Train Ridership Research Articles

Exploring Melbourne Metro Train Passengers’ Pre-Boarding Behaviors and Perceptions

The focus on sustainable transportation has increased interest in promoting sustainable modes of transport, such as rail. Understanding train passengers’ behaviors and perceptions is essential to enhance their travel experience and increase train ridership. Pre-boarding behaviors and perceptions are crucial in shaping the overall train travel experience. However, there are limited studies that have developed a systematic framework for investigating train passengers’ pre-boarding behaviors and perceptions. This paper examines the train passenger’s pre-boarding behaviors and perceptions about the station and platform. The study adopts a mixed-methods approach by developing a pre-boarding decision framework and combining it with questionnaire surveys to explore passengers’ behaviors and perceptions on the platform before boarding. A total of 429 valid responses from Melbourne metro train users were used for analysis. Descriptive statistics and correlation techniques were applied to identify patterns and relationships. The findings reveal common pre-boarding behaviors and perceptions. Furthermore, the study uncovers factors influencing these behaviors and perceptions, such as passenger demographics, travel patterns, and specific trip characteristics. For example, carrying large items and travel frequency significantly impact passengers’ travel experience in the pre-boarding phase. Waiting time, group travel, carrying small items, gender, and age group also significantly impact some pre-boarding behavior variables. Travel time, on the other hand, makes no significant impact on any of the pre-boarding variables that we examined. This research provides valuable insights for rail service operators and policymakers to enhance the pre-boarding experience, optimize station design, and improve passenger satisfaction.

Tracking the Transit Divide: A Multilevel Modelling Approach of Urban Inequalities and Train Ridership Disparities in Chicago

Using a multilevel modelling approach, this study investigates the impact of urban inequalities on changes to rail ridership across Chicago’s “L” stations during the pandemic, the mass vaccination rollout, and the full reopening of the city. Initially believed to have an equal impact, COVID-19 disproportionally impacted the ability of lower socioeconomic status (SES) neighbourhoods’ to adhere to non-pharmaceutical interventions: working-from-home and social distancing. We find that “L” stations in predominately Black or African American and Hispanic or Latino neighbourhoods with high industrial land-use recorded the smallest behavioural change. The maintenance of higher public transport use at these stations is likely to have exacerbated existing health inequalities, worsening disparities in users’ risk of exposure, infection rates, and mortality rates. This study also finds that the vaccination rollout and city reopening did not significantly increase the number of users at stations in higher vaccinated, higher private vehicle ownership neighbourhoods, even after a year into the pandemic. A better understanding of the spatial and socioeconomic determinants of changes in ridership behaviour is crucial for policymakers in adjusting service routes and frequencies that will sustain reliant neighbourhoods’ access to essential services, and to encourage trips at stations which are the most impacted to revert the trend of declining public transport use.

What moves Hong Kong's train ridership?

Hong Kong is a densely populated international metropolis with ∼7.5 million residents living in a small geographic area of ∼1,100 km2. Like some major cities around the world, it has a vast public transportation system that moves ∼12.4 million passengers per day, ∼42% of which is attributable to the Mass Transit Railway's (MTR's) extensive network interconnecting widely dispersed stations across Hong Kong. MTR's ridership substantially declined in 2019 because of social unrest and related system service suspension (SSS). This decline was further exacerbated by Covid-19's outbreak in 2020. Using a hand-collected sample of monthly data for January 2000–June 2020, we estimate a log-linear demand regression to find that MTR's ridership is price and income inelastic, varies seasonally, and exhibits a trend of gradual growth. Further, Covid-19, social unrest and SSS significantly reduce MTR's ridership. Finally, the estimates for passenger welfare losses due to SSS and social unrest are large, revealing the potential gains from improving MTR's service reliability and resolving the root causes of social unrest.

A passenger-pedestrian model to assess platform and train usage from automated data

We present a transit model that, based on automated fare collection and train tracking data, describes pedestrian movements in train stations and vehicle-specific train ridership distributions. Our approach differs from existing models in that we describe on-board passenger dynamics and pedestrian dynamics at stations in a joint framework. We assume that travelers first decide on the train(s) they will take, and then pursue their journey through the network by successively choosing pedestrian paths, waiting positions on platforms, and specific train cars. Travelers explicitly maximize their travel utility. We model how crowding influences their walking speeds, and how it affects travel utility both at stations and on-board. To illustrate the framework, we present a real case study of a major Dutch rail corridor, for which we collect a rich set of passenger, pedestrian and train operation data. We observe a good agreement of model estimates with empirical observations, and discuss the use of the model for various transit-related problems including level-of-service assessment, crowding estimation, transit optimization, and integrated investment appraisal.

The influence of weather conditions on adult transit ridership in the sub-tropics

This paper investigates the influence of local weather conditions on adult transit ridership across three transit modes. Drawing on smart card data and half hourly weather station records for a 12 month period, analysis reveals that weather imposes an effect on adult transit ridership and that its influence varies by mode. Ferry ridership is found to be more sensitive to changes in weather compared to either bus or train ridership. Findings also reveal that weather’s influence on ridership varies across the course of a day. During morning and evening peak hours, weather is shown to exert a weaker effect than other periods throughout the day. We argue that our findings are important in their capacity to contribute to a new evidence base with the potential to inform the (re)design of more weather-resilient transit systems by shedding new light on the weather–transit ridership relationship.

Gender and Rail Transit Use: Influence of Environmental Beliefs and Safety Concerns

Research suggests that gender influences attitudes toward both the environment and safety. While pro-environmental attitudes might encourage transit use, safety concerns might discourage transit use if the transit environment is perceived as unsafe. To quantitatively examine how gender, environmental beliefs, and safety concerns jointly affect transit use, results are analyzed from a longitudinal quasi-experimental study which conducted pre- and post-opening travel surveys near a new light rail transit service in Los Angeles. It is found that the influence of safety concerns on transit use is more prominent than that of environmental attitudes, particularly for women. Living closer to a new light rail transit station correlates with an increase in train ridership. This effect, however, is significantly lower for women. The results suggest that to foster transit use, reducing personal safety concerns related to transit may be more effective than increasing public awareness of transportation-related environmental issues, especially for attracting female riders.

Quantitative Analysis Of Crime Incidents In Chicago Using Data Analytics Techniques

In this paper we aim to identify certain social factors that influence, and thus can be used to predict, the occurrence of crimes. The factors under consideration for this analytic are social demographics such as age, sex, poverty, etc., train ridership, traffic density and the number of business licenses per community area in Chicago, IL. A factor will be considered pertinent if there is high correlation between it and the number of crimes of a particular type in that community area.

Approaches, technologies and importance of analysis of the number of train travellers

ABSTRACTSeveral studies have pointed to the difficulties of obtaining good data on train ridership. This paper is a literature review on how the number of travellers on trains are measured, including technologies and practices for measuring actual ridership. There are a number of publications and practical work done on estimating ridership. We find there are several technologies that can be applied for measuring ridership on trains. The technologies and approaches include (1) Manual counts and surveys, (2) On-board sensors such as door passing, weight, CCTV and Wi-Fi-use, (3) Ticketing systems, ticket sales or ticket validation, and (4) Tracking of travellers for larger part of the journey, e.g. by mobile phones and payments. Data from on-board sensors and ticketing systems are both managed by public transportation providers. By contrast, surveys, payments statistics and mobile phone data may be available to stakeholders outside the public transportation system, which can be an advantage, as access to ridership data can be an issue for business reasons. Furthermore, mobile phone data appears as an interesting option, as they can track complete journeys. New technologies, and especially mobile phone data, are therefore of special interest in future uses of ridership data for evaluations and quality assessments.

Exploring distance decay pattern of public transport-induced agglomeration and its impacts on train ridership attraction

Public transport infrastructure creates the effect of agglomeration through transportation externalities. Effective density is an accessibility based agglomeration that was raised as a positive externality from public transportation investments. The aim of this paper is to understand whether public transport facility would induce agglomeration around stations and furthermore induce train ridership. A methodology was developed to reveal the causality of effective density on ridership and reduce the confounding effects from land use-related determinant factor. This was shown by the propensity score matching that tested if effect of a station being in the treatment group (high effective density stations) on train ridership was influenced by land use characteristics of catchment stations. The causality of effective density on ridership was compared between station groups. Findings showed the effect of treatment group was higher in the matched sample compare to the unmatched sample. This difference may be assigned as the true effect of public transport induced agglomeration which was higher after controlling the land use characteristics of stations. Thus, the inclusion of land use variables in the model prediction may has the effect of rendering the influence of effective density variable lower in the model. These findings could guide station catchment area planning to maximise effective density benefits on train ridership.

Use of mobile phone data for analysis of number of train travellers

Several studies have pointed to the difficulties of obtaining good data on train ridership. There are at least two challenges regarding these data. First, train operators consider such data confidential business information, especially in high resolution. Second, the data that actually are available vary in quality and coverage. This paper studies mobile phone data as an alternative measure to obtain data about train ridership.Handset counts were obtained from one telecom operator for selected mobile phone base stations and compared with timetable data and APC. The selected base stations are located so that it is likely that a large share of the mobile phone traffic is generated by train passengers. The number of units connected to a base station is found to correspond relatively well with the trains that pass close to the base stations. A ratio between the handset count and APC data appear as promising in utilizing handset count to calculate train ridership, with ratios around one in the rush hours. We discuss preliminary results as well as methodological and technical challenges.To make sure that we do not violate privacy concerns, the data used in the study have been approved by personal privacy representatives.

Modeling the Influence of Weather on Transit Ridership: A Case Study from Brisbane, Australia

This paper examines the influence that weather conditions exert on transit ridership in sub-tropical Brisbane, Australia. Drawing on transit smart card and weather station records covering a period of six months, the weather-transit ridership relationship across three different travel modes is modeled. Our results reveal that weather does impose an effect on transit ridership and that its influence varies by both transit mode and across weekdays and weekends. On weekdays, the heat index, an indicator of human-perceived temperature, was found to exert a negative effect on bus and train ridership, while ferry ridership was shown to be most sensitive to rainfall. On weekends, the ridership across all three transit modes decreased on rainy days. Results from quantile regression suggest that on weekdays during periods of higher ridership the influence of weather on transit ridership becomes stronger, while on weekends the influence of weather is lessened. We argue that our findings are important for transit agencies in their capacity to unveil how an important predictor of ridership variation, weather, exerts an influence across a transit system. These preliminary findings mark the first step in assembling the necessary evidence to build a more weather-resilient transit system.

2437 - Connecting Cycling to Transit: A Typology of Toronto Region Commuter Rail Passengers

Highest Scoring Abstract: Undergraduate/Masters Background Nearly one in four trips to commuter rail stations in the Toronto region are between one and five kilometers in length – a reasonable distance for cycling. Yet only 1% of passengers arrive by bike, with the majority (62%) of nearly 100,000 daily passengers driving and parking at a station. A sixteen-billion-dollar investment in enhanced rail service is expected to double current train ridership. To support this growth, it is necessary to increase the use of non-parking-dependent station access modes. Cycling has potential to be a sustainable alternative for short station access trips, while also promoting superior health outcomes associated with a more active lifestyle. This study develops a cycling typology of passengers to foster a better understanding of customer perceptions and to identify customer segments who may be willing to cycle to the station. Methods Passengers intercepted at three suburban commuter rail stations were invited to complete an online survey (n = 300). Drawing on proven cycling determinants from the literature, the survey assessed willingness to cycle in adverse weather, the importance of end-of-trip facilities, comfort cycling on various types of infrastructure, and other personal identity and societal factors. A principal component analysis of the survey data followed by k-means cluster analysis was used to develop a typology of respondents. Results Using six factors derived from 17 survey variables, four distinct types of cyclists were identified: All-around cyclists (10%) most frequently cycle for transportation purposes and are most comfortable cycling in adverse weather conditions. Recreational cyclists (29%) frequently cycle for recreation but rarely for transportation purposes and are more comfortable on low-stress facilities such as paths, residential streets, and protected bike lanes than on busy roads. The safety-conscious (33%) do not frequently cycle, but express comfort with low-stress facilities. Finally, the facility-demanding (28%) express the strongest support for end-of-trip facilities and are least likely to agree that cycling is a practical way to reach the station. Nearly all (95%) of those who currently cycle to a station fall within the all-around type. Females are nearly twice as likely as males to be represented in the safety-conscious type. Conclusions Efforts to increase cycling to rail stations will necessitate encouraging recreational cyclists and the safety-conscious to ride for transportation purposes. Low-stress cycling networks surrounding the stations are critical in supporting these efforts, particularly for female riders.

A multi-modal network approach to model public transport accessibility impacts of bicycle-train integration policies

In the Netherlands, the bicycle plays an important in station access and, to a lesser extent, in station egress. There is however fairly little knowledge in the potential effects of bicycle-train integration policies. The aim of this paper is to examine the impacts of bicycle-train integration policies on train ridership and job accessibility for public transport users. We extended the Dutch National Transport Model (NVM) by implementing a detailed bicycle network linked to the public transport network, access/egress mode combinations and station specific access and egress penalties by mode and station type derived from a stated choice survey. Furthermore, the effects of several bicycletrain integration policy scenarios were examined for a case study for Randstad South, in the Netherlands, comprising a dense train network with 54 train stations. Our analysis shows that improving the quality of bicycle routes and parking can substantially increase train ridership and potential job accessibility for train users. Large and medium stations are more sensitive to improvements in bicycle-train integration policies, while small stations are more sensitive to improvements in the train level of service.

Substitution or Complementarity

The effects of a real-time bus information system, Chicago Transit Authority (CTA) Bus Tracker, on the ridership of the CTA transit rail system in Chicago, Illinois, are examined with panel data on monthly average weekday ridership at each transit train station in the entire CTA system from January 2005 through December 2010. Other factors that might contribute to changes in CTA rail ridership (e.g., socioeconomic situation, transit service, and weather) are controlled. Results of a mixed linear model indicate that Bus Tracker service increases CTA train ridership. This increase might be attributable to the increased intermodal transfer efficiency between CTA buses and trains that results from the real-time bus information provided. Findings suggest that the complementarity effect of providing real-time bus information outweighs its substitution effect on the ridership of a connected transit rail system. Together with the findings from an earlier study (which show that Bus Tracker service helps to increase bus ridership), study results provide empirical evidence in support of the hypothesis that implementation of a real-time transit information system leads to gains in transit ridership.

The effects of ozone action day public advisories on train ridership in Chicago

Ground level ozone is a criteria pollutant that is significantly affected by transportation patterns. Ozone action day advisories represent one type of voluntary ozone-abating program operating in urban areas where ozone pollution is concentrated. When forecasts predict that ground level ozone will exceed healthy levels, public advisories urge citizens to voluntarily choose public transportation as a means of eliminating automobile trips and reducing mobile emissions. To obtain credit for emission reductions spurred by voluntary programs, states must provide verifiable reduction estimates. This paper applies a fixed effects regression model to a panel of hourly Chicago Transit Authority train ridership data to evaluate the potential effects of Ozone Action Day advisories in Chicago from 2002 to 2003. Findings show that while the overall effect of ozone action days on ridership is not significant, there are statistically significant changes in hourly ridership patterns that indicate a more complex relationship between the public advisories and travel behavior.

Amtrak: A cost-effectiveness analysis

Intercity rail passenger train services in the United States are provided over a basic national route system by a Congressionally created quasi-public corporation called Amtrak. Although Amtrak has succeeded in arresting the long-term decline in passenger train ridership, operating deficits have grown substantially in recent years, requiring increased public subsidy. The justification for on-going public support rests on the belief that rail passenger transport contributes to fulfilling national transport policy objectives in the areas of safety, energy conservation, congestion alleviation and environmental protection. This paper examines the Amtrak impact in each of these goal areas and evaluates the organization's longer term potential. The evidence does not indicate that rail passenger train services contribute meaningfully to any of the national policy concerns outside the Northeast Corridor. Continued subsidization of the existing system appears unjustified, and a major route restructuring seems necessary.