Daily Ridership Research Articles

Composite Accessibility Index: A Novel and Holistic Measure for Evaluating Transit Accessibility

The accessibility of public transport is a vital factor for the overall success of transit operations. It determines whether people choose to use and rely on public transport by assessing the ease of accessing opportunities. Accessibility, as a measure of the system’s performance, encompasses various travel segments and can be evaluated from different perspectives. Evaluating transit accessibility in data-constrained environments, particularly in developing countries, requires an optimal framework. The present study proposed a framework by modifying available accessibility measurement indices and trying to encompass all affecting variables in a single composite index. The integrated transit system comprising the bus rapid transit system and city bus in Surat city, India, is selected to demonstrate the proposed framework. The results of the ranked-based correlation coefficient test exhibit the comprehensive assessment capabilities of the proposed composite index in evaluating transit performance. Surat city’s transit network shows better coverage based on the gravity model theory, that is, moderate performance in local coverage offered capacity per population. However, it exhibits poor accessibility with respect to reaching destinations, resulting in below-average transit accessibility. Except for the city center and eastern part, 65% of the city area experiences inadequate accessibility of public transport. These findings align with the city’s low transit mode share of 2.5% and stagnant daily ridership of 0.25–0.28 million passengers in the last half decade. The composite index map serves as a planning tool for optimizing the utilization of available resources and guiding future policy implementations.

Predicting transit ridership using an agent-based modeling approach

Accurate ridership estimation is pivotal in the advancement of sustainable transit systems, be it for proposed or existing transit networks. A multitude of methods, including travel demand models, direct ridership models, and regression models, have been employed by practitioners and researchers to estimate ridership at both station and network levels. However, travel demand models, frequently utilized for new transit lines, exhibit intrinsic limitations due to their aggregate nature and complexity based on their types. Researchers have also identified deficiencies, such as the incapacity to capture small spatial resolutions and specific station characteristics, as these models are predominantly designed for large-scale analyses.This study aims to overcome these limitations by introducing a novel approach that utilizes three microscopic agent-based models to develop a travel demand modeling suite, providing a policy-sensitive forecasting tool. The suite comprises three agent-based models: SILO-MITO-MATSim. Validation of the model against previous year data is conducted, and projections are made for future years. The model is applied to estimate network-level ridership for the proposed ‘Purple Line,’ a light rail transit line planned by MDOT, MTA, Maryland, which will integrate with the Washington D.C. Metro, the fourth largest transit system in the USA, boasting an average daily ridership of half a million. The study’s findings indicate an anticipated ridership of approximately 31,230 passengers in the inaugural year of 2027. The proposed model offers a robust and policy-sensitive solution empowering decision-makers to make informed choices to support a sustainable transportation system.

Short-term high-speed rail passenger flow prediction by integrating ensemble empirical mode decomposition with multivariate grey support vector machine

Short-term prediction of high-speed rail (HSR) passenger flow provides a daily ridership estimation for the near future, which is critical to HSR planning and operational decision making. This paper proposes a new methodology that integrates ensemble empirical mode decomposition with multivariate support vector machines (EEMD-MSVM). There are four steps in this hybrid forecasting approach: (i) explore the correlation of multivariate HSR passenger flows at various stations based on archived data; (ii) decompose empirical modes of historical passenger flows for each HSR station, using EEMD to generate a number of intrinsic mode functions (IMFs) and a trend term; (iii) predict the IMF for each correlated station pair using MSVM; and (iv) reconstruct the refined IMF components to predict daily multivariate HSR passenger flows. The proposed EEMD-MSVM approach is demonstrated with multiple OD pairs along the Wuhan-Guangzhou HSR in China. Results from various origin-destination pairs, show that the EEMD-MSVM approach outperforms the existing ensemble empirical mode decomposition with grey support vector machine approach (EEMD-GSVM). With the multivariate approach, the mean absolute percentage error in demand prediction is reduced by 13.9%, 1.2%, 1.0%, 2.0%, and 2.7% and the mean absolute deviation is reduced by 78.8, 38.0, 4.4, 4.6, and 3.9, between these OD pairs respectively. Such increase in short-term demand prediction accuracy can significantly improve HSR service planning, operations, and revenue management in the real world.

Assessing Service Quality of Bike Sharing Service within Kathmandu Valley

Embracing a sustainable transportation paradigm requires a shift towards more eco –friendly modes of travel such as bike sharing services like Pathao & Indrive, Introducing a potentially disruptive and beneficial change to the transportation landscape in Nepal, bike-sharing hold a significant presence in Kathmandu, evidenced by their daily ridership and public recognition. Being a comparatively new concept, its service assessment becomes critical for the persistence of the facility. Perceived service quality assessment is a result of convoluted decision making process which require careful considerations of various factors including observed and unobserved variables. The study focuses on evaluating the service quality of Pathao & Indrive bike service employing the concept of structural equation modeling through the identification of unobserved influencing factors. Six latent variables were identified through factor analysis. An empirical model was developed to identify the interactions among major variables affecting service quality. SPSS 22 and SPSS Amos 21 were used for model development. The findings of the study presented that, the variable that have significant effect on service quality, was user’s safety followed by latent variables service accessibility & application efficiency. The study will provide practical insights to these services to enhance their effectiveness and usability making the service more acceptable. It will also provide clarity in understanding to inform adoption of suitable policy decisions.

Investigating the Key Factors Affecting Public Transport Ridership in Developing Countries through Structural Equation Modeling

Every country requires efficient public transportation to reduce the reliance on motor vehicles, decrease harmful emissions and noise pollution, and, most importantly, provide accessible transportation for urban populations with limited resources. Yet, several challenges hinder the efficiency of the public transportation system, affecting factors like daily ridership, infrastructure, revenue, and operations. Therefore, this research investigated barriers and their impact on the daily ridership of rail transit services, especially in densely populated areas characterized by high automobile usage, inadequate pedestrian facilities, and limited connectivity to other transportation modes. To accomplish this goal, a self-administered questionnaire was developed, and a survey was conducted to collect responses from commuters at various public transport hubs. Structural equation modeling (SEM) was used to analyze data gathered from a sample of 1000 participants. The findings from the SEM model indicated a positive correlation between Instrumental Attractiveness (IA) and Facility Design and Operation (FDO) with daily travel. Conversely, Service and Information (SI) along with Environmental Comfort (EC) exhibited a negative correlation with daily travel. Private sector employees without personal vehicles and a monthly income below fifty thousand exhibited a strong preference for public transport. Individuals with higher incomes who owned vehicles showed lower preferences towards public transport usage. The main contributors to low ridership in public transport included poor connectivity with metro and feeder buses, lack of availability of discount tickets, insufficient seating at stations, and inadequate route map information. Gasoline prices have a notable impact on daily ridership too, as higher fuel prices tend to attract more travelers to use public transport. The ridership data demonstrate a positive trend in response to increased gasoline prices. The findings from this study are useful for transportation engineers, practitioners, planners, and policymakers.

Studying the Impact of the COVID-19 Pandemic on Bikeshares as a Mode of Shared Micromobility in Major Cities: A Case Study of Houston

A bikeshare system offers a convenient and cost-effective transportation service, providing shared bicycles for short-term use by individuals. It promotes affordability for users while fostering a healthier environment. By offering an alternative for those without access to private vehicles, it helps mitigate the rise in private car usage. Bike sharing also provides an important first-mile/last-mile commuting option. This study focuses on investigating the effects of the COVID-19 pandemic outbreak on bikeshare ridership, with a specific case study centered around Houston, Texas. The employed methodology involves a descriptive analysis and Negative Binomial regression modeling to uncover the relationship between the dependent variable (ridership) and the independent variables. The descriptive analysis revealed an overall increase in ridership during the COVID-19 period in 2020. Notably, longer duration trips were substantially higher in 2020 compared to 2019. Furthermore, the majority of trips occurred during off-peak hours, followed by evening and morning peak periods. Through regression analysis, this study found that the COVID-19 pandemic had a statistically significant positive impact on average daily ridership, with the number of COVID-19 cases positively influencing ridership levels. Additionally, the weekend indicator had a statistically significant positive impact on the average daily ridership. On the other hand, the temperature indicator did not show any significant impact on the average daily ridership, while precipitation had a statistically significant negative impact, leading to decreased ridership levels. The study highlights the significance of various factors in influencing bikeshare usage, contributing to a better understanding of urban transportation dynamics during such unprecedented times.

Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances

Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investigate this. Eleven variables comprising socioeconomic attributes (population density and the average wage of employees), the availability of other transit modes (the number of taxis, the number of private cars, the bus network length, and the street network length), service-related attributes (the number of trains in operation, the average headway during peak period, the opened days, and rail fare), and a network topological characteristic (the average path length) were introduced as covariates. In addition, city class was introduced as a grouping variable to capture the unobserved characteristics of the investigated cities. Two linear mixed-effects models (M1 and M2) were developed, with an interaction term between city class and ES being introduced in M2. The variances were allowed to vary to account for the heterogeneity of the predictor, which improved the overall fit of the models. The estimation results reveal a significant positive within-subject effect of ES on DR in both models, which varied according to city class. This implies that for each city, improving ES increased DR and the scale of the increase in DR is associated with the city’s class. However, there is no evidence of a relationship between ES and DR across different cities. In addition, the heterogeneity in the variance indicates that underestimating the variances of the effects could lead to inaccurate conclusions. The results of this study can help transit agencies in mastering ridership and assessing the designs of URT network topology.

Mode-specific impacts of social distancing measures on the intra- and inter-urban mobility of public transit in Seoul during the COVID-19 pandemic

Social distancing measures (SDMs) mitigated the spread of coronavirus disease 2019 (COVID-19) by reducing human activity and mobility. However, few studies have explored SDMs’ effects on intra- and inter-urban mobility concerning public transit. This study employed the autoregressive integrated moving average with exogenous variables models to examine distinct-level impacts of SDMs on intra- and inter-urban daily ridership by transit in Seoul, South Korea, during the COVID-19 pandemic. This study found: (1) The effect of SDMs on transit mobility was more pronounced for subways than buses, despite both modes experiencing reduced mobility without completely ceasing operations. (2) No substantial disparity was observed between intra- and inter-urban transit mobility. (3) Although other measures effectively curtailed transit mobility, mask-wearing had no statistically significant impact on intra- and inter-urban transit usage. (4) Heightened SDMs (e.g., workplace closures, cancelation of public events, stay-at-home orders) have reduced transit mobility more for railways/subways than buses. These findings underscore the importance of considering the distinct impacts of diverse SDMs on transit mobility, thereby ensuring the sustainable functioning of public transit while mitigating the spread of infectious disease during pandemics.

The impact of Covid-19 pandemic on public transit use: Case study of Konya city

Since March 2019, Turkey has been enforcing various measures on the policies based on the trends in the COVID-19 cases. To restrain the spread of virus, policies limiting the mobility of people (i.e. lock downs, remote working and travel bans) were applied as in many other countries. Furthermore, social distancing calls for health concern directly caused a major reduction in public transit (PT) use. However, economic activities and new normal conditions required return of a part of the commute travels, which brought the issue of use of PT modes. This study focuses on the comparison of the PT mobility during the month of April in the 2019 (pre-pandemic), in 2020 during restrictions and in 2021 under new normal condition using the Smart Card (SC) data in Konya, Turkey. Monthly, daily and hourly distribution of ridership patterns are compared as well as usage patterns and characteristics of different bus lines are examined in detail. The results suggested that during the restrictions, the ridership was about one eight of the pre-pandemic periods, while it increased to 2.5 million ridership in 2021 which is still very low. Daily ridership in 2020 showed no PT mobility due to lockdowns, while during weekdays, hourly ridership distributions were changed parallel to changes in the work/education activity schedules. Evaluation of the bus lines having highest ridership in 2019 with 2020 and 2021 showed some of the bus lines were cancelled during the pandemic and routes/frequencies changed. The results showed the importance of PT management during pandemic which is very challenging due to economic loss and fear of infection by public. However, it should be emphasized the importance of continuation of public transportation in terms of accessibility and equity for all.

Smart Metro: Deep Learning Approaches to Forecasting the MRT Line 3 Ridership

Since its establishment in 1999, the Metro Rail Transit Line 3 (MRT3) has served as a transportation option for numerous passengers in Metro Manila, Philippines. The Philippine government's transportation department records more than a thousand people using the MRT3 daily and forecasting the daily passenger count may be rather challenging. The MRT3's daily ridership fluctuates owing to variables such as holidays, working days, and other unexpected issues. Commuters do not know how many other commuters are on their route on a given day, which may hinder their ability to plan an efficient itinerary. Currently, the DOTr depends on spreadsheets containing historical data, which might be challenging to examine. This study presents a time series prediction of daily traffic to anticipate future attendance at a particular station on specific days.

Causal impacts of the COVID-19 pandemic on daily ridership of public bicycle sharing in Seoul

Public bicycle can be a disease-resilient travel mode during the coronavirus disease 2019 (COVID-19) pandemic. Nonetheless, its evidence on public bicycle sharing is still inconclusive. This study used Bayesian structural time series models and causal impact inference for the data on the daily ridership of public bicycles in Seoul, South Korea, for 1826 days from January 1, 2017, to December 31, 2021. The study found that the usage of public bicycles was robust against the COVID-19 pandemic even in densely populated Seoul. Compared with the pre-pandemic period, public bicycles’ usage was unaffected on days when weather conditions, such as snow, rain, and wind speed were not as severe, as well as on days with non-seasonal event factors, such as weekdays, public holidays, and traditional Korean holidays. In addition, its robustness against the pandemic became more pronounced as the number of bicycle racks increased and the intensity of social distancing increased. However, public bicycles were in demand primarily for leisure and exercise, not for travel, during the pandemic. Public bicycle sharing can be a disease-resilient travel mode. Continuous investment in infrastructure such as bicycle paths and public bicycle is required to become a more resilient travel mode against infectious diseases.

The impact of COVID-19 and related containment measures on Bangkok’s public transport ridership

The COVID-19 pandemic and related measures used to contain its spread affected public transport ridership in cities around the world. In Thailand, the government issued 41 Royal Decrees between April 2020 and December 2021 to mitigate the spread of the pandemic. In this study, we investigate how Bangkok's public transport services (bus, metro, and boat) have been affected during this period by analyzing the daily ridership data, confirmed COVID-19 cases, and aggregated travel trends by trip destinations using from Google mobility reports. The results show that public transport ridership decreased as daily COVID cases increased and the levels of restraining measures became higher. However, other factors, such as relative strictness compared to earlier measures and sequencing of the measures seems to have had an impact on the ridership. Moreover, the impact on ridership trends is unique for each of the three modes. Bus and metro ridership appear to be more sensitive to the changes in restrictions than the boats. Bus and metro ridership also shows similar changes in the travel trends concerning the place of visit. The findings reported here provide first insights into how Bangkok's public transport systems were affected and suggest the rationale of why different public transport modes were affected differently. These results can be useful for researchers and for decision-makers who plan and design policies and measures for public transport services.

Evaluating and Enhancing the Operational Performance of Public Bus Systems Using GIS-based Data Envelopment Analysis

In this paper, the baseline performance level of Al Ain Public Bus Service is evaluated using Data Envelopment Analysis (DEA) based on some selected input (travel time per round trip, total number of stops, total number of operators, total number of buses) and output (daily ridership and vehicle-kilometer) variables. Two types of scenarios were developed and tested. The first set of scenarios aimed at investigating the possibility of reducing the operating cost while maintaining the same performance levels (efficiency and effectiveness) for the routes. The second set of scenarios was used to demonstrate how the baseline performance levels can be improved by slightly altering the route alignment (and subsequently input and output variables). Sensitivity analysis was then conducted to measure the efficiency and effectiveness of each route. Conclusions on how the transit authority can reduce daily operating hours while maintaining the existing performance level are made. Also, suggestions are presented on how to improve the overall performance level of the bus service by changing some route characteristics.

Quantitative Evaluation of TOD Performance Based on Multi-Source Data: A Case Study of Shanghai.

Transit-oriented development (TOD) has been widely adopted as a primary urban planning strategy to better integrate transit and land use; further, the pedestrian-oriented perspective has been receiving increasing attention. However, most studies so far have only focused on few features and fail to capture comprehensive perceptions in the transportation (T), pedestrian-oriented accessibility (O), and urban development (D) dimensions. New emerging urban datasets provide a more refined and systematic approach to quantify the characteristics of metro station areas. This study offers a more efficient and convenient process and comprehensive approach to measure TOD performance. With a combination of traditional data collected by an official department, high-resolution open data, and innovative technology, large-scale analyses of 347 metro stations in Shanghai were conducted. Fifteen indicators for T, O, and D were chosen to categorize TOD performance into five clusters. Radar charts, boxplots, and colored maps were used to display numerous quantitative factors for each type. Combining the results with the Shanghai Comprehensive Plan (2017–2035) showed that the majority of Cluster 4 is located at the center of the Five New Towns. The correlation analysis between ridership and TOD performance showed that the transportation dimension indicator has a strong correlation with daily ridership, followed by the O and D indicators. Moreover, ridership per capita was found to be affected by resident density, employment density, O value, and D value, whereas no significant correlation was found between ridership per capita and T value. Population plays a pivotal role in metro passenger traffic, indicating ridership per capita had a high, strong correlation with resident density, with R = 0.658 for weekdays and R = 0.654 for weekends. This study reinterpreted the node-place method and 5Ds framework, resulting in a renewal method with new datasets and analysis tools. It contributes to providing pedestrian-oriented TOD planning methodology for urban planners and policymakers by combining T, O, and D dimensions and visualizing the results with current urban planning.

Causal Impacts of the Coronavirus-19 Pandemic on Daily Ridership of Public Bicycle Sharing in Seoul

Causal Impacts of the Coronavirus-19 Pandemic on Daily Ridership of Public Bicycle Sharing in Seoul

Causal Impacts of the Coronavirus-19 Pandemic on Daily Ridership of Public Bicycle Sharing in Seoul

Causal Impacts of the Coronavirus-19 Pandemic on Daily Ridership of Public Bicycle Sharing in Seoul

Prediction of “L” Train’s Daily Ridership in Downtown Chicago During the COVID-19 Pandemic

In this study, we utilized a random forest model to predict the "L" train’s daily ridership in the Chicago downtown area during the pandemic based on environmental, transportation, and COVID-19-related factors. The results indicated that the model accurately predicts ridership one month in advance. However, its accuracy degraded over time. Moreover, average temperature, stay-at-home order status, and percentage of home renters were found to be the most important factors contributing to ridership.

Spatiotemporal behaviors of the ridership of a public transportation system during an epidemic outbreak: case of MERS in Seoul.

During May and June 2015, an outbreak of the Middle East respiratory syndrome (MERS) occurred in Korea, which raised the fear of contagion throughout society and suppressed the use of public transportation systems. Exploring daily ridership data of the Seoul bus transportation system, along with the number of infected patients and search volume in web portals, we observe that ridership decreased abruptly while attention was heavily focused online. Then this temporal reduction recovered exponentially with a characteristic time of 3 weeks when newly confirmed cases began to decrease. We also find with the data of ranked keywords of web portals that areas with severely reduced ridership tended to cluster and spatiotemporal variations of such clusters were highly associated with general hospitals where MERS patients were treated. On the other hand, the spatial reduction in ridership relaxed algebraically with the distance from a general hospital while the outbreak was severe. We further probe the influence of the epidemic outbreak in the framework of linear response theory, which relates the responses to the epidemic outbreak (“perturbation”) with correlations in the absence of the perturbation. Indeed, the spatial correlation function of the ridership changes is observed to follow a power law, sharing the same exponent as the spatial relaxation of the response function. This new theoretical approach offers a useful tool for understanding responses of public transportation system to epidemic or accidental disasters.

Understanding the Effects of Fare Discount Schemes to Metro Transit Ridership Based on Structural Change Analysis

Since fare discounts have been regarded as an effective economical measure to increase passenger flow, it is helpful for local governments and transit operators to understand its impact on ridership. Taking Xiamen, China, as an example, this study uses transaction data to analyze the changes of weekday daily metro ridership after the opening of Xiamen Metro Line 1. At the initial stage of operation of Xiamen Metro Line 1, there are three preferential schemes: discount per trip, money reduced per trip, and discount after reaching the accumulated fare. Therefore, the algorithm of the iterated cumulative sums of squares is introduced to identify structural change points of the time series of daily ridership, which varies according to the type of ticket. The effects of different fare discounts on total ridership and ridership varied by ticket types are analyzed by the regression discontinuity method. The results show that the dates of structural change points are well-matched with the start and end dates of preferential schemes. Each preferential scheme has its own benefited groups. During the fare discount period, the number of passengers gradually increased. But after the cancellation of the favorable preferential scheme, the number of passengers decreased sharply. By understanding the impact of fare discounts on ridership in Xiamen, China, several metro ticketing policy recommendations are put forward, including raising the focus on E-Tickets, formulating more attractive preferential measures to promote the mode conversion of private cars and vehicles to metro, paying attention to high-frequency passengers, and seeking common subsidies from the financial industry to achieve a win-win situation. In addition, the analytical framework proposed in this study can be used to evaluate the effectiveness of other transportation policies in the future.

What can we learn from 9 years of ticketing data at a major transport hub? A structural time series decomposition

Mobility demand analysis is increasingly based on smart card data, that are generally aggregated into time series describing the volume of riders along time. These series present patterns resulting from multiple external factors. This paper investigates the problem of decomposing daily ridership data collected at a multimodal transportation hub. The analysis is based on structural time series models that decompose the series into unobserved components. The aim of the decomposition is to highlight the impact of long-term factors, such as trend or seasonality, and exogenous factors such as maintenance work or unanticipated events such as strikes or the COVID-19 health crisis. We focus our analysis on incoming flows of passengers to two transport lines known to be complementary in the Parisian public transport network. The available ridership data allows analysis over both long-term and short-term time horizons including significant events that have impacted people's mobility in the Paris region.