Automatic Fare Collection System Research Articles

Coordination of last train transfers using automated fare collection (AFC) system data

SummaryThis paper studies last train coordination problem for metro networks, aiming to maximize the total number of passengers who can reach their destinations by metro prior to the end of operation. The concept of last boarding time is defined as the latest time that passengers can board the last trains and reach final destinations. The corresponding method for calculating last boarding time is also put forward. With automatic fare collection system data, an optimization model for coordinating last trains is proposed. The objective function optimizes the number of passengers who can reach their final destinations during the train period using departure times and headways of last trains for each line as decision variables. Afterwards, an adaptive genetic algorithm is put forward to solve this model and is applied to a case study of the Shanghai metro system. Copyright © 2017 John Wiley & Sons, Ltd.

Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro

This paper contributes to the emerging applications of automatically collected data in revealing the aggregate patterns of passenger flows and monitoring system performance from the passengers’ perspective. The paper’s main objectives are to (1) analyze passenger flow characteristics and (2) evaluate travel time reliability for the Shanghai Metro network by visualizing the automatic fare collection (AFC) data. First, key characteristics of passenger flows are identified by examining three major aspects, namely, spatial distribution of trips over the network, temporal distribution of passenger entries at the line level and station inflow/outflow imbalances. Second, travel time reliability analyses from the users’ perspective are performed, after a new metric of travel time reliability is designed. Comparisons of travel time reliability at the OD level are provided and the network reliabilities across multiple periods are also evaluated. Thus, this paper provides a comprehensive and holistic view of passenger travel experiences. Although the case study focuses on Shanghai Metro, the same analysis framework can be applied to other transit networks equipped with similar AFC systems.

Análise da ocupação veicular para a inserção de beneficiários no transporte coletivo urbano

ResumoEste trabalho apresenta um método capaz de acompanhar e avaliar as taxas de ocupação críticas nos ônibus urbanos nos trechos mais carregados durante a operação. O objetivo é detectar períodos do dia com taxas inferiores às máximas estabelecidas nos contratos de concessão do serviço, para inclusão de beneficiários sem necessidade do pagamento integral da tarifa. O método, baseado em pesquisas de movimentação de passageiros e no tratamento e análise estatística dos dados do Sistema de Bilhetagem Eletrônica – SBE, foi aplicado para o caso de Belo Horizonte-MG. Foi formulado um parâmetro de qualidade, Índice de Conforto Desejável para determinar a capacidade ociosa da frota em operação e quantificar os beneficiários com descontos na tarifa por períodos determinados. A avaliação econômica mostra que em Belo Horizonte, no entrepico, podem ser inseridos mais de 100.000 beneficiários, aproximadamente o total de desempregados na cidade. O estudo demonstrou que investimentos em tecnologias de transportes permitem implantar políticas de inclusão econômica e tornar o ônibus acessível às categorias excluídas, sem provocar alterações no nível de qualidade e desequilíbrios financeiros ao sistema.

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

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

Design of intelligent recognition system based on gait recognition technology in smart transportation

With the rapid development of urban smart transportation, automatic fare collection system becomes more and more important. The related device based on recognition system in the smart transportation is a key equipment of automatic fare ticket system. It is part of the automatic fare collection system and passenger interface. Because of the complexity of the passenger traffic, how the intelligent recognition system can identify the passengers is a formidable challenge. In this paper, we design a sort of intelligent recognition system based on the simplified human gait recognition algorithm. Firstly, we analyze and study the existing identification system. After that we propose the improved and optimized algorithm and design layout of the sensors. And then, according to the motion including action, event and behavior, we propose a simplified method using infrared sensor based on XYT human gait recognition model. This system can effectively reduce the recognition system's judgment and computation time, and improve the accuracy of judgment. Therefore, the proposed system has a certain application value.

Making Headways

This report analyzes the impact of smart card fare payments on transit bus dwell time. Shorter dwell times improve travel speeds and reduce headways, which encourage patronage. First, determinants of dwell time are discussed through a review of existing literature. Data from automatic passenger counters and automatic fare collection systems on Los Angeles, California, Metropolitan Transportation Authority buses are then used to estimate a regression model of dwell time by fare payment type, controlling for other factors, including number of passengers boarding and alighting, wheelchairs, bicycles, bus type and configuration, and service (local, express, etc.). An ordinary least squares regression model estimated with those variables explains 45% of the variance in dwell time. Statistically significant coefficients show that smart card payments contributed roughly 2 s per person to dwell time, while cash or other media contributed about 4 s. Using a smart card to purchase a pass or stored value at the fare box contributed about 8 s. While smart cards contributed less to dwell time than did other forms of fare payment, they were not the largest determinants of dwell times overall. An articulated bus, for example, reduced dwell time compared with conventional buses by 3 s on average and as much as 13 s in crowded conditions; similarly, wheelchair boarding and alighting added as much as 35 s. These findings suggest that smart cards can help reduce delays at stops, but their effects were relatively modest compared with those of other factors known to influence dwell time.

Theoretical Method for Building OD Matrix from AFC Data

The smart cards conquered a serious space in the traffic in the past years. The cards are used like an automatic fare collection system by the different public transport companies. The fare collection is easier, faster and last but not least more efficient with this method. However the generated data are utilizable for other aims apart from the original aim.The origin-destination matrix plays a key role in a transport company's life. We may adapt to the travel demands efficiently in the knowledge of the matrix. Thanks to the proper service the passengers will be satisfied, and they use the service with pleasure. The additional benefit, that the unnecessary capacities can be forced back in the knowledge of the demand, and thereby the expenses of the service provider's decrease. The origin-destination matrix was able to create only with classical counting and interview methods till now. Their disadvantage, that the production consumes considerable workforce and time with a method like this, and hereby the costs are higher. The counting concern to a given time only and the sampling proportion compared to the number of travels of the year is quite slight. Because of that the distortion effect is considerable, the matrix cannot reflect the effects of an intervention. These effects get into the system only after the next counting.The penetration of the smart cards increases, the card user passengers’ number increases continuously. Thanks to that, not only the cost efficiency of the fare collection is growing, but at the same time the statistical sample size too. If more passengers from young persons to older ones accept the usage of the cards, we may receive more accurate picture about the transportation network.If the fare collection system was built up somewhere, the origin-destination matrix can be manufactured with lower expenses, and because of the continuous usage the matrix will be current always, the changes become traceable. We may know the demands in all the moments, the origin-destination matrix may change dynamically in function of the weather, time of the day, season and so on. The check-out data can be manufactured from exclusively check in data with different algorithms. The passenger's boarding and alighting places become known on the given route. Linking the sections belonging to a single trip and defining the target point, the origin destination matrix can be developed.

Use of Mobile Ticketing Data to Estimate an Origin–Destination Matrix for New York City Ferry Service

One of the fundamental components of transit planning is understanding passenger demand, which is commonly represented with origin–destination (O-D) matrices. However, manual collection of detailed O-D information through surveys can be expensive and time-consuming. Moreover, data from automated fare collection systems, such as smart cards, often include only entry information without tracking where passengers exit the transit network. New mobile ticketing systems offer the opportunity to prompt riders about their specific trips when they purchase a ticket, and this information can be used to track O-D patterns during the ticket activation phase. Therefore, the objective of this research is to use back-end mobile ticketing data to generate passenger O-D matrices and compare the outcome with O-D matrices generated with traditional onboard surveys. Iterative proportional fitting was used to create O-D matrices with both mobile ticketing and onboard survey data. These matrices were compared using Euclidean distance calculations. This work was done for the East River Ferry in New York City, and the results show that during peak periods, mobile ticketing data closely match survey data. However, in the off-peak period and during weekends, when travelers are more likely to be noncommuters and tourists, matrices developed from mobile ticketing and survey data have greater differences. The impact of occasional riders making noncommute trips is the likely cause for these differences, because commuters are familiar with using the mobile ticketing product and occasional riders are more likely to use paper tickets on the ferry service.

Passenger Journey Destination Estimation From Automated Fare Collection System Data Using Spatial Validation

A methodology for estimating the destination of passenger journeys from automated fare collection (AFC) system data is described. It proposes new spatial validation features to increase the accuracy of destination inference results and to verify key assumptions present in previous origin–destination estimation literature. The methodology applies to entry-only system configurations combined with distance-based fare structures, and it aims to enhance raw AFC system data with the destination of individual journeys. This paper describes an algorithm developed to implement the methodology and the results from its application to bus service data from Porto. The data relate to an AFC system integrated with an automatic vehicle location system that records a transaction for each passenger boarding a bus, containing attributes regarding the route, the vehicle, and the travel card used, along with the time and the location where the journey began. Some of these are recorded for the purpose of allowing onboard ticket inspection but additionally enable innovative spatial validation features introduced by the methodology. The results led to the conclusion that the methodology is effective for estimating journey destinations at the disaggregate level and identifies false positives reliably.

Unifying Time Reference of Smart Card Data Using Dynamic Time Warping

The wide use of smart card automatic fare collection (AFC) systems in public transit makes it popular to analyse public transit user behaviour based on smart card data. Most smart-card-based researches are time-related, but smart card data time is not always reliable because smart card systems are usually off-line and lack of maintenance in China, and smart card data of different buses often shares different time references. This paper explores the application of dynamic time warping to unify time reference of smart card data. Based on the analysis of the relationship between boarding time in smart card data and arrival-departure time in automatic vehicle location (AVL) data, a smart card data time reference unification algorithm was proposed. The results indicate that the algorithm can work out reliable time offsets by numerical calculation and runs well with integral automatic vehicle location data. Due to the fact that the time reference of smart card data for each bus changes daily, the best way to put an end to smart card time reference problem is to make smart card systems online, such as combining smart card readers with automatic vehicle location systems.

Effects of Two Fare Policies on Public Transport Travel Behaviour: Evidence from South East Queensland, Australia

Congestion and peak loading are key concerns for public transport agencies in Australian cities. Two fare policies, off-peak discounts and volume rebates, have been adopted in South East Queensland (SEQ) attempting to alter passengers' travel behaviour. This research examines how these two fare policies have changed passengers' travel patterns by using newly available smart card data from automated fare collection systems. The findings indicate that current off-peak discounts do not significantly alter passengers' boarding times or encourage peak spreading. Volume rebates, however, do provide incentives to encourage more use of public transport, implying that this could be a way to reduce traffic congestion should most of the additional trips be diverted from other modes. The paper concludes with suggestions for additional, more sophisticated policies to ease congestion and peak loading.

Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model

Although smart-card data were expected to substitute for conventional travel surveys, the reality is that only a few automatic fare collection (AFC) systems can recognize an individual passenger's origin, transfer, and destination stops (or stations). The Seoul metropolitan area is equipped with a system wherein a passenger's entire trajectory can be tracked. Despite this great advantage, the use of smart-card data has a critical limitation wherein the purpose behind a trip is unknown. The present study proposed a rigorous methodology to impute the sequence of activities for each trip chain using a continuous hidden Markov model (CHMM), which belongs to the category of unsupervised machine-learning technologies. Coupled with the spatial and temporal information on trip chains from smart-card data, land-use characteristics were used to train a CHMM. Unlike supervised models that have been mobilized to impute the trip purpose to GPS data, A CHMM does not require an extra survey, such as the prompted-recall survey, in order to obtain labeled data for training. The estimated result of the proposed model yielded plausible activity patterns that are intuitively accountable and consistent with observed activity patterns.

Real-time origin-destination matrices estimation for urban rail transit network based on structural state-space model

The major objective of this work was to establish a structural state-space model to estimate the dynamic origin-destination (O-D) matrices for urban rail transit network, using in- and out-flows at each station from automatic fare collection (AFC) system as the real time observed passenger flow counts. For lacking of measurable passenger flow information, the proposed model employs priori O-D matrices and travel time distribution from historical travel records in AFC system to establish the dynamic system equations. An arriving rate based on travel time distribution is defined to identify the dynamic interrelations between time-varying O-D flows and observed flows, which greatly decreases the computational complexity and improve the model’s applicability for large-scale network. This methodology is tested in a real transit network from Beijing subway network in China through comparing the predicted matrices with the true matrices. Case study results indicate that the proposed model is effective and applicative for estimating dynamic O-D matrices for large-scale rail transit network.

Inferring the route-use patterns of metro passengers based only on travel-time data within a Bayesian framework using a reversible-jump Markov chain Monte Carlo (MCMC) simulation

The passenger share and the average travel time for multiple routes connecting an origin–destination pair on a metro network has been examined based on a known number of used routes. Determining how many routes were used based only on travel times from smart-card data is a difficult task, even though the automatic fare collection system can provide a massive amount of travel data. The present study proposes a robust approach to incorporate the number of used routes as an unknown parameter into a Bayesian framework based on a reversible-jump Markov chain Monte Carlo (MCMC) algorithm. Other route-use patterns such as the passenger share and the mean and variance of route travel times were also estimated. The performance of the present approach was compared with the existing method, which depends on the Bayesian information criterion (BIC). The present approach showed better performance in reproducing the observed number of routes used, and also provided greater flexibility in recognizing route-use patterns through the marginal posterior distribution of other unknown parameters.

Passenger Segmentation Using Smart Card Data

Transit passenger market segmentation enables transit operators to target different classes of transit users for targeted surveys and various operational and strategic planning improvements. However, the existing market segmentation studies in the literature have been generally done using passenger surveys, which have various limitations. The smart card (SC) data from an automated fare collection system facilitate the understanding of the multiday travel pattern of transit passengers and can be used to segment them into identifiable types of similar behaviors and needs. This paper proposes a comprehensive methodology for passenger segmentation solely using SC data. After reconstructing the travel itineraries from SC transactions, this paper adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel pattern of each SC user. An a priori market segmentation approach then segments transit passengers into four identifiable types. The methodology proposed in this paper assists transit operators to understand their passengers and provides them oriented information and services.

Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data

Transit fares are an effective tool for demand management. Transit agencies can raise revenue or relieve overcrowding via fare increases, but they are always confronted with the possibility of heavy ridership losses. Therefore, the outcome of fare changes should be evaluated before implementation. In this work, a methodology was formulated based on elasticity and exhaustive transit card data, and a network approach was proposed to assess the influence of distance-based fare increases on ridership and revenue. The approach was applied to a fare change plan for Beijing Metro. The price elasticities of demand for Beijing Metro at various fare levels and trip distances were tabulated from a stated preference survey. Trip data recorded by an automatic fare collection system was used alongside the topology of the Beijing Metro system to calculate the shortest path lengths between all station pairs, the origin–destination matrix, and trip lengths. Finally, three fare increase alternatives (high, medium, and low) were evaluated in terms of their impact on ridership and revenue. The results demonstrated that smart card data have great potential with regard to fare change evaluation. According to smart card data for a large transit network, the statistical frequency of trip lengths is more highly concentrated than that of the shortest path length. Moreover, the majority of the total trips have a length of around 15km, and these are the most sensitive to fare increases. Specific attention should be paid to this characteristic when developing fare change plans to manage demand or raise revenue.

Development of Simulation Model to Improve Bus Service Reliability at High-Frequency Operation

The quality of service provided to passengers in high-frequency bus routes can be affected by variety of factors. According to this fact that high-frequency bus routes have high passenger demand and short headways (less than 10 minutes), interaction between buses would be much higher, causing decline in reliability of service. In order to represent a busy bus operation in Kuala Lumpur city centre network, a simulation model will be calibrated. The calibration and validation parameters for the key bus route will derive from data captured by the Automatic Vehicle Location (AVL) and Automatic Fare Collection (AFC) system and procedures will be outlined to adapt the model to other bus routes. The study is in its initial stage to find out what factors have highest impact on reliability of high-frequency bus services in the city centre. An initial model was developed for forecasting passenger demand by using RSM. The simulation model will first be used to conduct a sensitivity analysis of the factors influencing reliability, such as passenger demand, terminal departure behaviour, and unfilled trips. Next, several operating strategies, including terminal departure and time-point holding will be modelled and evaluated for their potential to improve reliability. Model results show that which strategies and factors can significantly improve bus service reliability.

Restructuring and modernization of the transport complex of ukraine for profitable work

The article is devoted to the definition of the theoretical and legal bases preceding the introduction on the territory of Ukraine of automated fare collection system on the ground of urban electric and road transport, as first step of a conclusion of a transport complex from the unprofitable and subsidized mode of functioning and need of modification in normative legal acts of Ukraine in connection with its implementation. The main algorithmic solutions regarding the optimal state transition to automated fare system are provided in public transport

Design and Implementation of the Door Module of Gates

This paper describes the structure, function and design difficulties of the door module which is in the rail transit automatic fare collection system, and design and implement a safe and reliable door module.

Analysis of public transit service performance using transit smart card data in Seoul

This study analyzed the performance of transit services using data collected from Automatic Fare Collection (AFC) system in Seoul with respect to service measures such as schedule adherence of metro, occupancy (crowdedness), and the operational speeds of buses. In order to analyze the transit services performances, we developed data-mining logics and applied them to a case study. These logics successfully indicate the route segment with a low Level of Service (LOS) for metro punctuality and bus operational speeds during peak hours (i.e., 7 a.m. to 9 a.m.). Further, a relationship between vehicle occupancy and punctuality on certain transit route segments is easily found based on the logical analysis. These empirical applications are beneficial to transit agencies and planners for improving transit service performances by using massive amount of data.