Increasing Transit Ridership: Lessons from the Most Successful Transit Systems in the 1990s, MTI Report-01-22

This study systematically examines recent trends in public transit ridership in the U.S. during the 1990s. Specifically, this analysis focuses on agencies that increased ridership during the latter half of the decade. While transit ridership increased steadily by 13 percent nationwide between 1995 and 1999, not all systems experienced ridership growth equally. While some agencies increased ridership dramatically, some did so only minimally, and still others lost riders. What sets these agencies apart from each other? What explains the uneven growth in ridership

Deciphering Public Transit Ridership in Baton Rouge: Spatial Disaggregation Approaches

Background: Surveys across the U.S. reveal that commuters driving personal vehicles spend a significant amount of time in traffic, while public transit, as an efficient commuting mode, has been largely underutilized. Purpose: What causes a low public transit ridership? How could public transit ridership be explained by demographic, socio-economic and spatial characteristics of neighborhood? This study answers these questions by deciphering the relationships between public transit ridership and various factors in a medium-size city in southern U.S. – Baton Rouge, Louisiana. Methods: Non-spatial and spatial data in a larger areal unit (e.g., block group) are used to infer demographic, socio-economic and spatial variables in a smaller areal unit (e.g., census block) to gain a sharper spatial resolution in the analysis of public transit ridership in geographic information systems (GIS). First, the ecological inference method is used to disaggregate demographic and socio-economic data from the block group level to the census block level. Secondly, Monte Carlo simulation and transit schedule data are used to improve the estimation of travel time by private vehicle and public transit, respectively, based on which commuting time ratio of these two is calibrated at the census block level. Regression analyses including ordinary least square (OLS) regression, geographically-weighted regression (GWR) and semi-parametric GWR (SGWR) are used to explain the variability of public transit ridership using demographic, socio-economic, and spatial variables at the census block level. Results: A stepwise regression process selects six variables from 25 original variables representing different aspects of demographic, socio-economic, and spatial characteristics at the census block level. The final model includes both global and localized effects on public transit ridership. Recent immigrants and carless population are positively related to public transit ridership. White population concentration is negatively related to public transit ridership. These relationships are found to be consistent across the study area. The relationships between public transit ridership and income, commuting time ratio, and accessibility to employment via public transit vary across the study area, and some of these variables even show opposite effects in specific pockets in contrast to their area-wide average effects

Net Effects of Gasoline Price Changes on Transit Ridership in U.S. Urban Areas, MTI Report 12-19

Using panel data of transit ridership and gasoline prices for ten selected U.S. urbanized areas over the time period of 2002 to 2011, this study analyzes the effect of gasoline prices on ridership of the four main transit modes—bus, light rail, heavy rail, and commuter rail—as well as their aggregate ridership. Improving upon past studies on the subject, this study accounts for endogeneity between the supply of services and ridership, and controls for a comprehensive list of factors that may potentially influence transit ridership. This study also examines short- and long-term effects and non-constant effects at different gasoline prices. The analysis found varying effects, depending on transit modes and other conditions. Strong evidence was found for positive short-term effects only for bus and the aggregate: a 0.61-0.62 percent ridership increase in response to a 10 percent increase in current gasoline prices (elasticity of 0.061 to 0.062). The long-term effects of gasoline prices, on the other hand, was significant for all modes and indicated a total ridership increase ranging from 0.84 percent for bus to 1.16 for light rail, with commuter rail, heavy rail, and the aggregate transit in response to a 10 percent increase in gasoline prices. The effects at the higher gasoline price level of over $3 per gallon were found to be more substantial, with a ridership increase of 1.67 percent for bus, 2.05 percent for commuter rail, and 1.80 percent for the aggregate for the same level of gasoline price changes. Light rail shows even a higher rate of increase of 9.34 percent for gasoline prices over$4. In addition, a positive threshold boost effect at the $3 mark of gasoline prices was found for commuter and heavy rails, resulting in a substantially higher rate of ridership increase. The results of this study suggest that transit agencies should prepare for a potential increase in ridership during peak periods that can be generated by substantial gasoline price increases over$3 per gallon for bus and commuter rail modes, and over $4 per gallon for light rail, in order to accommodate higher transit travel needs of the public through pricing strategies, general financing, capacity management, and operations planning of transit services

Improving Demand Modeling in California\u27s Rail Transit System

This paper analyzes urban rail-fare elasticity and compares the results across four California transit systems. A method of Internet search is adopted to collect monthly transit-fare records from 2002 to 2013. This paper contributes towards improving demand modeling for public transit using more precise and monthly data and applies econometric techniques involving autoregressive integrated moving average (ARIMA) and panel data models. Results show that demand for public transit in California is very inelastic. Any ridership promotion policy may have a heterogeneous impact across transit systems

Class Act: An Assessment of Los Angeles Metro’s U-Pass Program

In 2016, the Los Angeles County Metropolitan Transportation Authority (Metro) introduced the Universal College Student Transit Pass (U-Pass), its reduced transit fare pass program for college and university students, with the expressed goal of increasing student transit ridership. An increase in college student transit ridership has great potential in Los Angeles County, where public transit ridership is declining, traffic congestion is worsening, and over one million students are enrolled in postsecondary education at public institutions. Researchers have found that reduced transit fare pass programs for university students are successful in increasing student transit use, generally with modest operational costs imposed on transit agencies. Is this true for U-Pass? A relatively young program, U-Pass raises questions for Metro staff about added costs and service demand on Metro buses and trains in exchange for increased ridership and student savings. Using ridership and survey data from the first two years of U-Pass, this research explores the relationships between U-Pass and student transit ridership, service demand and operating costs, and fare revenue

Understanding Transit Ridership Demand for a Multi-Destination, Multimodal Transit Network in an American Metropolitan Area, Research Report 11-06

This study examines the factors underlying transit demand in the multi-destination, integrated bus and rail transit network for Atlanta, Georgia. Atlanta provides an opportunity to explore the consequences of a multi-destination transit network for bus patrons (largely transit-dependent riders) and rail patrons (who disproportionately illustrate choice rider characteristics). Using data obtained from the 2000 Census, coupled with data obtained from local and regional organizations in the Atlanta metropolitan area, we estimate several statistical models that explain the pattern of transit commute trips across the Atlanta metropolitan area. The models show that bus riders and rail riders are different, with bus riders exhibiting more transit-dependent characteristics and rail riders more choice rider characteristics. However, both types of riders value many of the same attributes of transit service quality (including shorter access and egress times and more direct trips) and their use of transit is influenced by many of the same variables (including population and employment). At the same time, the factors that influence transit demand vary depending on the type of travel destination the rider wishes to reach, including whether it is the central business district (CBD) or a more auto-oriented, suburban destination. The results of the study offer new insights into the nature of transit demand in a multi-destination transit system and provide lessons for agencies seeking to increase ridership among different ridership groups. The results suggest that more direct transit connections to dispersed employment centers, and easier transfers to access such destinations, will lead to higher levels of transit use for both transit-dependent and choice riders. The results also show that the CBD remains an important transit destination for rail riders but not for their bus rider counterparts. Certain types of transit-oriented development (TOD) also serve as significant producers and attractors of rail transit trips

What’s Behind Recent Transit Ridership Trends in the Bay Area? Volume I: Overview and Analysis of Underlying Factors

Public transit ridership has been falling nationally and in California since 2014. The San Francisco Bay Area, with the state’s highest rates of transit use, had until recently resisted those trends, especially compared to Greater Los Angeles. However, in 2017 and 2018 the region lost over five percent (>27 million) of its annual riders, despite a booming economy and service increases. This report examines Bay Area transit ridership to understand the dimensions of changing transit use, its possible causes, and potential solutions. We find that: 1) the steepest ridership losses have come on buses, at off-peak times, on weekends, in non-commute directions, on outlying lines, and on operators that do not serve the region’s core employment clusters; 2) transit trips in the region are increasingly commute-focused, particularly into and out of downtown San Francisco; 3) transit commuters are increasingly non-traditional transit users, such as those with higher incomes and automobile access; 4) the growing job-housing imbalance in the Bay Area is related to rising housing costs and likely depressing transit ridership as more residents live less transit-friendly parts of the region; and 5) ridehail is substituting for some transit trips, particularly in the off-peak. Arresting falling transit use will likely require action both by transit operators (to address peak capacity constraints; improve off-peak service; ease fare payments; adopt fare structures that attract off-peak riders; and better integrate transit with new mobility options) and public policymakers in other realms (to better meter and manage private vehicle use and to increase the supply and affordability of housing near job centers)