Ridership Data Research Articles

Regional heterogeneity in Taiwan HSR demand developments: station accessibility and its effect on usage adoption

ABSTRACTIn order to maintain the sustainability of High Speed Railway (HSR), it is important to understand total and region-specific long-term demand. Our study targets the eight HSR stations that have been in operation in Taiwan since 2007. As the service itself has not undergone major changes, we examine the impact of other demand and supply factors in order to explain demand growth over time in consideration of regional heterogeneity. We focus on the effects of station accessibility as well as its impact on demand via usage ‘adoption rates’. The latter is related to product diffusion theory. By using monthly ridership data, we calculated yearly regional ‘adoption ratios’ to describe the spread of THSR usage. Our results show that regional heterogeneity in adoption ratios exist and influence its HSR demand pattern. We also discuss that station accessibility by transit is an important factor to understand long-term HSR demand developments. We show that initial bus services (shuttle bus and bus rapid transit (BRT)) influence demand in the short and long term. Taking the findings together, we suggest that therefore improving access modes, has positive impacts to short- as well as long-term HSR demand and require careful consideration by decision makers.

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.

Does demand for subway ridership in Manhattan depend on the rainfall events?

The Northeast United States, particularly New York State has experienced an increase in extreme daily precipitation during the past 50 years. Recent events such as Hurricane Irene and Superstorm Sandy, have revealed vulnerability to the intense precipitation within the transportation sector. In the scale of New York City, where transit system is the most dominant mode of transportation and daily mobility of millions of passengers depends on it, any disruption in the transit service would result in gridlocks and massive delays. To assess the impacts of rainfall on the subway ridership, we merged high resolution radar rainfall and subway ridership data to conduct a detailed analysis for each of the 116 subway stations at the borough of Manhattan. The analysis is carried out on both hourly and daily resolution level, where a spatial-temporal Bayesian multi-level regression model is used to capture the underlying dependency between the parameters. The estimation results are obtained through Markov Chain Monte Carlo sampling method. The results for daily analysis indicate that during weekdays, transit ridership in the stations located in commercial zones are less sensitive to the rainfall compared to the ones in residential zones.

Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure

Reliable and accurate estimates of metro demand can provide metro authorities with insightful information for the planning of route alignment and station locations. Many existing studies focus on metro demand from daily or annual ridership profiles, but only a few concern the variation in hourly ridership. In this paper, a geographically and temporally weighted regression (GTWR) model was used to examine the spatial and temporal variation in the relationship between hourly ridership and factors related to the built environment and topological structure. Taking Nanjing, China as a case study, an empirical study was conducted with automatic fare collection (AFC) data in three weeks. With an analysis of variance (ANOVA), it was found that the GTWR model produced the best fit for hourly ridership data compared with traditional regression models. Four built-environment factors, namely residence, commerce, scenery, and parking, and two topological-structure factors, namely degree centrality and closeness centrality, were proven to be significantly related to station-level ridership. The spatial distribution pattern and temporal nonstationarity of these six variables were further analyzed. The result of this study confirmed that the GTWR model can provide more realistic and useful information by capturing spatiotemporal heterogeneity.

Extreme weather, public transport ridership and moderating effect of bus stop shelters

This paper examines how extreme weather conditions influence urban public transport ridership with a particular focus on the role of bus stop shelters. Using bus ridership data from the Salt Lake City metropolitan area, we find that extreme weather such as very high and low temperatures, and heavy rainfall reduces public transport ridership, while bus stop shelters have a modest effect on mitigating ridership losses resulting from these adverse weather conditions. The moderating effect of shelters is more pronounced on weekdays, and for bus stops with lower service frequency and fewer transfers. Our research also shows that the installation of bus shelters correlates with a variety of factors including service frequency, land use types, and local socioeconomic and demographic characteristics. Overall, our findings suggest that public transport amenities with weather-proof attributes have the potential to retain and attract more ridership on extreme weather days.

<b>Method for Forecasting Fluctuation in Railway Passenger Demand for High-speed Rail Services</b>

In order to plan high-speed rail transport services efficiently, it is necessary to be able to forecast fluctuations in passenger demand based on historical ridership data. Forecasting is difficult however, because of the number of components making up passenger demand. An effective way to forecast demand therefore should be to decompose these fluctuations into several independent demand components, which can then be forecast individually. This study applied an independent component analysis to decompose the fluctuation into several independent components. A method was then developed to forecast the fluctuation in passenger demand based on actual ridership data, calendar array, and number of people mobilized for large events.

Impact of pricing and transit disruptions on bikeshare ridership and revenue

Bikeshare operators routinely explore options to improve ridership and revenue by studying interaction among pricing, service and operations. The objective of this research is to study the impact of introducing a new $2 fare for single-trip on revenue and ridership at Capital Bikeshare (CaBi) in the metro Washington DC region. The single-trip fare (STF) at CaBi is one of the three fare products aimed at casual users—the other two being 24-h pass and 3-day pass. STF was introduced inconjuction with SafeTrack, which is a major Metrorail track maintenance program intiated by the Washington Metropolitan Area Transit Authority. The impact analysis of STF includes studying the influence of SafeTrack on CaBi ridership. The analysis was based on revenue and ridership data before and after the implementation of STF and weather data for the region. The results showed that the first-time casual members increased by as much as 79% immediately after the introduction of STF, along with an overall increase in ridership. Jurisdiction-level analysis indicated a statistically significant increase in casual user ridership for identical 12-month periods before and after the introduction of STF. However, the analysis indicated that the impact of STF on revenue from casual users before and after STF at jurisdiction-level was inconclusive. As would be expected, the launch of STF, which is a casual fare product, did not impact ridership and revenue of monthly and annual registered members. Sensitivity analysis of ridership to rail transit disruptions due to SafeTrack indicated that there was a statistically significant increase in ridership by registered members and casual users at the CaBi stations affected by SafeTrack. The concurrency of STF launch with SafeTrack may have played a role in this increase. For, the new fare product created an opportunity for commuters to try CaBi as an alternative travel mode at an affordable price that is compatible with transit fare during transit service disruptions. However, the analysis did not present any evidence on the sustained nature of CaBi ridership increase attributable to SafeTrack. The methods used in this research are helpful for bikeshare operators to model changes in ridership and revenue from attributable to pricing structure.

Assessment of emission reduction potential of Mumbai metro rail

Abstract First line of Mumbai metro rail system was implemented in 2014 for reduction in traffic congestion and to provide a better connectivity between eastern and western suburbs of Mumbai metropolitan region (MMR). Apart from connectivity, reduction in greenhouse gases (GHG), gaseous pollutants like carbon monoxide (CO), hydrocarbon (HC), oxides of nitrogen (NOx), particulate matter (PM) were the main goal behind implementation of metro. In this study, we analyse the reduction in carbon dioxide (CO2) and other emissions due to shift of commuters to metro from other mode of transportation. In addition, monetary savings in travel time is also determined as travel time is the main attraction for using metro. The emissions were estimated through modal shift of passengers by converting the ridership data to equivalent number of vehicles on road. Our analysis shows modal shift resulted in reduction of 22.7 tonne of CO2 emission per day but ∼75.6 tonne of CO2 is emitted per day from the electricity consumed in metro operation. However, pollutants like CO, HC were reduced by metro and high savings in value of travel time was obtained. Overall, the results illustrate that so far the metro rail is yet to achieve reduction in greenhouse gases (GHG) emissions since a major shift occurred from public transportation. However, with increase in ridership metro rail can achieve the benefits in reduction of emissions.

Using an ARIMA-GARCH Modeling Approach to Improve Subway Short-Term Ridership Forecasting Accounting for Dynamic Volatility

Subway short-term ridership forecasting plays an important role in intelligent transportation systems. However, limited efforts have been made to forecast the subway short-term ridership, accounting for dynamic volatility. The traditional forecasting methods can only provide point values that are unable to offer enough information on the volatility/uncertainty of the forecasting results. To fill this gap, the aim of this paper is to incorporate the dynamic volatility into the subway short-term ridership forecasting process that not only generates the expected value of the short-term ridership but also obtains the prediction interval. Four kinds of the integrated ARIMA and GARCH models are constructed to model the mean part and volatility part of the short-term ridership. The performance of the proposed method is investigated with the real subway short-term ridership data from three stations in Beijing. The model results show that the proposed model outperforms the traditional model for all three stations. The hybrid model can significantly improving the reliability of the predicted point value by reducing the mean prediction interval length of the ridership, and improve the prediction interval coverage probability. Considering the different traffic patterns between weekday and weekend, the short-term ridership is also modeled, respectively. This paper can help management understand the dynamic volatility of the subway short-term ridership, and have the potential to disseminate more reliable subway information to travelers through the information systems.

Using crowdsourced data to monitor change in spatial patterns of bicycle ridership

Abstract Cycling is a sustainable mode of transportation with numerous health, environmental and social benefits. Investments in cycling specific infrastructure are being made with the goal of increasing ridership and population health benefits. New infrastructure has the potential to impact the upgraded corridor as well as nearby street segments and cycling patterns across the city. Evaluation of the impact of new infrastructure is often limited to manual or automated counts of cyclists before and after construction, or to aggregate statistics for a large region. Due to methodological limitations and a lack of data, few spatially explicit approaches have been applied to evaluate how patterns of ridership change following investment in cycling infrastructure. Our goal is to demonstrate spatial analysis methods that can be applied to emerging sources of crowdsourced cycling data to monitor changes in the spatial-temporal distribution of cyclists across a city. Specifically, we use crowdsourced ridership data from Strava to examine changes in the spatial-temporal distribution of cyclists in Ottawa-Gatineau, Canada, using local indicators of spatial autocorrelation. Strava samples of bicyclists were correlated with automated counts at 11 locations and correlations ranged for 0.76 to 0.96. Using a local indicator of spatial autocorrelation, implemented on a network, we applied a threshold of change to separate noise from patterns of change that are unexpected given a null hypothesis that processes are random. Our results indicate that the installation or temporary closure of cycling infrastructure can be detected in patterns of Strava sample bicyclists and changes in one location impact flow and relative volume of cyclists at multiple locations in the city. City planners, public health professionals, and researchers can use spatial patterns of Strava sampled bicyclists to monitor city-wide changes in ridership patterns following investment in cycling infrastructure or other transportation network change.

Assessment of metro ridership fluctuation caused by weather conditions in Asian context: Using archived weather and ridership data in Nanjing

This paper aims to assess metro ridership fluctuation caused by weather conditions in an Asian context (Nanjing, China) on metro single line and network levels. The daily ridership data utilized in the study is obtained from Nanjing Metro Agency, while meteorological data is from the Nanjing Meteorology Bureau. To address the main research questions of this paper, time-serials moving average method is extended and refined to define benchmark and to model ridership fluctuation (expressed as ridership residual). On the other hand, analyses of variance (ANOVAs) are introduced to examine meteorological events and temporal effects on ridership residuals. The data and result demonstrates that the impact of meteorological variables on daily ridership fluctuations generally results in a decrease in passengers on single lines and the travel network, especially on weekends. Precipitation related events produce more significant ridership fluctuations than temperature related events. Snowfall related events and large temperature deviations in winter account for the most dramatic changes in ridership. However, seasonality is not a significant factor in meteorological events-ridership residual relationship. This paper contributes to extending research on the weather-ridership relationship around the world especially to refining and extending the nine-term moving average method in this field. This could benefit metro operators by enabling them to add meteorological effects into their ridership prediction and budgeting work.

Estimation of Pedestrian Origin-Destination Demand in Train Stations

We present a framework for estimating pedestrian demand within a train station. It takes into account ridership data, and various direct and indirect indicators of demand. Such indicators may include link flow counts, density measurements, survey data, historical, or other information. The problem is considered in discrete time and at the aggregate level, i.e., for groups of pedestrians associated with the same origin-destination pair and departure time interval. The formulation is probabilistic, allowing to consider the stochasticity of demand. A key element is the use of the train timetable, and in particular of train arrival times, to capture demand peaks. A case study analysis of a Swiss train station underlines the practical applicability of the proposed framework. Compared to a classical estimator that ignores the notion of a train timetable, the gain in accuracy in terms of root-mean-square error is between 20% and 50%. More importantly, the incorporation of the train schedule allows for prediction when little or no data besides the timetable and ridership information is available. The online appendix is available at https://doi.org/10.1287/trsc.2016.0723 .

Modeling Transit Bus Emissions Using MOVES : Comparison of Default Distributions and Embedded Drive Cycles with Local Data

This study focuses on the comparison of operating mode distributions and other assumptions used in the estimation of transit bus emissions with the motor vehicle emission simulator (MOVES). The study area is the city of Montreal, Canada, where a single transit provider operates bus service along 220 routes. For this purpose, instantaneous speeds and passenger ridership data were collected onboard a total of 96 buses during the summer and fall of 2013. The data collection campaign covered eight bus routes in Montreal. The selected routes serve a range of corridor types capturing a variability in land use, road geometry, traffic flow, bus type, and transit service. Ultimately, the authors analyzed data from 3,702 road segments amounting to approximately 975.5 km (606 mi) with bus service. Significant differences between locally derived operating mode distributions and MOVES2014 default distributions were observed. The MOVES distributions assume a significantly larger portion of idling than that obtained from local data. The authors also investigated the drive cycle characteristics of different bus types and observed differences between standard and articulated buses, which are currently unaccounted for by MOVES. The findings illustrate the importance of collecting local bus data when estimating transit emissions.

Bike share in Fargo, North Dakota: Keys to success and factors affecting ridership

The growing popularity of bike share programs in the United States has prompted many cities to implement bike share systems to enhance mobility and health in their communities. While many of these programs have been introduced in large cities, and existing research has tended to focus on these large systems, bike share programs are becoming increasingly popular in smaller cities as well. Great Rides Bike Share launched with 11 stations and 101 bikes in 2015 in Fargo, North Dakota. This is one of the smaller systems in the United States, but it has been very successful. This study examines the ridership data for Great Rides Bike Share during its first two years of operations, investigates it keys to success, and estimates impacts of weather, temporal, and spatial variables on bike share use. In terms of trips per bike per day, bike share usage in Fargo surpasses that of the largest programs in the country. Keys to its success were the presence of a college campus and the reduced barriers to use for college students. The ridership model showed that temperatures, wind, precipitation, and the location of stations on a college campus all have significant impacts on bikes share use.

Enhancing Beijing\u2019s Resilience by Improving Tongzhou\u2019s Access to High-Speed Rail Transportation

The relocation of 400,000 Beijing municipal employees to Tongzhou promises to reduce congestion and pollution in the city center. Further, the relocation could facilitate economic cooperation and integration of the Beijing–Tianjin–Hebei megaregion. Together, these initiatives can help make Beijing and Northern China more resilient, though critical issues of the connectivity of Tongzhou to the rest of Beijing and the megaregion need to be addressed. This paper focuses on two questions related to the success of these efforts: (1) How many additional commuters can be expected to use the existing subway stations in Tongzhou as a result of the relocation; and (2) How can Tongzhou be better connected to the megaregion’s high-speed rail network and a major airport. To answer the first question, a GIS-based model was used to analyze subway ridership data to estimate changes in rush hour commuters at Tongzhou’s subway stations under three scenarios. The results estimate substantial increases in commuters unless large proportions of the workers move their residences to Tongzhou. To answer the second question, a Moving Platform Infrastructure Network (MPIN) was conceptualized as an innovative alternative to provide needed access to high-speed rail and a major airport. The resulting MPIN provides fast and environmentally sensitive connections, while potentially reducing congestion at the Beijing South railway station. In addition, the MPIN concept could be used in other parts of the megaregion.

Data-Driven Spatial Modeling for Quantifying Networkwide Resilience in the Aftermath of Hurricanes Irene and Sandy

In recent years, the New York City metropolitan area was hit by two major hurricanes, Irene and Sandy. These extreme weather events disrupted and devastated the transportation infrastructure, including road and subway networks. As an extension of the authors’ recent research on this topic, this study explored the spatial patterns of infrastructure resilience in New York City with the use of taxi and subway ridership data. Neighborhood tabulation areas were used as the units of analysis. The recovery curve of each neighborhood tabulation area was modeled with the logistic function to quantify the resilience of road and subway systems. Moran's I tests confirmed the spatial correlation of recovery patterns for taxi and subway ridership. To account for this spatial correlation, citywide spatial models were estimated and found to outperform linear models. Factors such as the percentage of area influenced by storm surges, the distance to the coast, and the average elevation are found to affect the infrastructure resilience. The findings in this study provide insights into the vulnerability of transportation networks and can be used for more efficient emergency planning and management.

The Hangover: Assessing Impact of Major Service Interruptions on Urban Rail Transit Ridership

Driven by several factors, transit ridership has increased dramatically in some major U.S. urban areas over the past several years. Developing accurate econometric models of system ridership growth will help transit agencies plan for future capacity. As major weather events and maintenance issues can affect transit systems and have large impacts on the trajectory of ridership growth, this study examined the effect of major and minor service interruptions on the PATH heavy rail transit system in northern New Jersey and New York City. The study, which used PATH ridership data as well as data on weather, economic conditions, and fares for both PATH and competing services, concluded that Hurricane Sandy likely dampened ridership gains. Other major service interruptions, which lasted only hours or days, had little effect on long-term ridership growth. Suggestions for further study of service interruptions, especially in the face of climate change and resiliency issues in coastal regions, are presented.

The influence of slope on walking activity and the pedestrian modal share

Abstract The aim of this paper is to research the relation between the slope of the terrain and walking activity, an area that has so far been little researched in transportation science. With the use of pedestrian counts from a busy and steeply sloped street as well as public transport ridership data, the effect of the inclination on the pedestrian modal share is researched. The results show a significant influence of slope on walking attractiveness, which is quantified by representing the walking modal share with a logit model. The model parameters acquired by maximum likelihood estimation suggest that a 1% increase in slope makes a walk roughly 10% less attractive. This value corresponds well with the small number of literature sources that exist on the same topic. The results were obtained with “live” data, acquired in the built environment rather than in a controlled experimental setting. Since this means that the exact origins and destinations or trip purposes of travelers cannot be fully known, the authors express reservation in interpreting the results.

Highway Vehicle Emissions Avoided by Diesel Passenger Rail Service Based on Real-World Data

Avoided emissions attributable to the reduction in personal automobile trips for passenger rail riders are quantified based on real-world measurements. The North Carolina Department of Transportation (NCDOT) sponsors the Piedmont passenger rail service between Raleigh and Charlotte, NC. Per passenger-kilometer locomotive emissions were quantified based on portable emissions measurement system measured exhaust concentrations and duty cycles, or the fraction of trip time spent in each throttle notch setting of the prime mover engine, from 68 one-way trips of six Tier 0+ and Tier 1+ locomotives, and actual ridership data. Motor Vehicle Emissions Simulator (MOVES) software was used to estimate light-duty gasoline vehicle (LDGV) emission factors. Moving a passenger from an LDGV to a Piedmont train would lead to a net reduction in carbon dioxide (CO2) and carbon monoxide (CO) emissions by 44–94 %, respectively, between Raleigh and Charlotte, based on the assumption that the driver is the only LDGV passenger. However, locomotive nitrogen oxides (NO x ), hydrocarbons (HC), and particulate matter (PM) emission factors were 4–11 times higher than for the LDGV, respectively. Delays for either the train or highway vehicles did not substantially alter the key findings. If a Tier 4 locomotive was used, NO x , PM, and HC emission rates would be 90–99 % lower than current NCDOT locomotives. The use of real-world data representative of actual train operations provides an accurate basis for comparing rail and personal vehicle energy use and emissions and for identifying key factors affecting variability in the comparison.

Bus commuting, subway commuting, and walking to workplaces in US cities: Socioeconomic factors of transit commuters

ABSTRACTMost previous studies that examine the controversies involved in transit subsidies concentrate on large metropolitan areas in the United States. The main contribution of this article to the extant literature is that it investigates bus ridership data in small- and medium-sized US cities, which has been primarily ignored in earlier literature. The article also analyzes data on bus commuters, subway commuters, and walkers to workplaces in the eight largest cities with extensive subway systems and 22 other large cities. The analysis employs the 2010 US Census (IPUMS data). Generally, the characteristics of subway commuters are quite different from those of bus commuters. The characteristics of walkers in all the US cities are very similar to those who commute by bus.