Rail Ridership Research Articles

Calculation of Benefits of Advanced Integrated Rail Service

This paper calculates the benefits of the Advanced Integrated Rail Service in Lima, Peru. The service consists of the application of five new intelligent transportation systems to rail transit managed from an advanced marketing and information center. These systems provide ( a) advanced passenger information, ( b) optimized productivity of roads that access rail stations and their capacities, ( c) improved rail transfers, ( d) advanced taxi–rail service, and ( e) advanced parking management. The new service can produce benefits by improving the quality of rail travel, increasing the productivity of taxis, and optimizing the productivity of roads that access rail stations and their capacities. The benefits include the reduction of three cost items: ( a) transport user costs (time, monetary, and vehicle operating costs), ( b) the cost required to operate the system (fuel subsidies to stabilize the effect of the variability in oil prices), and ( c) the cost of externalities (health impacts, the cost of accidents, social costs, and the cost from carbon emissions). The benefits are estimated by the use of transportation planning software with data from the urban transportation master plan for the period from 2005 to 2025. As a result, the new rail service can produce considerable benefits. These are derived from the transfer of passengers from automobiles and taxis to rail for a segment of their trip, but it can also produce negative but smaller effects derived from the shift of traffic from faster highways to the slower roads that access rail stations. The advanced integrated rail service can foster rail ridership, increase average speeds in the city, and reduce fuel subsidies.

An analysis of air mass effects on rail ridership in three US cities

This paper examines whether daily weather affects ridership in urban transportation systems. When examining human–weather relationships, it is often advantageous to examine air masses, which take into account the entire parcel of air over a region. Spatial synoptic classification characterizes air masses based upon numerous meteorological variables at a given location. Thus, rather than examining temperature or precipitation individually, here we compare daily ridership to synoptic air mass classifications for three urban rail systems: Chicago Transit Authority (CTA), Bay Area Rapid Transit (BART), and the Hudson–Bergen light-rail line in northern New Jersey. Air masses are found to have a significant impact on daily rail ridership, with usage typically increasing on dry, comfortable days and decreasing on moist, cool ones, particularly on weekends. Although the comfort of a particular air mass changes throughout the year, seasonality is not a significant factor with respect to the air mass–ridership relationship. The results of this study can benefit rail system managers who must predict daily ridership or in the development of cost-benefit analyses for station improvements.

Impact of Weather on Transit Ridership in Chicago, Illinois

This paper explores the weather–ridership relationship and its potential applications in transit operations and planning. Using the Chicago Transit Authority (CTA) in Illinois as a case study, the paper investigates the impact of five weather elements (temperature, rain, snow, wind, and fog) on daily bus and rail ridership and variation across modes, day types, and seasons. The resulting relationships are applied to the CTA ridership trend analysis, showing how preliminary findings may change after controlling for weather. The paper emphasizes the importance of having a theoretical framework encompassing weather and travel.

Bus-and-Rail and All-Bus Transit Systems: Experience in Dallas and Houston, Texas, 1985 to 2003

Dallas Area Rapid Transit (DART) in Texas has experienced widely acknowledged success with its light rail system, New Start. Ridership results have met or exceeded expectations. The DART system has experienced significant ridership changes as it transitioned from an all-bus system to a bus-and-rail system. These changes are reflected not only in the growth of light rail ridership but also in changes in bus system ridership. Until the 2004 opening of its light rail line, the Metropolitan Transit Authority of Harris County (Metro) had been a highly successful all-bus transit system. Metro's success with high-occupancy-vehicle lanes supporting an extensive network of express and park-and-ride routes is renowned. Metro's local bus network serves high volumes of passengers daily. These two Texas transit systems have some interesting similarities. Until 1996, both were all-bus systems. Their ridership trends shared general patterns. There are also divergences in ridership patterns occasioned by the differing natures of the two systems. Their service areas are significantly different. Their service area populations, however, are of similar size. Ridership statistics for the two systems as reported in the National Transit Database for 1985 through 2003 are examined. The statistics examined include unlinked passenger trips, passenger miles, and average trip lengths. The changes in these statistics and their comparisons after the inauguration and subsequent expansion of the DART light rail system reflect the changing nature of the DART bus system as its role in the regional transit network refocuses.

Bus-and-Rail and All-Bus Transit Systems

Dallas Area Rapid Transit (DART) in Texas has experienced widely acknowledged success with its light rail system, New Start. Ridership results have met or exceeded expectations. The DART system has experienced significant ridership changes as it transitioned from an all-bus system to a bus-and-rail system. These changes are reflected not only in the growth of light rail ridership but also in changes in bus system ridership. Until the 2004 opening of its light rail line, the Metropolitan Transit Authority of Harris County (Metro) had been a highly successful all-bus transit system. Metro's success with high-occupancy-vehicle lanes supporting an extensive network of express and park-and-ride routes is renowned. Metro's local bus network serves high volumes of passengers daily. These two Texas transit systems have some interesting similarities. Until 1996, both were all-bus systems. Their ridership trends shared general patterns. There are also divergences in ridership patterns occasioned by the differing natures of the two systems. Their service areas are significantly different. Their service area populations, however, are of similar size. Ridership statistics for the two systems as reported in the National Transit Database for 1985 through 2003 are examined. The statistics examined include unlinked passenger trips, passenger miles, and average trip lengths. The changes in these statistics and their comparisons after the inauguration and subsequent expansion of the DART light rail system reflect the changing nature of the DART bus system as its role in the regional transit network refocuses.

Sketch Models to Forecast Commuter and Light Rail Ridership: Update toTCRP Report 16

Ridership potential is among the most valuable attributes to understand about a proposed light or commuter rail line during early stages of project development, yet few nationally relevant sketch-level tools exist for feasibility analyses. Research was done to develop a nationally applicable, sketch-level ridership forecasting tool for light rail and commuter rail. The study collected current ridership, demographic, and transportation system data from 17 U.S. regions, including 58 commuter rail corridors, 22 light rail corridors, and 1,218 stations, and it tested 163 possible explanatory variables. The effort yielded two multivariable regression equations that show close relationships between actual and predicted values, with adjusted R-squared values of 0.97 for commuter rail and 0.92 for light rail. The models also validate well to existing rail systems in six regions and successfully predict actual line ridership with adjusted R-squared values of 0.84 for commuter rail and 0.47 for light rail. The new models improve on previous tools by introducing sensitivity to reverse-commute trips and special transportation hubs or ports and by introducing transportation-system variables to measure the impacts of faster operating speeds, lower fares, or shorter midday headways. The models require readily available data and use simple form, making the tool technically accessible to a large number of transportation planners. An apples-to-apples comparison with similar sketch models developed nearly a decade ago in TCRP Report 16 shows that the new tools perform consistently better, explaining between 31% and 213% more variation in observed rail boardings among the same validation data sets.

Sketch Models to Forecast Commuter and Light Rail Ridership

Ridership potential is among the most valuable attributes to understand about a proposed light or commuter rail line during early stages of project development, yet few nationally relevant sketch-level tools exist for feasibility analyses. Research was done to develop a nationally applicable, sketch-level ridership forecasting tool for light rail and commuter rail. The study collected current ridership, demographic, and transportation system data from 17 U.S. regions, including 58 commuter rail corridors, 22 light rail corridors, and 1,218 stations, and it tested 163 possible explanatory variables. The effort yielded two multivariable regression equations that show close relationships between actual and predicted values, with adjusted R-squared values of 0.97 for commuter rail and 0.92 for light rail. The models also validate well to existing rail systems in six regions and successfully predict actual line ridership with adjusted R-squared values of 0.84 for commuter rail and 0.47 for light rail. The new models improve on previous tools by introducing sensitivity to reverse-commute trips and special transportation hubs or ports and by introducing transportation-system variables to measure the impacts of faster operating speeds, lower fares, or shorter midday headways. The models require readily available data and use simple form, making the tool technically accessible to a large number of transportation planners. An apples-to-apples comparison with similar sketch models developed nearly a decade ago in TCRP Report 16 shows that the new tools perform consistently better, explaining between 31% and 213% more variation in observed rail boardings among the same validation data sets.

Rail’s Share of Airport Access: Examining the Data

Currently, many U.S. cities are exploring the possibility of constructing rail connections to their airports. The perceptions of rail’s role in serving ground-access needs may not be in accordance with what is actually experienced at existing airport rail systems. Data from these existing systems could help planners forecast ridership at proposed new systems. The data from existing systems is not always reported in a consistent manner that facilitates the useful comparison of airport rail systems. This lack of information is addressed. Comparable data on current ridership at eight airport rail stations are provided, and a standard method for collecting these data is presented, along with common sources of error that can lead to misrepresentations of airport station rail ridership. In addition to this discussion of the data collection process and its pitfalls, cross-checking techniques and suggestions for improved future data generation are detailed. Current ridership trends are also included. The findings show that most airport rail connections serve less than 6 percent of air passenger needs and between 8 and 14 percent of airport employee ground-access needs. Furthermore, the examination of the published data demonstrates the many ways in which error can affect the calculations and subsequent presentation of these statistics.

Energy supply and demand, and their relationship to the transportation sector

In early January, 1997, at the 76th annual meeting of the Transportation Research Board (TRB) of the National Research Council, a special session was conducted that struck a familiar chord for many of us in attendance. It was not the title so much that attracted our attention--in fact, in a way, the title seemed somewhat ethereal--Energy Supply and Demand: Policy Issues for the Next Millennium. It was, instead, the underlying and recurring themes of nonrenewable energy resources--themes with which so many of us are familiar by virtue of lifelong work and interests-that drew us into that very large hall in the Washington Hilton and compelled us to listen. Here, in a rather inconspicuous setting, at a conference generally more noted for its emphasis on topics like steamship freight tonnage, light rail ridership, and the structural mechanics of concrete pavement, were people talking about fossil fuel energy, the inevitability of our declining resources, and the myriad of impacts that such a decline will have on society--particularly one that relies so heavily on personal transportation. Suffice it to say it was a very good session. Organized and chaired by Mike Lawrence of Jack Faucett Associates, it successfully spanned the spectrum of issues--from estimation of remaining oil and gas resources, on one end, to transportation policy implications, on the other. The presentations focused not only on the United States, but encompassed a global perspective as well. The session was cosponsored by two TRB committees--the Committee on Transportation Energy and the Committee on Alternative Fuels. Because of the situation and environment in which these presentations were made, we felt compelled to include them in the record of this journal as a means of providing our readers access to the thoughts and ideas of individuals not normally represented in Nonrenewable Resources. We think you will find the diversity of ideas to be fascinating, and perhaps even troubling, as you witness participants from various organizations, backgrounds, and disciplines discuss the penetrating and intertwining impacts of United States and world energy resources on such an important economic sector as transportation. The session actually consisted of two parts. In this issue we bring you the four papers from the first half of the session, which was more directly resourceoriented. We also include one paper from the second half of the session, which was aimed more at transportation economics and policy, and which, of course, are inevitably impacted by resource issues. In a Forum article, Mike Lawrence summarizes all five papers from his own vantage point. Also included is a slightly abridged transcript of the audio tape of the questionand-answer session that followed the first half of the session. To round out this special issue of Nonrenewable Resources, we return to our roots a bit and include an article by Schmoker and Crovelli that extends their work on estimating the future growth of oil and gas reserves. We hope you enjoy reading the entire issue, and that you come away with a deeper appreciation of the perspectives of others as they consider the breadth of issues pertaining to our precious nonrenewable energy resources.

Fares, Service Levels, and Demographics: What Determines Commuter Rail Ridership in the Long Run?

Using panel data covering a 13-year period, this paper examines the roles of transportation policy and demographic changes in determining rail ridership. The paper presents a model which allows identification of long-run responses of ridership to changes in prices and service independently of exogenous shocks to ridership caused by demographic factors. Long-run price and service elasticities are estimated to be almost twice as large as short-run elasticities. After controlling for transportation prices and service levels, demographic variables contribute little to explaining residual differences in ridership. To the extent that demographic changes are driven by transportation variables, these effects are captured in the estimated long-run elasticities.

A heteroscedastic extreme value model of intercity travel mode choice

Estimation of disaggregate mode choice models to estimate the ridership share on a proposed new (or improved) intercity travel service and to identify the modes from which existing intercity travelers will be diverted to the new or upgraded service constitutes a critical part of evaluating alternative travel service proposals to alleviate intercity travel congestion. This paper develops a new heteroscedastic extreme value model of intercity mode choice that overcomes the ‘independence of irrelevant alternatives’ (IIA) property of the commonly used multinomial logit model. The proposed model allows a more flexible cross-elasticity structure among alternatives than the nested logit model. It is also simple, intuitive and much less of a computational burden than the multinomial probit model. The paper discusses the non-IIA property of the heteroscedastic extreme value model and presents an efficient and accurate Gaussian quadrature technique to estimate the heteroscedastic model using the maximum likelihood method. The multinomial logit, alternative nested logit structures, and the heteroscedastic model are estimated to examine the impact of improved rail service on business travel in the Toronto-Montreal corridor. The nested logit structures are either inconsistent with utility maximization principles or are not significantly better than the multinomial logit model. The heteroscedastic extreme value model, however, is found to be superior to the multinomial logit model. The heteroscedastic model predicts smaller increases in rail shares and smaller decreases in non-rail shares than the multinomial logit in response to rail-service improvements. It also suggests a larger percentage decrease in air share and a smaller percentage decrease in auto share than the multinomial logit. Thus, the multinomial logit model is likely to provide overly optimistic projections of rail ridership and revenue, and of alleviation in inter-city travel congestion in general, and highway traffic congestion in particular. These findings point to the limitations of the multinomial logit and nested logit models in studying intercity mode choice behavior and to the usefulness of the heteroscedastic model proposed in this paper.

Metro Rail Ridership

Metro Rail Ridership