Potential Ridership Research Articles

Measuring students’ satisfaction levels for transit services: An application of latent class analysis

Past studies have identified the general public’s level of satisfaction with the service attributes of conventional fixed-route transit and ridesharing services, but few have limited their focus to students. This study employs latent class cluster analysis (LCCA) to identify clusters of university students, based on their satisfaction levels of the attributes of conventional fixed-route and ridesharing services, and uses a latent class behavioral model of a sample of university students in Arlington, Texas to explore the heterogeneity of their preferences toward ridesharing services. The results indicate that younger- and lower-income populations are more likely to be satisfied with on-demand ridesharing services than older- and higher-income populations, females are more likely to be satisfied with ridesharing services than males, and domestic students are more likely to be satisfied with ridesharing services than international students. The outcomes of the study will provide transportation planners with new insights about the significance of sociodemographic factors on the satisfaction level of those who use conventional transit and on-demand ridesharing services and will help them incorporate strategies that will make their services more attractive to their potential ridership.

Impacts of the COVID-19 Pandemic on Bike-Sharing: A Literature Review

Bike-sharing is recognized as a fast and efficient transport solution in cities, with zero emissions, convenience, speed of movement for short distances and beneficial effects on users’ health. In recent years, especially during the COVID-19 pandemic, its use has been rather heterogeneous across countries and significant differences have emerged from empirical studies. However, there has been no systematic review of the empirical studies focused on the impacts that the COVID-19 pandemic has had on the use of bike-sharing. This article aims to review all of the papers that have been published on this topic since the outbreak of the pandemic. In this analysis, we consider several elements, such as the impacts of the pandemic on actual and potential ridership, on the demand for bike-sharing compared to other public transport means, and on commuting and non-commuting trips. We also focus on the factors that have had an impact on the change in bike-sharing usage and the related policy and operational implications that have emerged from the literature. Finally, we highlight the gaps in the literature that require further investigation.

Multicriteria Feasibility Evaluation for Rural Transit in Georgia

This article describes transit feasibility analysis features of the Georgia Department of Transportation’s (GDOT’s) Multimodal Transportation Planning Tool (MTPT). Using open databases that are available agencywide, the MTPT can provide a system-level analysis of transportation requirements of rural areas, identify potential implementation constraints early in the planning process, and develop a prioritized project list by mode for an analysis region. In addition to demand-responsive transit, the MTPT addresses intercity bus, intercity passenger rail, commuter rail, highways, aviation, and bicycle modes. This article focuses on the demand-responsive transit component of the MTPT, and provides an overview of how the MTPT can help automate system-level transit planning for the general public in rural Georgia.The MTPT transit analysis assesses service feasibility for nonurbanized areas that do not currently have local transit service for the general public. In the MTPT, transit service feasibility considers the existence of human service transit providers in the county; the percentage of population in certain “target” populations; and estimates of potential ridership, vehicle requirements, capital costs, operating costs, and economic benefits. The first factor indicates if opportunities may exist to coordinate service with existing providers, and helps to address funding issues. The second factor assesses market characteristics of the target area as one determinant of potential service need. The third factor provides a range of potential values for key operating and financial statistics at a system-planning level of analysis. The intent of the analysis is to identify those locations where it makes sense to more seriously analyze new demand-responsive services.This article should be helpful for transportation planners with responsibility for developing system-level transportation plans and programs at the county, region, and state levels. It will also be helpful for decision-makers who are trying to match transit funding to areas with high service needs.

Investigating the Ridership Impact of New Light-Rail Transit and Arterial Bus Rapid Transit Lines in the Twin Cities

Cities in the United States and across the world are investing in high-capacity modes with enhanced reliability, including light-rail transit (LRT) and arterial bus rapid transit (BRT), to regain ridership. However, the impact of replacing high-frequency bus service with these modes is not well understood. This paper investigates the ridership effect of implementing LRT and arterial BRT on corridors that were already well served by local bus routes. Using data from Metro Transit in Minneapolis/Saint Paul between 2012 and 2017, overall ridership and frequency on the corridors are evaluated to distinguish between trips that were newly generated and trips that were drawn from the local bus routes running in the same corridor. Fixed-effects models are fitted to estimate how much of the new ridership can be attributed to the high-capacity modes and to the reliability improvements they provide while controlling for covariates. Results show that the Green Line LRT generated 86% more ridership and the arterial BRT A Line generated 12% more ridership than if the transit agency had relied on local bus service. These results demonstrate the potential ridership impacts of replacing and supplementing existing bus service with reliable high-capacity modes.

FACTORS INFLUENCING RIDERSHIP PREFERENCE IN USING LRT SERVICE IN KLANG VALLEY, MALAYSIA

Light Rail Transit (LRT) is one of the public transports that provides a lot of benefits to the Malaysian. Yet this consumption depends on the diverse tastes of potential ridership which are influenced by various factors. However, it is very challenging to predict significant factors influencing ridership preferences. As such, the identification of these factors is very important in ensuring this transportation service really attract ridership attention. Thus, this paper intends to identify the main factors that influence ridership preference in taking LRT transportation. 28 attributes have been identified in this research which expands from four (4) main components. Data were collected from ridership’s survey, site observations and ridership statistical data. Pearson Chi-square has been employed to justify the significant status and the influence level of each LRT attribute and component factors toward ridership preference. The results show that 23 attributes recorded a significant status (<0.00) in two (2) different directions of correlation. Overall, three (3) component factors namely i) Comfortable Service, ii) Economics and iii) Indoor Environment Conditions, have influenced and contributed to the same effect on ridership considerations, as compared to the negative effects displayed by the Site Design Attributes.

Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method

The outbreak of COVID-19 in 2020 has had drastic impacts on urban economies and activities, with transit systems around the world witnessing an unprecedented decline in ridership. This paper attempts to estimate the effect of COVID-19 on the daily ridership of urban rail transit (URT) using the Synthetic Control Method (SCM). Six variables are selected as the predictors, among which four variables unaffected by the pandemic are employed. A total of 22 cities from Asia, Europe, and the US with varying timelines of the pandemic outbreak are selected in this study. The effect of COVID-19 on the URT ridership in 11 cities in Asia is investigated using the difference between their observed ridership reduction and the potential ridership generated by the other 11 cities. Additionally, the effect of the system closure in Wuhan on ridership recovery is analyzed. A series of placebo tests are rolled out to confirm the significance of these analyses. Two traditional methods (causal impact analysis and straightforward analysis) are employed to illustrate the usefulness of the SCM. Most Chinese cities experienced about a 90% reduction in ridership with some variation among different cities. Seoul and Singapore experienced a minor decrease compared to Chinese cities. The results suggest that URT ridership reductions are associated with the severity and duration of restrictions and lockdowns. Full system closure can have severe impacts on the speed of ridership recovery following resumption of service, as demonstrated in the case of Wuhan with about 22% slower recovery. The results of this study can provide support for policymakers to monitor the URT ridership during the recovery period and understand the likely effects of system closure if considered in future emergency events.

Mining urban sustainable performance: Spatio-temporal emission potential changes of urban transit buses in post-COVID-19 future

Emission benefits of transit buses depend on ridership. Declines in ridership caused by COVID-19 leads uncertainty about the emission reduction capacity of buses. This paper provides a method framework for analyzing spatio-temporal emission patterns of buses in combination with real-time ridership and potential emission changes in the post-COVID-19 future. Based on GPS trajectory and Smart Card data of 2056 buses from 278 routes covering 1.5 million ridership in Qingdao, China, spatio-temporal emissions characteristics of buses are studied. 7589 taxis with 0.2 million passengers’ trips are used for acquiring private cars’ emissions to evaluate the emissions difference between buses and cars. Empirical results show that the average difference between buses and cars with 2 persons can reach up to 117 g/km-person during 7:00–8:59 and 115 g/km-person during 17:00–18:59. However, buses have various emission benefits around the city at different periods. A double increase in emissions during non-rush hours can be observed compared with rush hours. 224 online survey data are used to study the potential ridership reduction trend in post-COVID-19. Results show that 56.3% of respondents would decrease the usage of buses in the post-COVID-19 future. Based on this figure, our analysis shows that per kilometer-person emissions of buses are higher than cars during non-rush hours, however, still lower than cars during rush hours. We conclude that when ridership reduces by more than 40%, buses cannot be “greener” travel modal than cars as before. Finally, several feasible policies are suggested for this potential challenge. Our study provides convincing evidence for understanding the emission patterns of buses, to support better buses investment decisions and promotion on eco-friendly public transport service in the post-COVID-19 future.

Examining determinants of rail ridership: a case study of the Orlando SunRail system

ABSTRACTThe current study contributes to the literature on transit ridership by considering daily boarding and alighting data from a recently launched commuter rail system in Orlando, Florida – SunRail. The analysis is conducted based on daily boarding and alighting data for 10 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 levels – station, 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.

Incorporating Product Choice into Transit Fare Policy Scenario Models

Customer fare product choices can affect both ridership and revenue, so they are strategically important for transit agencies. Nearly all major agencies offer choices between pay-per-use and pass products, and with each potential fare change, agencies face decisions about whether to modify pass “multiples”—the number of rides needed to “break even” on a pass purchase. However, the simple elasticity spreadsheet models often used to analyze the potential ridership and revenue impacts of fare changes make little or no adjustment for shifts in fare product choices. This paper reviews different options for incorporating product choice into fare policy scenario models, and it presents a ridership and revenue prediction procedure that combines a multinomial logit fare product choice model with the logic of an elasticity spreadsheet model. This combination facilitates evaluation of complex fare changes that are likely to alter fare product market shares while maintaining much of the flexibility and simplicity of a traditional spreadsheet model. Additionally, the proposed model uses only preexisting, revealed-preference automated fare collection data rather than requiring customer surveys. The proposed model is demonstrated using examples at the Chicago Transit Authority (CTA). The CTA experienced a large shift from passes to pay-per-use following a fare change in 2013, illustrating the potential value of accounting for fare product choices in fare scenario evaluation.

A flow model for optimization of highway toll stations

This paper is aimed at developing a highway toll stations route optimization model (HTSROM) for cost-effective and sustainable highway infrastructure planning and design. There are many factors for the locations of highway toll stations, including potential ridership, costs of land, construction and operation, and travel times. We developed HTSROM algorithm which used a genetic model to optimize and integrate a geographic information system for transfer of land-use, environmental, and topographic data during the optimal search process. The experimental results are discussed on the application, and more extensions of the HTSROM model remain to be studied in the future work.

Assessing the spatial equity of Seoul’s public transportation using the Gini coefficient based on its accessibility

ABSTRACTThe purpose of this research is to develop a methodology for calculating the index of the spatial equity for the public transportation services for the city of Seoul using the Lorenz curve and Gini coefficient based on accessibility to the services. The index was estimated for 100 m × 100 m cells first and then aggregated for 424 traffic analysis zones (TAZs) in the city. The indices for Seoul’s subway and bus were represented with and without the weighting factor. On the basis of this methodology, this study assessed the effect of an additional subway line on the spatial equity before and after its operation. Furthermore, an index of relative difference indicating the change in population and employment in the station areas was applied to investigate the changed effects of spatial equity between population and employment from the new subway line. The core findings from this research are summarized as follows. First, the subway network of Seoul provides more unequal public transportation services for the TAZs than for the bus network. Second, an improvement in the operation frequency of both transit modes would deepen the spatial inequity of Seoul. Third, a subway extension plan for Seoul could noticeably reduce the spatial inequity. The plan would improve the spatial equity for the population more than that for employment. The most important meaning of this study is to be found in combining spatial equity on the supply side with transit-oriented development (TOD) on the demand side. So, by establishing a TOD policy for the additional subway station areas, an appropriate land-use pattern can be established for maximizing potential ridership in consideration of the combination.

Rail Transit Route Optimization Model for Rail Infrastructure Planning and Design: Case Study of Saint Andrews, Scotland

This paper is aimed at developing a rail transit route optimization model (RTROM) for cost-effective and sustainable rail infrastructure planning and design. Locations of rail transit routes and stations depend on many factors, including potential ridership, costs of land, construction and operation, land use, connecting routes, passenger travel times, and environmental impacts. Suitably located rail transit routes effectively serve the potential demand while also minimizing costs and environmental impacts. Thus, a common problem in all rail infrastructure planning and design projects is to identify the best possible route that satisfies design constraints (such as minimum radius, maximum gradient, and vertical clearance), geographical considerations (such as demand generators and socio-economically sensitive areas) and objectives (such as minimization of associated costs and environmental impacts, or maximization of net benefits). The developed RTROM uses a genetic algorithm for performing optimization, which is integrated to a geographic information system for seamless transfer of land-use, environmental, and topographic data during the optimal search process. The model is tested in a real-world case study from Saint Andrews, Scotland. The lessons learned from the real-world application of the model are discussed. Many extensions of the model remain to be tested in future works.

Mapping Potential Metro Rail Ridership In Los Angeles County

Los Angeles County, as in many metropolitan areas, is coping with increasing street and highway traffic. Transit, and particularly rail, often is regarded as a strategy to help reduce urban traffic congestion especially in these economic downturn times, the rising price of gasoline and the acute awareness of global warming. The objectives of this paper are to identify the total potential riders who have access by walking to the Metro Rail, and the level of utilization, using the process of Trip Generation for data research. The potential ridership produced and attracted to each station is estimated using certain assumptions about the number of trips possibly generated by each person from residential and employment regions, based on the Origin-Destination (OD) flow patterns. Estimating the number of potential riders accessing the Metro Rail System involves a spatial analysis regarding the location of current Metro Rail stations serving populations in a reasonable access time by walking. Service Area Zones (SAZ) were mapped, representing the areas that the potential riders could be served by existing stations within a ten minute interval. The potential ridership was measured to be approximately one million, which represents a figure ten times larger than the present one. The results of the analysis were used to build an Atlas of Los Angeles County Ridership as the cartographic contribution in transportation and visual communication for transit forecasting and service planning of the system.

Comparative Study of Pedestrian Accessibility to Transit Stations Using Free and Proprietary Network Data

Availability of a transit service is a key factor in a traveler's choice of transportation mode. Transit service is a realistic option only if the service is available at or near locations when a person plans to travel. Whereas various measures exist for transit availability such as service frequency, the focus of this study was on the spatial aspect of pedestrian accessibility to transit stations, that is, on service coverage. Service areas are commonly used to visualize accessibility for pedestrians to transit systems and to analyze the potential ridership. Because the service area for a station is defined over the maximum network walking distance from a transit station, a complete street network that includes pedestrian segments, that is, shortcuts, is highly important for a realistic assessment of service areas. Whereas most proprietary geodata providers concentrate solely on car-related geodata, public domain street data and volunteered geographic information, such as OpenStreetMap, provide a potential valuable source for pedestrian data. The authors compared the amount of pedestrian-related data between freely available sources (OpenStreetMap or TIGER or both) and proprietary providers (Tele Atlas or NAVTEQ or both). The effect on modeling transit accessibility for pedestrians was analyzed for five U.S. and four German cities, and differences between these two countries were identified.

Land Use–Based Transit Planning

Bus rapid transit (BRT) is gaining greater interest by transportation planners and decision makers in the United States because of concerns about growing traffic congestion and a desire for improved environmental quality and sustainable land use development patterns. To help planners and decision makers, FTA developed the BRT implementation guidelines based on the 26 case studies located in North America, Australia, Europe, and South America. The FTA guidelines serve as a valuable source for the basic BRT concepts including the main system components and their types, general planning principles, and general design guidelines. However, the detailed implementation process, such as BRT station selection and route alignment determination, as one of the primary and practical concerns by transportation planners and decision makers, has not been studied in detail. A conceptual planning approach used to quickly evaluate potential BRT stations and alignments based on a case study in Placer County, California, is presented. The approach relies on land use development criteria and an analogous station-matching process to determine an initial range of potential ridership for future stations based on existing stations with similar land use development patterns. This information is combined to help gauge station feasibility and alignment options. The criteria used in this planning process were developed from a comprehensive literature review of the relationship between land use development and transit station ridership, as well as a unique database of transit ridership and land use development intensity for more than 80 existing transit stations in the San Francisco Bay Area and the Sacramento, California, region.

Spatial Representation and Scale Impacts in Transit Service Assessment

There has been much research over the past decade on aspects of bus-transit-system service and performance. Common to studies is the necessity that the population served by systems be estimated. Usually this entails delineating areas for which demand is covered through the use of geographic information systems (GIS), as well as deciding on how best to represent potential ridership spatially. However, little attention has been paid to spatial issues inherent in these choices. Research has emphasized the modeling of transit problems using GIS rather than giving explicit attention to the spatial considerations underlying analyses. To this end, we investigate issues of spatial representation in transit studies. A GIS-based analysis of bus transit for a small urban municipality is utilized to highlight that estimates of service coverage are sensitive to the choice of areal units representing ridership (scale), whether individual stops or routes are used to determine access, and how network versus Euclidean distances affect estimates. As such, spatial representation and modeling sensitivity are evaluated in the context of transit-service provision. The study results suggest that representation issues are critical in evaluating transit access in the context of planning and policy development using GIS.

Enhancing Transit’s Competitiveness: A Survey Methodology

Attracting customers to the bus and subway is an often-discussed goal for transit agencies. Current research methodologies such as travel demand, stated preference, and discrete choice models and opinion research are sometimes poorly suited to determining which types of service improvements, facility designs, or fare policy initiatives will be most effective in attracting potential customers. Drawing on elements of established methods, an approach is developed for use by New York City Transit focusing on travelers’ actual experience of the subway, bus, automobile, taxi, and car service and their reasons for mode choice. Survey questions were developed from focus group findings that New Yorkers actively choose between competing modes on the basis of six major factors, from how long the trip will take to availability of parking. Travelers choose the mode that presents the least difficulty for a particular type of trip. Survey results show that the areas of subway service improvement with the greatest potential ridership payoff are reducing how long it takes to make a trip, increasing the availability or ease of use of transit, and making traveling on the subway a more comfortable and relaxing experience. Two external factors—parking availability and taxi fares—are also found to significantly affect subway ridership.

Reaching Jobs in the Suburbs: Tri-Rail in South Florida

Continued expansion of development into suburban areas has led to increased suburb-to-suburb travel, which has contributed to the relative decline in ridership of commuter rail systems from the suburbs to the central business district (CBD). If commuter rail is to remain a viable element of the urban transportation network, it must serve new markets. The potential of commuter rail to serve suburb-to-suburb markets is examined. The size of the potential market for suburb-to-suburb commuter rail travel is estimated and compared with that of the suburb-to-CBD market. The mean market penetration is compared for suburb-to-suburb and suburb-to-CBD markets to determine whether there is a statistically significant difference between the two markets. The relationship between station-to-station distance and market penetration is tested for both the suburb-to-suburb and suburb-to-CBD markets. The results of this study indicate that the total potential ridership market for suburb-to-suburb home-based work trips is comparable with that for suburb-to-CBD homebased work trips. However, market penetration is greater for suburb-to-CBD trips than for suburb-to-suburb trips. Market penetration is found to increase with distance; however, initial results indicate that other factors affect market penetration more than distance.

Provision of accessible transportation alternatives for the transportation handicapped in Taiwan

AbstractFour transportation handicapped groups are identified in Taiwan (impaired ambulation, visually impaired, aged, and others) and nine accessible transportation alternatives are proposed. The costs and potential ridership for these nine alternatives in the Taipei area are estimated. Using analytic hierarchy process (AHP) and multicriteria evaluation with qualitative and quantitative data (MEQQD) methods, the most appropriate accessible transportation alternatives for each of the four handicap groups are selected. It is found that the best accessible transportation alternative for the impaired ambulation, the aged, and others would be a demand‐responsive lift‐equipped specialized van, while half‐fare subsidized taxi would be the second‐best option. By contrast, the best alternative for the visually impaired would be half‐fare subsidized taxi, while conventional bus with broadcast equipment would be the second‐best choice.

The transit pricing evaluation model: A tool for exploring fare policy options

A model is presented for probing a range of impacts which could be expected when instituting a new transit pricing system. The model weights on-board ridership survey responses based upon disaggregated fare elasticity estimates. In addition to forecasting the potential ridership and fiscal consequences of a new fare structure, the model incorporates revenue and cost data on specific users' trips to estimate changes in farebox recovery as well as effects on efficiency and equity. This paper describes the working components of the model followed by a demonstration of its use. Employing fare, cost, and trip-making data from the Southern California Rapid Transit District, the system's current pricing practices are examined. Long distance users were found to pay disproportionately low fares in relation to the cost of their trips, while very short riders were generally overchanged for their trips. A distance-based pricing scenario is then designed and tested with respect to its ability to remedy some of the problems associated with flat fares.