An analysis of Metro ridership at the station-to-station level in Seoul

While most aggregate studies of transit ridership are conducted at either the stop or the route level, the present study focused on factors affecting Metro ridership in the Seoul metropolitan area at the station-to-station level. The station-to-station analysis made it possible to distinguish the effect of origin factors on Metro ridership from that of destination factors and to cut down the errors caused by the aggregation of travel impedance-related variables. After adopting two types of direct-demand patronage forecasting models, the multiplicative model and the Poisson regression model, the former was found to be superior to the latter because it clearly identified the negative influences of competing modes on Metro ridership. Such results are rarely found with aggregate level analyses. Moreover, the importance of built environment in explaining Metro demand was confirmed by separating built environment variables for origin and destination stations and by differentiating ridership by the time of day. For morning peak hours, the population-related variables of the origin stations played a key role in accounting for Metro ridership, while employment-related variables prevailed in destination stations. In evening peak hours, both employment- and population-related variables were significant in accounting for the Metro ridership at the destination station. This showed that a significant number of people in the Seoul metropolitan area appear to take various non-home-based trips after work, which is consistent with the results from direct household travel surveys.     

Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models

A growing base of research adopts direct demand models to reveal associations between transit ridership and influence factors in recent years. This study is designed to investigate the factors affecting rail transit ridership at both station level and station-to-station level by adopting multiple regression model and multiplicative model respectively, specifically using an implemented Metro system in Nanjing, China, where Metro implementation is on the rise. Independent variables include factors measuring land-use mix, intermodal connection, station context, and travel impedance. Multiple regression model proves 11 variables are significantly associated with Metro ridership at station level: population, employment, business/office floor area, CBD dummy variable, number of major educational sites, entertainment venues and shopping centers, road length, feeder bus lines, bicycle park-and-ride (P&R) spaces, and transfer dummy variable. Results from multiplicative model indicate that factors influencing Metro station ridership may also influence Metro station-to-station ridership, varied by both trip ends (origin/destination) and time of day. In comparison with previous case studies, CBD dummy variable and bicycle P&R are statistically significant to explain Metro ridership in Nanjing. In addition, Metro travel impedance variables have significant influence on station-to-station ridership, representing the basic time-decay relationship in travel distribution. Potential implications of the model results include estimating Metro ridership at station level and station-to-station level by considering the significant variables, recognizing the necessity to establish a cooperative multi-modal transit system, and identifying opportunities for transit-oriented development.     

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

The 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.     

Secrets of success: assessing the large increases in transit ridership achieved by Houston and San Diego transit providers

This paper summarizes and updates the findings from an earlier study by the same authors of transit systems in Houston (all bus) and San Diego (bus and light rail). Both systems achieved unusually large increases in transit ridership during a period in which most transit systems in other metropolitan areas were experiencing large losses. Based on ridership models estimated using cross section and time series data, the paper quantifies the relative contributions of policy variables and factors beyond the control of transit operators on ridership growth. It is found that large ridership increases in both areas are caused principally by large service increases and fare reductions, as well as metropolitan employment and population growth. In addition, the paper provides careful estimates of total and operating costs per passenger boarding and per passenger mile for Houston's bus operator and San Diego's bus and light rail operators. These estimates suggest that the bus systems are more cost-effective than the light rail system on the basis of total costs. Finally, the paper carries out a series of policy simulations to analyze the effects of transit funding levels and metropolitan development patterns on transit ridership and farebox recovery ratio.     

Spatio-temporal analysis of rail station ridership determinants in the built environment

The development of new routes and stations, as well as changes in land use, can have significant impacts on public transit ridership. Thus, transport departments and governments should seek to determine the level and spatio-temporal dependency of these impacts with the aim of adjusting services or improving planning. However, existing studies primarily focus on predicting ridership, and pay relatively little attention to analyzing the determinants of ridership from temporal and spatial perspectives. Consequently, no comprehensive cognition of the spatio-temporal relationship between station ridership and the built environment can be obtained from previous models, which makes them unable to facilitate the optimization of transportation demands and services. To rectify this problem, we have employed a Bayesian negative binomial regression model to identify the significant impact factors associated with entry/exit ridership at different periods of the day. Based on this model, we formulated geographically weighted models to analyze the spatial dependency of these impacts over different periods. The spatio-temporal relationship between station ridership and the built environment was analyzed using data from Beijing. The results reveal that the temporal impacts of most ridership determinants are related to the passenger trip patterns. Furthermore, the spatial impacts correspond with the determinants’ spatial distribution, and the results give some implications on urban and transportation planning. This analysis gives a common analytical framework analyzing impacts of urban characteristics on ridership, and extending researches on how we capture the impacts of urban and other factors on ridership from a comprehensive perspective.

     

Transit passenger response: Short and long term elasticities using time series analysis

This paper investigates the effects of price and service changes on transit ridership. The concept of elasticity is introduced and the traditional methods for estimating elasticities are discussed. In this paper an extra dimension is added by investigating short and long term elasticities. Time series analysis, developed by Box and Jenkins is chosen for the analysis. The Box and Jenkins methodology is applied to a monthly time series of average weekday ridership on the Chicago Transit Authority (CTA) rail system. Four categories of explanatory variables are investigated: fare on the CTA rail system, service provided on the CTA rail system, cost of car trips and weather effects. The effects of gas prices and rail service were found to be significant; however the results indicate a twelve month delay before service changes influence ridership. The effect of transit fares was found to be insignificant, indicating that both the short and long term fare elasticities are zero.     

A time series analysis of monthly ridership for an urban rail rapid transit line

This paper presents two time series regression models, one in linear form and the other in logarithmic form, to estimate the monthly ridership of a single urban rail rapid transit line. The model was calibrated for a time period of about six and a half years (from 1978–1984) based on ridership data provided by a transit authority, gasoline prices provided by a state energy department, and other data.The major findings from these models are: (1) seasonal variations of ridership are –6.26%, or –6.20% for the summer period, and 4.77%, or 4.62% for the October period; (2) ridership loss due to a station closure is 2.46% or 2.41%; and (3) elasticities of monthly ridership are –0.233 or –0.245 with respect to real fare, 0.113 or 0.112 with respect to real gasoline price, and 0.167 or 0.185 with respect to real bridge tolls for the competing automobile trips. Such route specific application results of this inexpensive approach provide significant implications for policymaking of individual programs in pricing, train operation, budgeting, system changes, etc., as they are in the case reported herein and would be in many other cities.     

What determines rail transit passenger volume? Implications for transit oriented development planning

Transit oriented development (TOD) has been an important topic for urban transportation planning research and practice. This paper is aimed at empirically examining the effect of rail transit station-based TOD on daily station passenger volume. Using integrated circuit (IC) card data on metro passenger volumes and cellular signaling data on the spatial distribution of human activities in Shanghai, the research identifies variations in ridership among rail transit stations. Then, regression analysis is performed using passenger volume in each station as the dependent variable. Explanatory variables include station area employment and population, residents’ commuting distances, metro network accessibility, status as interchange station, and coupling with commercial activity centers. The main findings are: (1) Passenger volume is positively associated with employment density and residents’ commuting distance around station; (2) stations with earlier opening dates and serving as transfer nodes tend to have positive association with passenger volumes; (3) metro stations better integrated with nearby commercial development tend to have larger passenger volumes. Several implications are drawn for TOD planning: (1) TOD planning should be integrated with rail transit network planning; (2) location of metro stations should be coupled with commercial development; (3) high employment densities should be especially encouraged as a key TOD feature; and (4) interchange stations should be more strategically positioned in the planning for rail transit network.     

Evaluating light rail sketch planning: actual versus predicted station boardings in Phoenix

In recent years, transit planners are increasingly turning to simpler, faster, and more spatially detailed “sketch planning” or “direct demand” models for forecasting rail transit boardings. Planners use these models for preliminary review of corridors and analysis of station-area effects, instead of or prior to four-step regional travel demand models. This paper uses a sketch-planning model based on a multiple regression originally fitted to light-rail ridership data for 268 stations in nine U.S. cities, and applies it predictively to the Phoenix, Arizona light-rail starter line that opened in December, 2008. The independent variables in the regression model include station-specific trip generation and intermodal–access variables as well as system-wide variables measuring network structure, climate, and metropolitan-area factors. Here we compare the predictions we made before and after construction began to pre-construction Valley Metro Rail predictions and to the actual boardings data for the system’s first 6 months of operations. Depending on the assumed number of bus lines at each station, the predicted total weekday ridership ranged from 24,767 to 37,907 compared with the average of 33,698 for the first 6 months, while the correlation of predicted and observed station boardings ranged from r = 0.33 to 0.47. Sports venues, universities, end-of-line stations, and the number of bus lines serving each station appear to account for the major over- and under-predictions at the station level.     

“Comparing guideway transit implementation plans,including the impact on road traffic”

A methodology for comparing phased implementation plans for a new fixed guideway transit system in an urban area is presented. Four assumptions are made: (1) the guideway system replaces existing or planned bus service, (2) superior service on the new system results in increased ridership when compared to buses; (3) presence of the guideway facility redirects outward urban growth resulting in additional ridership, and (4) conversely, the absence of any action on the new guideway facility reinforces a diffuse urban growth pattern that creates an irreversible loss of transit ridership. The economic comparision of alternative plans includes total as well as “relative” inflation of principal cost components. A key feature of the proposed methodology is including in the comparisons the costs of private automobile mileage that could have been replaced by transit. These costs are expressed as “fuel” and “all other” automobile costs; favorable transit system implementation schedules can then be identified as a function of parametrically assumed values for these two unit costs. A hypothetical example demonstrates the proposed method.     

A disaggregate study of urban rail transit feeder transfer penalties including weather effects

Transfers between urban rail transit (URT) and its feeder modes represent a considerable barrier to its ridership and the network-wide usage of public transit. The aim of this research is to quantify the time-independent transfer penalty between URT system and feeder modes and to explore its variability by different factors. Based on Melbourne URT origin and destination survey data, this study focused on URT access and egress journeys and estimated URT feeder transfer penalties by formulating feeder mode choice models. With three-hourly weather data and demographical data introduced, this paper conducted disaggregate analyses to investigate the variability of URT feeder transfer penalty across weather conditions, trip types and individual characteristics. According to the model estimation results, the values of transfer penalty vary according to the direction of transfer and the preference ordering for different transfer combinations is URT-tram, URT-bus, tram-URT, bus-URT and auto-URT. It found that local weather elements in terms of air temperature and precipitation are significant factors resulting in the variability of the transfer perception by URT travellers. Transfer penalties for access journeys increase with the rise of air temperature. The non-linear effects of precipitation on URT feeder transfer penalties were observed. In addition, commuters perceive smaller transfer penalties than other travellers for all of the transfer combinations except for bus-URT transfers. Travelers from remote areas perceive smaller transfer penalties for access trips. Travellers’ loyalty to public transit restrains transfer penalties. The male travellers perceive higher transfer penalties than the female. The elderly travellers impose low transfer penalties to access journeys but high transfer penalties for egress journeys. Finally, the paper explored policy implications and details areas for future research.     

Transit trip distribution model considering land use differences between catchment areas

Sustainable land use planning and advanced public transport system are believed to be effective solutions to traffic congestion. To this end, it is important to reveal the relationship between transit ridership and land use. However, current Direct Ridership Models only focus on the relationship between single station's boarding volume and the corresponding catchment area land use. This paper analyzed the ridership distribution between transit stations by considering the land use difference between catchment areas. Land use difference was calculated from point of interest (POI) data extracted from an electronic map of Beijing; transit trip distribution volume was obtained from ‘automatic fare collection’ facility. After data specification, a transit ridership distribution model was proposed and calibrated. The calibration results suggest that land use difference has a directly proportional correlation with transit ridership distribution. The research findings build a bridge between detailed urban form and public transport, which is of significance for the further research of sustainable urban planning. Copyright © 2016 John Wiley & Sons, Ltd.     

The effect of slow zones on ridership: An analysis of the Chicago Transit Authority “El” Blue Line

Transit agencies frequently upgrade rail tracks to bring the system to a state of good repair (SGR) and to improve the speed and reliability of urban rail transit service. For safety during construction, agencies establish slow zones in which trains must reduce speed. Slow zones create delays and schedule disruptions that result in customer dissatisfaction and discontinued use of transit, either temporarily or permanently. While transit agencies are understandably concerned about the possible negative effects of slow zones, empirical research has not specifically examined the relationship between slow zones and ridership. This paper partially fills that gap. Using data collected from the Chicago Transit Authority (CTA) Customer Experience Survey, CTA Slow Zone Maps, and, the Automatic Fare Collection System (AFC), it examines whether recurring service delays due to slow zones affect transit rider behavior and if the transit loyalty programs, such as smart card systems, increase or decrease rider defections. Findings suggest that slow zones increase headway deviation which reduces ridership. Smart card customers are more sensitive to slow zones as they are more likely to stop using transit as a result of delay. The findings of this paper have two major policy implications for transit agencies: (1) loyalty card users, often the most reliable source of revenue, are most at risk for defection during construction and (2) it is critical to minimize construction disruptions and delays in the long run by maintaining state of good repair. The results of this paper can likely be used as the basis for supporting immediate funding requests to bring the system to an acceptable state of good repair as well as stimulating ideas about funding reform for transit.     

Transit system evaluation: Guideway bus vs. light rail transit

This paper describes a set of specialized spreadsheets that model the cost and performance of transit system options including light rail transit, guideway bus, express bus, and ride sharing. These spreadsheets are demonstrated by comparing a guideway bus (GWB) transit system and a light rail transit (LRT) system proposed for construction in an active rail corridor. The comparisons for assumed levels of transit ridership include guideway geometry, travel time, headways, vehicle requirements, grade crossing protection, and capital and operating costs. The planned GWB system runs on an exclusive dual guideway in the rail right-of-way, and the alternative LRT system operates on the existing rails with new bridges and track as needed for a dual guideway system. The analysis compares the two options for mode splits between 0.5% and 50%. Results show that while both options have approximately the same travel time, the GWB system costs approximately 30% less than the LRT system. The cost difference results primarily from lower GWB vehicle purchase and operating costs. The spreadsheets are available through the McTrans Center at the University of Florida, Gainesville, Florida.     

A whole-system approach to evaluating urban transit investments

New transit capital expenditures are typically evaluated in isolation from the transit/transport systems to which they belong. Problems with reporting performance elements such as ridership and costs are discussed. A focus on evaluating the total transport systems impact of new transit project implementation is called for. On this basis, new US rail transit systems have generally performed poorly. Total transit ridership has generally shown only minimal improvements and, at times, has declined. Financial performance has been disappointing in most cases, particularly when understood in the context of the additional system costs imposed through the reconfiguration of bus networks to serve the new rail systems. Low-cost approaches to improving basic transit services can often be more effective than either rail or bus capital-based projects. An obsession with technology leads to the wrong questions being asked. We should instead start inquiry with the study of needs.     

伦敦的新建轨道交通系统——Tramlink

新建轨道交通系统作为一种重要的公共交通工具,正在得到越来越广泛的应用。介绍了伦敦新建轨道交通系统——Tramlink的线路、车辆、运营管理、客流及特点等内容。作为地铁系统的补充,Tramlink在伦敦公共交通领域发挥着重要的作用。     

Impacts of weather on public transport ridership: Results from mining data from different sources

The existing studies concerning the influence of weather on public transport have mainly focused on the impacts of average weather conditions on the aggregate ridership of public transit. Not much research has examined these impacts at disaggregate levels. This study aims to fill this gap by accounting for intra-day variations in weather as well as public transport ridership and investigating the effect of weather on the travel behavior of individual public transit users. We have collected smart card data for public transit and meteorological records from Shenzhen, China for the entire month of September 2014. The data allow us to establish association between the system-wide public transit ridership and weather condition on not only daily, but also hourly basis and for each metro station. In addition, with the detailed trip records of individual card holders, the travel pattern by public transit are constructed for card holders and this pattern is linked to the weather conditions he/she has experienced. Multivariate modeling approach is applied to analyze the influence of weather on public transit ridership and the travel behavior of regular transit users. Results show that some weather elements have more influence than others on public transportation. Metro stations located in urban areas are more vulnerable to outdoor weather in regard to ridership. Regular transit users are found to be rather resilient to changes in weather conditions. Findings contribute to a more in-depth understanding of the relationship between everyday weather and public transit travels and also provide valuable information for short-term scheduling in transit management.     

Effects of new bus and rail rapid transit systems – an international review

Cities worldwide are implementing modern transit systems to improve mobility in the increasingly congested metropolitan areas. Despite much research on the effects of such systems, a comparison of effects across transit modes and countries has not been studied comprehensively. This paper fills this gap in the literature by reviewing and comparing the effects obtained by 86 transit systems around the world, including Bus Rapid Transit (BRT), Light Rail Transit (LRT), metro and heavy rail transit systems. The analysis is twofold by analysing (i) the direct operational effects related to travel time, ridership and modal shifts, and (ii) the indirect strategic effects in terms of effects on property values and urban development. The review confirms the existing literature suggesting that BRT can attract many passengers if travel time reductions are significantly high. This leads to attractive areas surrounding the transit line with increasing property values. Such effects are traditionally associated with attractive rail-based public transport systems. However, a statistical comparison of 41 systems did not show significant deviations between effects on property values resulting from BRT, LRT and metro systems, respectively. Hence, this paper indicates that large strategic effects can be obtained by implementing BRT systems at a much lower cost.     

Impacts of built environment and emerging green technologies on daily transportation greenhouse gas emissions in Quebec cities: a disaggregate modeling approach

This paper aims to investigate the impact of the built environment (BE) and emerging transit and car technologies on household transport-related greenhouse gas emissions (GHGs) across three urban regions. Trip-level GHG emissions are first estimated by combining different data sources such as origin–destination (OD) surveys, vehicle fleet fuel consumption rates, and transit ridership data. BE indicators for the different urban regions are generated for each household and the impact of neighborhood typologies is derived based on these indicators. A traditional ordinary least square (OLS) regression approach is then used to investigate the direct association between the BE indicators, socio-demographics, and household GHGs. The effect of neighborhood typologies on GHGs is explored using both OLS and a simultaneous equation modeling approach. Once the best models are determined for each urban region, the potential impact of BE is determined through elasticities and compared with the impact of technological improvements. For this, various fuel efficiency scenarios are formulated and the reductions on household GHGs are determined. Once the potential impact of green transit and car technologies is determined, the results are compared to those related to BE initiatives. Among other results, it is found that BE attributes have a statistically significant effect on GHGs. However, the elasticities are very small, as reported in several previous studies. For instance, a 10 % increase in population density will result in 3.5, 1.5 and 1.4 % reduction in Montreal, Quebec and Sherbrooke, respectively. It is also important to highlight the significant variation of household GHGs among neighborhoods in the same city, variation which is much greater than among cities. In the short term, improvements on the private passenger vehicle fleet are expected to be much more significant than BE and green transit technologies. However, the combined effect of BE strategies and private-motor vehicle technological improvement would result in more significant GHGs reductions in the long term.     

Impact of weather on urban transit ridership

Utilizing daily ridership data, literature has shown that adverse weather conditions have a negative impact on transit ridership and in turn, result in revenue loss for the transit agencies. This paper extends this discussion by using more detailed hourly ridership data to model the weather effects. For this purpose, the daily and hourly subway ridership from New York City Transit for the years 2010–2011 is utilized. The paper compares the weather impacts on ridership based on day of week and time of day combinations and further demonstrates that the weather’s impact on transit ridership varies based on the time period and location. The separation of ridership models based on time of day provides a deeper understanding of the relationship between trip purpose and weather for transit riders. The paper investigates the role of station characteristics such as weather protection, accessibility, proximity and the connecting bus services by developing models based on station types. The findings indicate substantial differences in the extent to which the daily and hourly models and the individual weather elements are able to explain the ridership variability and travel behavior of transit riders. By utilizing the time of day and station based models, the paper demonstrates the potential sources of weather impact on transit infrastructure, transit service and trip characteristics. The results suggest the development of specific policy measures which can help the transit agencies to mitigate the ridership differences due to adverse weather conditions.