Automatic Fare Collection Data Research Articles

Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit

For urban rail transit, the spatial distribution of passenger flow in holiday usually differs from weekdays. Holiday destination choice behavior analysis is the key to analyze passengers’ destination choice preference and then obtain the OD (origin-destination) distribution of passenger flow. This paper aims to propose a holiday destination choice model based on AFC (automatic fare collection) data of urban rail transit system, which is highly expected to provide theoretic support to holiday travel demand analysis for urban rail transit. First, based on Guangzhou Metro AFC data collected on New Year’s day, the characteristics of holiday destination choice behavior for urban rail transit passengers is analyzed. Second, holiday destination choice models based on MNL (Multinomial Logit) structure are established for each New Year’s days respectively, which takes into account some novel explanatory variables (such as attractiveness of destination). Then, the proposed models are calibrated with AFC data from Guangzhou Metro using WESML (weighted exogenous sample maximum likelihood) estimation and compared with the base models in which attractiveness of destination is not considered. The results show that theρ2values are improved by 0.060, 0.045, and 0.040 for January 1, January 2, and January 3, respectively, with the consideration of destination attractiveness.

Validating Rail Transit Assignment Models with Cluster Analysis and Automatic Fare Collection Data

Passenger flow data are necessary for making and coordinating operational plans for urban rail transit (URT) systems; the availability and the service state of those systems directly influence the activity of a city and its people. Although many transit assignment models have been developed, the results of passenger flows estimated by these models as well as assumptions made in the estimation process, especially for large-scale, complex, and dynamically changing URT networks, had not been validated. This paper proposes a methodology that can validate existing URT assignment models by using automatic fare collection data and a cluster analysis technique. Initial applications to the URT system of Shanghai, China, which is one of the largest in the world, show that the proposed approach works well and can efficiently find the origin–destination pairs in which passengers' route choices are misestimated by those assignment models. The analysis suggests that several factors result in errors (for the URT assignment model used in Shanghai). These factors include the threshold for the difference in travel costs, a misrepresentation of the transferring cost, and inadequate values for the standard deviation. This information is useful for detecting errors in existing URT assignment models, leading to improvements.

Splitting Travel Time Based on AFC Data: Estimating Walking, Waiting, Transfer, and In-Vehicle Travel Times in Metro System

The walking, waiting, transfer, and delayed in-vehicle travel times mainly contribute to route’s travel time reliability in the metro system. The automatic fare collection (AFC) system provides huge amounts of smart card records which can be used to estimate all these times distributions. A new estimation model based on Bayesian inference formulation is proposed in this paper by integrating the probability measurement of the OD pair with only one effective route, in which all kinds of times follow the truncated normal distributions. Then, Markov Chain Monte Carlo method is designed to estimate all parameters endogenously. Finally, based on AFC data in Guangzhou Metro, the estimations show that all parameters can be estimated endogenously and identifiably. Meanwhile, the truncated property of the travel time is significant and the threshold tested by the surveyed data is reliable. Furthermore, the superiority of the proposed model over the existing model in estimation and forecasting accuracy is also demonstrated.

Estimation Method of Path-Selecting Proportion for Urban Rail Transit Based on AFC Data

With the successful application of automatic fare collection (AFC) system in urban rail transit (URT), the information of passengers’ travel time is recorded, which provides the possibility to analyze passengers’ path-selecting by AFC data. In this paper, the distribution characteristics of the components of travel time were analyzed, and an estimation method of path-selecting proportion was proposed. This method made use of single path ODs’ travel time data from AFC system to estimate the distribution parameters of the components of travel time, mainly including entry walking time (ewt), exit walking time (exwt), and transfer walking time (twt). Then, for multipath ODs, the distribution of each path’s travel time could be calculated under the condition of its components’ distributions known. After that, each path’s path-selecting proportion can be estimated. Finally, simulation experiments were designed to verify the estimation method, and the results show that the error rate is less than 2%. Compared with the traditional models of flow assignment, the estimation method can reduce the cost of artificial survey significantly and provide a new way to calculate the path-selecting proportion for URT.

Assessment of Transit Quality of Service with Occupancy Load Factor and Passenger Travel Time Measures

The quality of service (QOS) and resource productivity implications of transit route passenger loading and travel time are investigated. The value of the occupancy load factor is highlighted as a direct measure of QOS measured as passenger comfort. Automatic fare collection data for a premium radial bus route in Brisbane, Australia, are used to investigate the time series correlation between the occupancy load factor and passenger average travel time. This correlation is strong across the entire span of service in both directions. Passengers tend to make longer commuter trips under significantly less comfortable conditions in the peak direction than in the off-peak direction. The Transit Capacity and Quality of Service Manual uses segment-based load factor to determine QOS measured as onboard loading comfort. Additional insight into QOS is provided by relating the route-based dimensions of occupancy load factor and passenger average travel time in a two-dimensional format, from the perspectives of the passenger and the operator. Future research will apply value of time to QOS measurement that will reflect perceived passenger comfort as related to crowding and average time spent onboard. Study results would assist the econometric modeling of transit service quality. The proposed method can be applied in a practical setting where automatic fare collection data are available for fixed-schedule routes. Study outcomes also provide valuable research and development directions.

EVALUATION OF ELDERLY MOBILITY BASED ON TRANSIT CARD DATA IN SEOUL

We analyzed the travel patterns of senior citizens in Seoul using Automatic Fare Collection (AFC) data. We focused specifically on mode choices and transfer patterns. Results showed that 99% of trips made by senior citizens (individuals over 65 years old), who were given free subway transit passes, consisted of single-mode trips. Average travel time was 31 minutes, and subway travel times were longer than bus travel times. Individuals made fewer transfers, took longer metro trips, and paid smaller fares when using their free subway transit cards. They were more negatively sensitive to bus travel time than metro travel time. Encouraging older adult travelers to use transfers that increase costs to a modest extent might help improve travel quality among a group of individuals who find it difficult to enter the metro system or who are uncomfortable making inter-metro transfers. Additionally, as older adults have more time, yet are economically disadvantaged and take more leisure trips, travel improvements could include adopting a time-flexible fare discount. We discuss these improvements in terms of the individual and social benefits afforded to transit passengers in South Korea.

Aggregated Metro Trip Patterns in Urban Areas of Hong Kong: Evidence from Automatic Fare Collection Records

AbstractThe automatic fare collection (AFC) system incorporating smart card technology is widely used in transportation systems for revenue management, but the data from this could be mined for wide-ranging applications. This paper used AFC data of eight metro stations in the central area of Hong Kong to analyze metro trip patterns at an aggregate level. Based on the ridership differences between days, time periods, and directed flows, empirical data were categorized into three groups to better understand the station area characteristics. Compared to previous studies, it was found that factors may play varied roles in determining trip quantities for divergent station areas. Shopping and recreational factors demonstrated a statistically significant relationship with metro trips during the afternoon peak at exit flow and the evening peak at entry flow in the commercial districts of Hong Kong, such as Causeway Bay, Tsim Sha Tsui, and Mong Kok. This suggests that the distinctive context characteristics of the...

Calibrating Rail Transit Assignment Models with Genetic Algorithm and Automated Fare Collection Data

AbstractRecently, automated fare collection (AFC) systems using smart card technology have become the main method for collecting urban rail transit (URT) fares in many cities around the world. Transaction data obtained through these AFC systems contain a large amount of archived information including how passengers use the URT system, and thus can be used in calibrating assignment models for precise passenger flow calculation. We present a methodology for calibrating URT assignment models using AFC data. The calibration approach uses a genetic algorithm‐based framework with nonparametric statistical techniques. Three initial numerical tests show that the proposed approach finds more reasonable solutions than traditional approaches for the calibrated parameters. Furthermore, after calibration by the proposed approach, the existing assignment model delivers more accurate calculations of passenger flows in the network.

Calibration of Rail Transit Assignment Model with Automated Fare Collection Data and a Parallel Genetic Algorithm

In recent years, cities around the world have begun to use automated fare collection (AFC) systems with smart card technologies as the main method of collecting urban rail transit (URT) fares. Transaction data obtained through these AFC systems contain a large amount of archived information about how passengers use the URT system. These data can be used to calibrate assignment models for precise passenger flow calculations. However, this calibration typically is a computationally intensive problem because of multiroute searches, iteration strategies, and especially massive AFC data sets. This paper proposes a methodology for calibrating URT assignment models with AFC data and a parallel genetic algorithm. The calibration approach uses a framework based on a parallel genetic algorithm with nonparametric statistical techniques, which calibrate assignment model parameters by comparing observed and calculated travel time distributions. In initial case studies on the URT network in Beijing, the proposed approach found reasonable solutions for the calibrated parameters.

Monitoring the Performance of the Pedestrian Transfer Function of Train Stations Using Automatic Fare Collection Data

Over the last years all train stations in The Netherlands have been equipped with automatic fare collection gates and/or validators. All public transport passengers use a smart card to pay their fare. In this paper we present a monitor for the performance of the pedestrian function of train stations which is based on data from the automatic fare collection system. To our knowledge this is the first study that uses smart card data in the context of pedestrian behaviour at train stations. To illustrate the added value of the monitor, various applications for a number of train stations are presented.

Impacts of Real-Time Passenger Information Signs in Rail Stations at the Massachusetts Bay Transportation Authority

Real-time information systems have been used in transit agencies around the world to inform passengers better of their estimated wait. In 2012, the Massachusetts Bay Transportation Authority (MBTA) activated new real-time information signage across its heavy rail system. These signs displayed the estimated arrival of the next two trains in each direction. The study reported in this paper examined whether the introduction of realtime arrival signage led to reduced expectations of wait time, improved satisfaction with MBTA, and increased ridership. In-station surveys were conducted before and after real-time information was introduced to gauge changes in passenger satisfaction and wait-time expectations. These expectations were compared against headways collected from automated train tracking data. Ridership changes were measured with automated fare collection data provided by MBTA. Survey results revealed that, after the introduction of the countdown signs, people reduced their overestimation of wait time by 50%. Satisfaction with MBTA did not change significantly as a result of the real-time signage. People reported that they felt more relaxed with real-time signage if the next train arrival occurred within a scheduled headway but less relaxed in cases in which the headways were much greater than scheduled. Minor improvements in ridership were detected in stations with the real-time information after other factors were controlled for, but these results were preliminary. This study suggests that real-time arrival signage would be a positive addition to heavy rail systems to increase passenger comfort and improve perceptions of system performance in a relatively cost-effective manner with the use of existing technologies.

Using cell phone data to measure quality of service and passenger flows of Paris transit system

This paper shows that the particular conditions under which a cellular phone network is operated underground can make it possible to measure passenger flows in an underground transit system. With the help of the mobile network operator Orange, some experiments have been conducted in Paris underground transit system to assess the potential of this new kind of data for transportation studies. The results show that good estimates of dynamic quantities, such as travel times, train occupancy levels and origin–destination flows can be derived from cellular data. The travel times, train occupancy levels and origin–destination flows inferred from cellular data have been compared to direct field observations and Automatic Fare Collection data provided by the STIF (the public transport authority in the Paris metropolitan area). The quantities inferred from cellular data are shown to be consistent with those inferred from the other data sources.

Trip purpose inference using automated fare collection data

In this paper, we exploit the extensive smart card transaction data for deriving useful information about transit passenger behavior, namely trip purpose or activity. We show how the automated fare collection data (e.g., smart card) can be used to infer trip purpose and to reveal travel patterns in an urban area. A case study demonstrates the process of trip purpose inference based on smart card data from Metro Transit in the Minneapolis/St. Paul metropolitan area.

Are Transit Trips Symmetrical in Time and Space?

This study exploits electronic fare collection data to examine the symmetry of boardings and alightings along a transit route. The symmetry of boardings and alightings is arguably the most important concept in the estimation of travel distances such as average trip lengths and passenger miles from data from entry-only fare collection systems. The paper shows the ways such data can be used to examine the symmetry of boardings and alightings through travel patterns in spatial and temporal dimensions. A novel method for aggregating stops, especially for the nearest stops in the opposite direction, is used to compare boardings in one direction with alightings in the opposite direction. Spatially, the method allows examination of the characteristics of boardings and alightings in a spatial dimension. Temporally, the method examines whether a specific and symmetric passenger flow is observed between specific periods (e.g., between morning and afternoon peaks). A case study of the Minneapolis–Saint Paul, Minnesota, region is performed by using automatically collected data from Metro Transit. Automatic fare collection data reveal considerable variation in passenger flow between specific periods. The use of automated passenger-counting data shows this variation to be statistically significant when both temporal and spatial symmetry are examined on an individual day.

Development of a transfer‐cost‐based logit assignment model for the Beijing rail transit network using automated fare collection data

SUMMARYLiterature review indicates that little is known about traveler behavior, such as transfer and route choices, in large transit systems because of the number of alternative routes involved and lack of empirical data. Even though many transit route assignment models have been developed and ample automated fare collection data have been collected, nearly no study has quantified how accurate resulting flow assignments are, especially for transfer flows. However, as a multi‐stakeholder system, it is essential to estimate passenger flows over the Beijing rail transit network for revenue sharing and daily management/operation purpose. In this paper, major factors (including total travel time and transfer cost) that influence passenger flow pattern in the Beijing rail transit network are considered in a logit‐based network flow assignment model. Specifically, a full transfer cost function, including transfer walking time, vehicle waiting time, and a penalty to additional transfers, is proposed to better simulate passengers' transfer behaviors. A generalized cost function for urban rail transit network is presented, and the corresponding route choice behavior of travelers is analyzed. An improved logit‐based model is then presented for solving this network flow assignment problem. The depth‐first method is used to search for “effective paths” among all O–D pairs. The average errors of estimated transfer flows from the proposed assignment model, which is proven to be more realistic in searching a set of effective paths, are below 20%. The results indicate that the models being developed in this study are capable of reasonably reproducing passengers' transfer and route choices and thus helpful for understanding the transfer behaviors of passengers of large rail transit networks. Copyright © 2012 John Wiley & Sons, Ltd.

Strategic Look at Friday Exceptions in Weekday Schedules for Urban Transit

This paper describes a strategic business case for weekday exception scheduling in urban transit services, specifically, treating Fridays differently from other weekdays. As commuters trend toward more flexible work scheduling, telecommuting arrangements, and 4½-day weeks, gaps between midweek and Friday ridership volumes have widened. Exception schedules neither were nor are unusual; transit operators ran full Saturday lunchtime peak-hour service in the interwar era, while private bus companies, airlines, and freight railroads operate exceptions today. Systematic consideration of day-of-week scheduling confirmed that Friday modifications were best leveraged in better matching of service supply to ridership demand. Longitudinal analysis of New York City Transit automated fare collection data revealed that more regular commuters skipped a Friday trip than other weekday trips. Analysis by route and time period for 14 representative routes over 10 months showed 4.7% lower ridership levels on Fridays, potentially allowing 7.4% reductions in vehicle hours operated. Available savings were route specific, with 25% service reductions possible on some routes, but service increases of 25% were required on other routes with higher Friday ridership. Implementing separate Friday schedules systemwide could provide a surplus of $10 to $17 million (0.6%) for reinvestment elsewhere in the network. Additionally, reduced Friday crew requirements could lead to increases of 1.2% to 2.4% in desirable weekend-inclusive regular days off (RDOs) and a 2.4% reduction in nonpreferred midweek RDOs. Two prerequisites for realizing savings are a computerized run-cutting system and an infrastructure for ridership analysis across multiple days, routes, and time periods. Transit agencies should determine whether routes can benefit from weekday exceptions. If productivity improvements are indicated, contracts permitting weekday work program exceptions can be negotiated.

Rail Transit Travel Time Reliability and Estimation of Passenger Route Choice Behavior

Applications of automatic fare collection data were investigated, with a focus on analysis of travel time reliability and estimation of passenger route choice behavior. Beijing Metro was used as a case study. A rail journey was decomposed, and each component was studied with regard to the uncertainties involved. Methods were then designed and validated to infer platform elapsed time (PET) for through stations and platform elapsed time–transfer (PET-Trans) for transfer stations by using smart card transactional data, train schedules, and complementary manual surveys. With this information, the journey time distribution of any path can be established, and methods were proposed for inferring route choice proportions. After data preparation, the methods were applied to two typical origins and destinations from the Beijing Metro. Key values concerning travel time reliability, such as PET, PET-Trans, travelers left behind (unable to board), and path coefficients, were obtained and interpreted in detail. The outcome of this research could facilitate analysis of transit service reliability and passenger flow assignment in daily operations.

Transit Stop-Level Origin–Destination Estimation through Use of Transit Schedule and Automated Data Collection System

As fare and data collection technology has developed, the resolution of collected data has reached the level of the individual traveler in investigations of transit passenger behavior. This paper investigates the use of these data to estimate passenger origins and destinations at the level of individual stops. Because of a lack of information from the fare collection system, researchers still need some estimate of passengers' alighting stops to complete each passenger trip chain on a specific day. Automated fare collection (AFC) and automated vehicle location (AVL) systems are the inputs to the estimation. Instead of typical AVL data, the paper proposes two models to estimate the alighting stop; both consider passenger trip chaining by using AFC data, transit schedule data (Google's General Transit Feed Specification), and automated passenger counter (APC) data. The paper validates the model by comparing the output to APC data with vehicle location data (APC-VL) and performs sensitivity analyses on several parameters in the models. To detect transfer trips, the new models propose a submodel that takes into account the effect of service headway in addition to some typical transfer time thresholds. Another contribution of this study is the relative relaxation of the search in finding the boarding stops, which enables the alternative algorithm to detect and fix possible errors in identification of the boarding stop for a transaction. As a result, the paper provides algorithms for the proposed models and sensitivity analysis for several predefined scenarios. The results are based on data and observed bus passenger behavior in the Minneapolis–Saint Paul, Minnesota, area.

Use of Entry-Only Automatic Fare Collection Data to Estimate Linked Transit Trips in New York City

Many large transit systems use automatic fare collection (AFC) systems. Most AFC systems were designed solely for revenue management, but they contain a wealth of customer use data that can be mined to create inputs to operations planning and demand forecasting models for transportation planning. More detailed information than could ever be collected by any travel survey is potentially available if it is assumed that the transactional data can be processed to produce the desired information. Previous work in this field focused primarily on rail transit, since boardings at fixed stations are easier to locate than boardings of buses, which move around. This paper presents a case study for the Metropolitan Transit Authority's New York City Transit, a transit system in which a rider swipes a fare card only to enter a station or board a bus. This is the first work to include trips by all transit modes in a system that records the transaction only on rider entry, which is significantly more challenging because all the alighting locations need to be inferred and the bus boarding locations need to be estimated. No location information (from automated vehicle location technology or a Global Positioning System) was available for buses. Software that processes the 7 million–plus daily transactions and that creates a data set of linked transit trips was created. The data set can then be analyzed by using geographic information system-based query software to create reports, maps, origin–destination matrices, load profiles, and new data sets. Subway journeys are assigned by using a schedule-based shortest-path algorithm.