus lanes. The findings of this study can help inform agencies about their performance compared to their peers and highlight general challenges facing the transit industry.

Public investment in transit increased following the Great Recession, yet transit use nationally mostly fell, even prior to the 2020 pandemic. We investigate this troubling disjuncture by comparing transit ridership trends during the 2010s in two of America’s largest regions: Greater Los Angeles and the San Francisco Bay Area. While both California regions lost transit riders, we see substantial differences in the scale, timing, geography, and modes of these declines. In the LA area, ridership fell longer and further, spread more across routes, times, and sub-regions and concentrated on the region’s dominant operator. In both regions, increasing auto access appears to have played a central role, albeit in different ways. Greater LA saw increased automobile ownership, particularly among high-propensity transit riders. In the Bay Area, as jobs and housing have dispersed, ridehail services like Lyft and Uber may have eroded non-commute transit use.

Many transit providers changed their schedules and route configurations during the COVID-19 pandemic, providing more frequent bus service on major routes and curtailing other routes, to reduce the risk of COVID-19 exposure. This research first assessed the changes in MARTA service configurations by reviewing the pre-pandemic vs. during-pandemic General Transit Feed Specification (GTFS) files. Energy use per route for a typical week was calculated for pre-pandemic, during-closure, and post-closure periods by integrating GTFS data with MOVES-Matrix transit energy and emission rates. MARTA automated passenger count (APC) data were appended to the routes, and the energy use per passenger mile was compared across routes for the three periods. The results showed that the coupled effect of shift in transit frequency and decrease in ridership from 2019 to 2020 increased route-level energy use for more than 87% of the routes and per-passenger mile energy use for more than 98% of the routes. In 2021, although MARTA service had largely returned to pre-pandemic conditions, ridership remained in an early stage of recovery. Total energy use decreased to about the pre-pandemic level, but per-passenger energy use remained higher than pre-pandemic for more than 91% of the routes. The results confirm that while total energy use is more closely associated with trip schedules and routes, per-passenger energy use depends on both trip service and ridership. The results also indicated a need for data-based transit planning, to help avoid inefficiency associated with over-provision of service or inadequate social distancing protection caused by under-provision of service.View the NCST Project Webpage

During the COVID-19 outbreak, serious concerns were raised over the risk of spreading the infection on public transportation systems. As the pandemic recedes it will be important to determine optimal timetable design to minimize the risk of new infections as systems resume full service. In this study, we developed an integrated optimization model for service line reopening plans and timetable design. Our model combines a space-time passenger network flow problem and compartmental epidemiological models for each vehicle and platform in the transit system. The algorithm can help policy makers to design schedules under COVID-19 more efficiently. The report develops an optimized timetable for the Bay Area Rapid Transit system. We found that if passengers choose other mode of transportation when closing part of the system or decreasing the frequency of service can prevent the spread of infections, otherwise, if passengers choose to use the closest open station, closings will lead to longer waiting times, higher passenger density and greater infection risk. We found that the goal of stopping the spread of infection could be achieved by minimizing the total delay when infections were similar in different districts across the service area. Where infection rates are different in different districts, minimizing the risk of exposure can be achieved by minimizing weighted travel time where higher weights are applied to areas where the infection rate is highest.

This research project examines the link between job access and stop/station level transit ridership. Job access, following recent literature, is measured as the number of jobs that can be reached within a 30-minute transit travel time, including transfers and walk time to access jobs once exiting a transit station. Cumulative opportunity job access measures of this sort—i.e., the number of jobs that can be reached within 30 minutes—have become common in the recent access literature, and those measures have often focused on access via transit. Yet there have been few studies that examine the link between transit job access and transit ridership, and of those none that examine the link at a station or stop level. This study uses station and stop level ridership data for the Los Angeles Metro bus and rail system and the BART rail system in the San Francisco Bay Area. The research team calculated transit job access as jobs that can be reached within 30 minutes, using the Remix software tool. Regression analysis of 1,000 randomly selected Los Angeles bus stops reveals a robust relationship between stop-level ridership and job access. The association between transit job access and bus stop ridership (embarkations and disembarkations at the stop) is statistically significant. Converting that association into an elasticity, if the number of jobs accessible within 30-minutes were to increase by 1 percent, on average stop-level ridership would increase between 0.6 to 0.8 percent. The same association, with similar magnitudes, exists for Metro rail stations and BART rail stations, but due the smaller sample sizes, those relationships are not statistically significant when control variables are added to the regression. The findings show that job access is closely related to ridership at the bus stop level, suggesting transit agencies can increase job access by increasing bus frequency, reducing transfers, siting lines that connect job concentrations to residents, and by improving bus stop/rail station access/egress times.View the NCST Project Webpage

We examine pre-COVID declines in transit ridership, using Southern California as a case study. We first illustrate Southern California’s unique position in the transit landscape: it is a large transit market that demographically resembles a small one. We then draw on administrative data, travel diaries, rider surveys, accessibility indices, and Census microdata for Southern California, and demonstrate a strong association between rising private vehicle access, particularly among the populations most likely to ride transit, and falling transit use. Because we cannot control quantitatively for the endogeneity between vehicle acquisition and transit use, our results are not causal. Nevertheless, the results strongly suggest that increasing private vehicle access helped depress transit ridership. Given Southern California’s similarity to most US transit markets, we conclude that vehicle access may have played a role in transit losses across the US since 2000.

From March 2020 through March 2021, researchers monitored three San Francisco Bay Area transit agencies: two large – Alameda-Contra Costa Transit District (AC Transit), Valley Transportation Authority (VTA); and one small – Tri Delta Transit. As the lockdown was imposed in response to the COVID-19 pandemic, white-collar commuters, students, and the elderly stopped using public transit. Initially, ridership fell 90 percent, and then over the year slowly climbed to less than 50 percent for AC Transit and VTA, and to around 60 percent for Tri Delta Transit. The pace of recovery was not steady as ridership declined during protests in June 2020, during fare reinstatements in autumn 2020 and during the second COVID-19 wave in winter 2020-21. Agencies’ responses to the pandemic consisted of three parts: 1) maintaining health and safety of their employees; 2) minimizing COVID risk for their riders by keeping buses clean and enabling social distancing through capping the number of passengers on buses; and 3) changing their service. There was a direct relationship between the socioeconomic status of the population and transit ridership during the year studied. Higher ridership was observed in low-income areas with a high percentage of Latino, Black and Asian populations. These are generally renters, who do not have a car, but have to go to work either because they are essential workers and/ or are undocumented immigrants who cannot afford staying jobless. On the other hand, in wealthy areas of the Bay Area transit activity all but disappeared.

The Los Angeles County Metropolitan Transportation Authority (Metro) has allocated significant resources (time, money, political) to rework their bus network through the NextGen initiative, the first sweeping overhaul of the bus system in 25 years. Because such overhaul of a bus system requires extensive resources, the next major restructuring of Metro’s bus routes will not occur for at least another decade or two. Thus, it is imperative that Metro aligns their bus service where people actually travel to, rather than merely supporting existing Metro routes. This project analyzes major travel patterns across L.A. County to spot any possible bus service recommendations explicitly not identified in NextGen. Major travel is assessed utilizing Metro’s location-based service (LBS) cell-phone database, Metro's ridership data, and Census “OnTheMap” data. Though our analysis corroborates NextGen’s study revealing most trips in LA County are under 5 miles, we found notable exceptions. For the exceptions, namely in the South Bay, Gateway Cities, and the San Fernando Valley, this project provides critical service opportunities to match Metro service to observed travel patterns. Finally, we suggest a pilot microtransit program, a low-cost investment, to test the actual demand strength of proposed service suggestions.

This report presents and reviews the available sources of data on public transit riders and ridership. We intend it to be a resource for those who manage or simply wish to understand U.S. transit. In conducting this review, we consider the advantages and disadvantages of publicly available data on transit from a variety of public and private sources. We consider as well the relatively scarcer and less available sources of data on other providers of shared mobility, like ride-hail services, that compete with and complement public transit, as well as pieces we see as missing from the transit analytics pie. We conclude by discussing how data gaps both align with existing inequities and enable them to continue, unmeasured, and how the COVID-19 pandemic has made closing these gaps all the more important.

This report presents and reviews the available sources of data on public transit riders and ridership. We intend it to be a resource for those who manage or simply wish to understand U.S. transit. In conducting this review, we consider the advantages and disadvantages of publicly available data on transit from a variety of public and private sources. We consider as well the relatively scarcer and less available sources of data on other providers of shared mobility, like ride-hail services, that compete with and complement public transit, as well as pieces we see as missing from the transit analytics pie. We conclude by discussing how data gaps both align with existing inequities and enable them to continue, unmeasured, and how the COVID-19 pandemic has made closing these gaps all the more important.

Transit patronage plunged staggeringly, from 50 to as much as 94 percent, during the first half of 2020 amidst the worst global pandemic in a century. But transit’s troubles in California date much earlier. From 2014 to 2018, California lost over 165 million annual boardings, a drop of over 11 percent. This report examines public transit in California in the 2010s and the factors behind its falling ridership.We find that ridership gains and losses have been asymmetric with respect to location, operators, modes, and transit users. Transit ridership has been on a longer-term decline in regions like Greater Los Angeles and on buses, while ridership losses in the Bay Area are more recent. While overall transit boardings across the state are down since 2014, worrisome underlying trends date back earlier as patronage failed to keep up with population growth. But reduced transit service is not responsible for ridership losses, as falling transit ridership occurred at the same time as operators instead increased their levels of transit service.What factors help to explain losses in transit ridership? Increased access to automobiles explains much, if not most, of declining transit use. Private vehicle access has increased significantly in California and, outside of the Bay Area, is likely the biggest single cause of falling transit ridership. Additionally, new ridehail services such as Lyft and Uber allow travelers to purchase automobility one trip at a time and likely serve as a substitute for at least some transit trips. Finally, neighborhoods are changing in ways that do not bode well for public transit. Households are increasingly locating in outlying areas where they experience longer commutes and less transit access to employment. At the same time, a smaller share of high-propensity transit users now live in the state’s most transit-friendly neighborhoods.While the 2010s proved a difficult decade for public transit in California, and the opening of the current decade has been an even bigger challenge, transit remains an essential public service. Effectively managing transit recovery in California will require a clear-eyed understanding of the substantially altered environment within which these systems large and small must now operate.

Transit patronage plunged staggeringly, from 50 to as much as 94 percent, during the first half of 2020 amidst the worst global pandemic in a century. But transit’s troubles in California date much earlier. From 2014 to 2018, California lost over 165 million annual boardings, a drop of over 11 percent. This report examines public transit in California in the 2010s and the factors behind its falling ridership.We find that ridership gains and losses have been asymmetric with respect to location, operators, modes, and transit users. Transit ridership has been on a longer-term decline in regions like Greater Los Angeles and on buses, while ridership losses in the Bay Area are more recent. While overall transit boardings across the state are down since 2014, worrisome underlying trends date back earlier as patronage failed to keep up with population growth. But reduced transit service is not responsible for ridership losses, as falling transit ridership occurred at the same time as operators instead increased their levels of transit service.What factors help to explain losses in transit ridership? Increased access to automobiles explains much, if not most, of declining transit use. Private vehicle access has increased significantly in California and, outside of the Bay Area, is likely the biggest single cause of falling transit ridership. Additionally, new ridehail services such as Lyft and Uber allow travelers to purchase automobility one trip at a time and likely serve as a substitute for at least some transit trips. Finally, neighborhoods are changing in ways that do not bode well for public transit. Households are increasingly locating in outlying areas where they experience longer commutes and less transit access to employment. At the same time, a smaller share of high-propensity transit users now live in the state’s most transit-friendly neighborhoods.While the 2010s proved a difficult decade for public transit in California, and the opening of the current decade has been an even bigger challenge, transit remains an essential public service. Effectively managing transit recovery in California will require a clear-eyed understanding of the substantially altered environment within which these systems large and small must now operate.

Increasing transit use has many benefits, including reducing traffic congestion and greenhouse gas (GHG) emissions. However, riders need to be able to get to a station in order to use transit. Walking is an option only for those within a limited radius of a station. Driving to a station may be feasible for some, but providing sufficient parking can be expensive and land intensive. The rise of ride-hailing companies such as Uber and Lyft presents a new opportunity for bridging the “first-mile” gap to high quality transit. Transit agencies are beginning to launch pilot projects to test public-private partnerships with ride-hailing companies to increase access to transit.This policy brief summarizes findings from researchers at UC Davis who used existing modeling tools and data to understand the potential market demand for a first-mile transit access service in the San Francisco Bay Area. They modeled the likelihood of commuters who drive alone to switch to using ride-hailing and the Bay Area Rapid Transit (BART) rail system to get to work based on travel time, cost, and distance to a BART station. They explored the magnitude of change in overall travel time and cost for travelers who switch from driving alone to using ride-hailing and BART, as well as potential changes to vehicle miles traveled (VMT) and GHG emissions at both the regional and station level.View the NCST Project Webpage

This research brief summarizes the findings from the associated study, which examined the potential to improve transportation system efficiency by reevaluating and redefining the role of public transit and its design principles in the new context of technology and shared mobility. Specifically, it evaluated the financial feasibility of an integrative, multimodal transportation system where public and private mobility services coexist to maximize economic and environmental benefits and free up public transit resources to be reallocated more efficiently.

This study investigates barriers to accessing the Davis Amtrak Station and will inform a pilot program to increase access to the station with on-demand alternatives. The program aims to decrease private vehicle use to access the station and for travel to locations outside of Davis. Currently, the Davis station has the third highest passenger usage along the Capitol Corridor and ridership is increasing, but potential ridership is limited by parking availability. The city has limited interest in or ability to add new parking capacity, however pricing will be introduced to the lot in the future. In addition, the city will introduce a pilot program to support the use of on-demand alternatives and other modes to access the Davis station. The goal of this phase of the study was to develop surveys for Davis residents and current Capital Corridor riders that will assess barriers to use of the station. Such factors likely include limited vehicle and bicycle parking, limited local bus service hours, last mile challenges at the other end of the trip, and the convenience of driving for regional travel. The results of this survey will offer insights into the key factors influencing the use of the Capitol Corridor regional rail, and the importance of station access among those factors. Further, this phase of the study will inform the development and implementation of the city’s pilot program.

Ideally, public transit, by moving more people using fewer vehicles, serves as a backbone of a transportation system. However, most transit systems in the United States suffer from low ridership and high operating costs, thus they provide a significantly compromised mobility service to the transportation system users. Under current transit system design principles, such as service area requirements, inefficiencies in resource use are almost inevitable. Given the opportunities brought by new mobile technology and the environment of mobility as a service, current transit system design principles need to be reevaluated and redefined to enable transit to serve as a backbone in the transportation system. In this seed-grant project, the researchers evaluated whether building an integrated multimodal public transportation system via reallocation of transit resources is financially feasible and environmentally sustainable. They also conducted an in-depth review of related literature and discussed other concerns regarding an integrated system. Based on the results from the case study and review of other recent studies, the authors draw an optimistic conclusion about an integrated service system where public and private mobility service providers coexist.

Over the past decade, while public transit operators in the Los Angeles region started experiencing steep declines in ridership, Pasadena Transit was adding riders. More recently, however, Pasadena Transit’s ridership plateaued and then started to decrease. The Transit Division of the City of Pasadena Department of Transportation is therefore interested in understanding what may be causing transit ridership to decline in its jurisdiction, what are the circumstances driving these changes, and what these causes and circumstances suggest for ways to make the system more attractive to riders.

In the last ten years transit use in Southern California has fallen significantly. This report investigates that falling transit use. We define Southern California as the six counties that participate in the Southern California Association of Governments (SCAG) – Los Angeles, Orange, Riverside, San Bernardino, Ventura and Imperial. We examine patterns of transit service and patronage over time and across the region, and consider an array of explanations for falling transit use: declining transit service levels, eroding transit service quality, rising fares, falling fuel prices, the growth of Lyft and Uber, the migration of frequent transit users to outlying neighborhoods with less transit service, and rising vehicle ownership. While all of these factors probably play some role, we conclude that the most significant factor is increased motor vehicle access, particularly among low-income households that have traditionally supplied the region with its most frequent and reliable transit users.

In this study, the authors use existing modeling tools and data from the San Francisco Bay Area (California) to understand the potential market demand for a “first” mile transit access service and possible reductions in vehicle miles traveled (VMT) (and thus GHGs) at both the regional and station level. The authors develop a project scenario that targets commuters who drive alone but could take rail (Bay Area Rapid Transit, or BART) to work. The authors use travel time, cost, and distance data from San Francisco Bay Area travel models, Google and BART APIs, and TNC fares to explore the magnitude of change in overall travel time and cost for travelers who switch from driving alone to using TNC and BART to travel to work. The analysis indicates that 31% of the identified drive-alone trips could reduce generalized costs (travel time and monetary costs) by switching to TNC and BART. If all travelers who could benefit from traveling by TNC and BART, did in fact switch from drive-alone travel, about 40 thousand new BART trips could be generated and over a half a million miles of VMT avoided during the morning commute period. Most of these trips experienced relatively high levels of cost and VMT savings, which may be more likely to motivate behavioral change. Examination of cost savings by income level and vehicle availability suggests that the new service is more likely to benefit lower income households with fewer vehicles. Sensitivity analyses indicate that even with increased TNC fares and waiting times, there is still a relatively large number of trips that could benefit from switching from driving to TNC and BART. Tests of a shared TNC BART access service suggest cost savings and VMT reductions even when travel time costs increase by 60%. Analysis of benefits by station indicate locations with relatively high market potential (i.e., trips and cost saving) and environmental benefits (VMT reduction) for early pilot implementation. These results can be used to estimate potential TNC fare subsidies to increase performance by station to achieve project objectives, such as equitable access, increased BART ridership, and reduced VMT.View the NCST Project Webpage

In 2015, Los Angeles increased the minimum wage to one of the highest in the country, leading the way to boost stagnating pay for workers and to ensure that workers are able to earn enough to support themselves and their families. Los Angeles needs to take the same care to ensure quality employment within the taxi industry, particularly in light of the rapid growth of transportation networking companies (TNCs) such as Uber and Lyft into Los Angeles.TNCs are on-demand ride services where unlicensed individuals use their personal vehicles as for-hire vehicles. These companies have constructed an app-based ridesharing model that is having a major impact on the taxi industry in regards to income for taxi drivers, revenue for cab companies, and the local economy. TNCs are receiving praise for their innovation and their rapid growth is changing the way we do work. Yet these changes inevitably signal a business model shift that has a widespread and dramatic impact on workers in Los Angeles and nationwide. It is important to further evaluate and research how this business model will impact Los Angeles workers and the future of quality jobs in the city.

This report proposes a generalized process for evaluating the ridership impact and traffic impact of a bus rapid transit system. The process proposed aims at providing such evaluation at the sketch planning stage and has a specific focus on two aspects -- the ridership forecast based on the implementation of various ITS technologies and the degradation of level of service that results from converting a traffic lane into exclusive bus lane for the implementation of a bus rapid transit system. The proposed process is tested on the Van Ness Blvd site in San Francisco, California. It is also implemented as a web-based toolbox that is easily accessible.

The cost of building rail transit facilities in the United States has skyrocketed in recent decades. Sections of Los Angeles’s Red Line subway cost more than $750 million per mile to build and even less pricey light-rail systems can cost more than $200 million per mile. Soaring capital investment costs are today’s biggest deterrent, both political and financial, to constructing new transit infrastructure. It stands to reason that high-cost transit projects need high ridership levels. Without sufficient numbers of riders and the fares they generate, new rail investments will inevitably incur huge deficits. Nor will environmental benefits accrue. Transit only reduces traffic congestion and tailpipe emissions when it draws former motorists – and particularly single-occupant drivers – to trains and buses. A system with few riders and a high price tag is a poor investment compared to a system with many riders and a low price tag.Through the investigation of more than 50 transit investment projects built in the U.S. since 1970, we find a strong correspondence between costs and ridership. As one would expect, capital costs and ridership are positively correlated. Moreover, both ridership and capital costs typically rise with job and population densities. By clustering trip ends near stops, concentrated development tends to average far more transit trips per square mile than less concentrated development. But density often increases construction costs as well – via increased costs for right-of-way acquisitions and building demolitions, more complicated route alignments, utility relocation expenses, and higher labor costs.This symbiotic relationship between density and both ridership and costs begs the question: are there densities that offer the most “bang for the buck” in terms of the number of riders for the investment costs? If so, what minimum densities should municipalities zone for around existing or planned stations in different settings or for different types of investments? These are among the most frequently asked questions in the urban planning field today – questions for which there are surprisingly few good answers or widely accepted benchmarks. This paper aims to help fill this knowledge gap.

This report presents the results of a performance assessment of the applicability of bus rapid transit on conventional highways in the setting of a site-specific case study along the Lincoln Boulevard corrider in Santa Monica, California. When bus rapid transit systems are implemented on conventional highways, especially on arterials, there are numerous bus priority treatments that can be applied and each has associated with it issues that need to be investigated. In this study, we are investigating concurrent flow curb bus lanes based on the removal of peak period parking along the Lincoln Boulevard corridor. We have focused on traffic and ridership impacts associated with this type of bus rapid transit system implementation.

One of the more disappointing transportation trends of the 1980s was mass transit's declining market share of metropolitan trips throughout the United States. Despite the infusion of tens of billions of dollars in public assistance for constructing new facilities and supporting bus and rail operations, transit's nationwide share of total commute trips fell from 6.4 percent in 1980 to 5.3 percent in 1990. In California, while transit journeys rose in absolute numbers during the 1980s (one of the few states where this was the case), transit's share of commute trips fell in the state's four largest metropolitan areas, despite their new rail systems: greater Los Angeles - 5.4 to 4.8 percent; San Francisco Bay Area - 11.9 to 10 percent; San Diego - 3.7 to 3.6 percent; and Sacrament - 3.7 to 2.5 percent. Nor do these trends appear to be slowing. Recent studies show Southern California's drive-alone rate increasing from 77 percent in 1992 to 79 percent in 1993. Given that California has invested over $10 billion state-wide in urban rail transit infrastructure and is poised to spend upwards of $160 billion more over the next thirty years, these trends are worrisome.

This is the age of the train: certainly in Japan and in Europe; probably, soon, on the East Coast. The urgent question is whether California will catch the train, whether indeed it should catch the train, and if so how. Modern high-speed train travel involves trains that achieve sustained high speed - a minimum of 125 mph, a maximum in revenue service so far of 187 mph - between cities that are typically between 100 and 500 miles apart. It all began exactly thirty years ago, when the Japanese opened their Tokaido Shinkansen between Tokyo and Osaka. It took nearly another two decades before France followed suit with its TGV (Train a Grande Vitesse) over the 270 miles between Paris and Lyon in 1981. But since then, high-speed trains have proliferated.