Evaluating the impact of a newly added commuter rail system on bus ridership: a grouped ordered logit model approach (original) (raw)
Related papers
Examining determinants of rail ridership: a case study of the Orlando SunRail system
Transportation Planning and Technology, 2019
The current study contributes to literature on transit ridership by considering daily boarding and alighting data from a recently launched commuter rail system of Orlando-SunRail. The analysis is conducted based on daily boarding and alighting data for ten months for the year 2015. With the availability of repeated observations for every station the potential impact of common unobserved factors affecting ridership variables are considered. The current study develops an estimation framework, for boarding and alighting separately, that accounts for these unobserved effects at multiple levelsstation, station-week and station-day. In addition, the study examines the impact of various observed exogenous factors such as station level, transportation infrastructure, transit infrastructure, land use, built environment, sociodemographic and weather variables on ridership. The model system developed will allow us to predict ridership for existing stations in the future as well as potential ridership for future expansion sites.
How to Increase Rail Ridership in Maryland: Direct Ridership Models for Policy Guidance
Journal of Urban Planning and Development, 2016
The state of Maryland aims to double its transit ridership by the end of 2020. The Maryland Statewide Transportation Model (MSTM) has been used to analyze different policy options at a system-wide level. Direct ridership models (DRM) estimate ridership as a function of station environment and transit service features rather than using mode-choice results from large-scale traditional models. They have been particularly favored for estimating the benefits of smart growth policies such as Transit Oriented Development (TOD) on transit ridership and can be used as complementary to the traditional four-step models for analyzing smart growth scenarios at a local level and can provide valuable information that a system level analysis cannot provide. In this study, we developed DRMs of rail transit stations, namely light rail, commuter rail, Baltimore metro, and Washington D.C. metro for the state of Maryland. Data for 117 rail stations were gathered from a variety of sources and categorized by transit service characteristics, station built environment features and social-demographic variables. The results suggest that impacts of built environment show differences for light rail and commuter rail. For light rail stations, employment at half-mile buffer areas, service level, feeder bus connectivity, station distance to the CBD, distance to the nearest station, and terminal stations are significant factors affecting ridership. For commuter rail stations only feeder bus connection is found to be significant. The policy implications of the results are discussed.
How to Increase Rail Ridership in Maryland? Direct Ridership Models (DRM) for Policy Guidance
The state of Maryland aims to double its transit ridership by the end of 2020. The Maryland 5 Statewide Transportation Model (MSTM) has been used to analyze different policy options at 6 a system-wide level. Direct ridership models (DRM) estimate ridership as a function of 7 station environment and transit service features rather than using mode-choice results from 8 large-scale traditional models. They have been particularly favored for estimating the benefits 9 of smart growth policies such as Transit Oriented Development (TOD) on transit ridership 10 and can be used as complementary to the traditional four-step models for analyzing smart 11 growth scenarios at a local level and can provide valuable information that a system level 12 analysis cannot provide. In this study, we developed DRMs of rail transit stations, namely 13 light rail, commuter rail, Baltimore metro, and Washington D.C. metro for the state of 14 Maryland. Data for 117 rail stations were gathered from a variety of sources and categorized 15 by transit service characteristics, station built environment features and social-demographic 16 variables. The results suggest that impacts of built environment show differences for light rail 17 and commuter rail. For light rail stations, employment at half-mile buffer areas, service level, 18 feeder bus connectivity, station distance to the CBD, distance to the nearest station, and 19 terminal stations are significant factors affecting ridership. For commuter rail stations only 20 feeder bus connection is found to be significant. The policy implications of the results are 21 discussed.
Factors influencing light-rail station boardings in the United States
Transportation Research Part A: Policy and Practice, 2004
Many US cities have recently built or approved light-rail systems to combat congestion, sprawl, and pollution. Critics questions light rail's ability to generate ridership in low-density, automobile-oriented, polycentric US cities with smaller downtowns. Proponents counter that sufficient numbers of homes and workplaces have convenient access to stations via walking, park-and-ride, or bus to develop feasible corridors connecting major residential areas with suburban concentrations of employment and the CBD. With this in mind, we used multiple regression to determine factors that contribute to higher light-rail ridership. Cross-sectional data on average weekday boardings were collected for the year 2000 for 268 stations in nine US cities representing a variety of urban settings. The results showed the importance of land use and accessibility. Employment, population, and percent renters within walking distance, as well as bus lines, park-and-ride spaces, and centrality, were significant. Dummy variables for terminal and transfer stations and international borders were all positive and significant. Total degree-days were negative and significant, lowering expectations for cities with extreme climates. Notably, the stations in the CBD generate much higher boardings, but these are explainable by the same variables present in lesser combinations at non-CBD stations and account for their generally lesser boardings. Importantly, a dummy variable for CBD location was not significant. The resulting model may be useful as a first-cut, one-step approach for predicting demand for possible light-rail alignments.
This study seeks to understand the relative efficacy of two classes of strategies intended to increase transit ridership. One class of strategies is land use planning-based and seeks to relocate people and their activity around transit in higher density building clusters called Transit Oriented Development (TOD). This class of strategies is intended to bring a greater proportion of regional population to transit, thereby encouraging the relocated population to use transit more than they did in the past. The other class of strategies is transit planning-based and seeks to restructure transit networks around dispersed locations where people live and do business. This class of strategies brings transit to people, with the objective of inducing those people to use transit more. We evaluate these strategies by specifying and estimating a model of transit demand between pairs of places where we know the magnitude of transit usage. Estimation of model parameters yields insight into the importance of variables that might give rise to the observed transit ridership. Explanatory variables include indicators of whether origin or destination zones are TODs as well as the time required to travel between the zones by transit, and control variables. The purpose of the analysis is to determine the relative importance of the TOD and transit travel time variables in inducing transit patronage.
Urban Studies, 2014
MTI works to provide policy-oriented research for all levels of government and the private sector to foster the development of optimum surface transportation systems. Research areas include: transportation security; planning and policy development; interrelationships among transportation, land use, and the environment; transportation finance; and collaborative labormanagement relations. Certified Research Associates conduct the research. Certification requires an advanced degree, generally a Ph.D., a record of academic publications, and professional references. Research projects culminate in a peer-reviewed publication, available both in hardcopy and on TransWeb, the MTI website (http://transweb.sjsu.edu).
Identifying the Factors that Influence Urban Public Transit Demand
ArXiv, 2021
The rise in urbanization throughout the United States (US) in recent years has required urban planners and transportation engineers to have greater consideration for the transportation services available to residents of a metropolitan region. This compels transportation authorities to provide better and more reliable modes of public transit through improved technologies and increased service quality. These improvements can be achieved by identifying and understanding the factors that influence urban public transit demand. Common factors that can influence urban public transit demand can be internal and/or external factors. Internal factors include policy measures such as transit fares, service headways, and travel times. External factors can include geographic, socioeconomic, and highway facility characteristics. There is inherent simultaneity between transit supply and demand, thus a two-stage least squares (2SLS) regression modeling procedure should be conducted to forecast urban ...
Evaluating new start transit program performance: Comparing rail and bus
2006
There is ongoing debate over the relative advantages of rail and bus transit investments. Rail critics assert that cities which expand their bus transit systems exhibit better performance than those that expand rail systems. This study examines those claims. It compares public transport performance in U.S. urban areas that expanded rail transit with urban areas that expanded bus transit from the mid-1990s through 2003, using Federal Transit Administration data. This analysis indicates that cities that expanded their rail systems significantly outperformed cities that only expanded bus systems in terms of transit ridership, passenger-mileage, and operating cost efficiency. This indicates that rail transit investments are often economically justified due to benefits from improved transit performance and increased transit ridership.
Examining the Bus Ridership Demand: Application of Spatio-Temporal Panel Models
Journal of Advanced Transportation, 2021
An important tool to evaluate the influence of these public transit investments on transit ridership is the application of statistical models. Drawing on stop-level boarding and alighting data for the Greater Orlando region, the current study estimates spatial panel models that accommodate for the impact of spatial and temporal observed and unobserved factors on transit ridership. Specifically, two spatial models, Spatial Error Model and Spatial Lag Model, are estimated for boarding and alighting separately by employing several exogenous variables including stop-level attributes, transportation and transit infrastructure variables, built environment and land use attributes, and sociodemographic and socioeconomic variables in the vicinity of the stop along with spatial and spatiotemporal lagged variables. The model estimation results are further augmented by a validation exercise. These models are expected to provide feedback to agencies on the benefits of public transit investments ...
2011
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 (largely choice riders). Using data obtained from the 2000 Census, coupled with data obtained from local and regional organizations in the Atlanta metropolitan area, several statistical models that explain the pattern of transit commute trips across the Atlanta metropolitan area are estimated. 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. More direct transit connections to dispersed employment centers, and easier transfers to access such destinations, should lead to higher levels of transit use for both transit-...