Automatic Fare Collection Data Research Articles

Mining Multimodal Travel Mobilities with Big Ridership Data: Comparative Analysis of Subways and Taxis

Understanding traveler mobility in cities is significant for urban planning and traffic management. However, most traditional studies have focused on travel mobility in a single traffic mode. Only limited studies have focused on the travel mobility associated with multimodal transportation. Subways are considered a green travel mode with large capacity, while taxis are an energy-consuming travel mode that provides a personalized service. Exploring the relationship between subway mobility and taxi mobility is conducive to building a sustainable multimodal transportation system, such as one with mobility as a service (MaaS). In this study, we propose a framework for comparatively analyzing the travel mobilities associated with subways and taxis. Firstly, we divided taxi trips into three groups: competitive, cooperative, and complementary. Voronoi diagrams based on subway stations were introduced to divide regions. An entropy index was adopted to measure the mix of taxi trips. Secondly, subway and taxi trip networks were constructed based on the divided regions. The framework was tested based on the automatic fare collection (AFC) data and global positioning system (GPS) data of a subway in Beijing, China. The results showed that the proportions of taxi competition, taxi cooperation, and taxi complements were 9.1%, 35.6%, and 55.3%, respectively. The entropy was large in the central city and small in the suburbs. Moreover, it was found that the subway trip network was connected more closely than the taxi network. However, the unbalanced condition of taxis is more serious than that of the subway.

The impacts of COVID-19 on route choice with guidance information in urban rail transit of megacities

The outbreak of COVID-19 has caused unprecedented decline of ridership in urban rail transit and changed passenger travel habits, which greatly challenges subway operations. Therefore, it is necessary to better understand and quantify the impact of COVID-19 on passenger travel behavior, specifically route choice. Thus, we collected automatic fare collection data and 2060 random samples through a web-based survey in Beijing on passengers’ route choice behavior during the COVID-19 pandemic. This study utilizes an initial dataset to conduct an analysis and introduces an improved Generalized Random Regret Minimization model (GRRM) aimed at understanding passengers' route choice adjustments in response to COVID-19 guidance information. This improved GRRM accounts for two decision-making criteria, namely, maximum utility and minimum regret, and considers passenger heterogeneity. This marks the first instance of capturing the heterogeneity shift effect in route choice perception during the COVID-19 pandemic. The results show that the improved model has the best fitting result with adjusted Rho square of 0.536, demonstrating that the attributes related to guidance information (i.e., information push/time to receive traffic information/perceived route COVID-19 risk) indeed enhance the model’s fit. Furthermore, the research employs Value of Information Time to quantify the preference of passengers for information in various groups. Compared with the usual scenario, women, young and non-commuter passengers are more likely to receive an early information update to plan their trips in advance. Finally, the perceived risk of COVID-19 on routes is examined in relation to passengers’ personal attributes. It is observed that the elderly and students exhibit heightened sensitivity to the epidemic at all stages, while young passengers and commuters are particularly sensitive only during the small-scale epidemic. These findings offer valuable insights for managers to implement targeted strategies, thereby enhancing passenger flow control and encouraging increased subway ridership.

An empirical study on the stochastic long-term travel demands of a large-scale metro network

The widely-existed uncertainty of origin–destination (OD) demand in transportation networks has attracted extensive attention. Most characterizations or modeling of stochastic OD demands in networks assume a homogenous probability distribution, but it lacks empirical studies of large-scale networks to justify this assumption. Given that the long-term continuous automatic fare collection data of metro networks can provide complete OD passenger demand information, this study takes the Shanghai Metro network as an example to empirically examine the stochasticity characteristics of OD passenger demands of metro network. Based on the morning peak OD demand data for 250 weekdays, a local outlier factor method is used to identify and remove outliers in the data. A clustering method is used to cluster the OD pairs, study the fluctuation and distribution characteristics of the OD passenger demands, and select the optimal distribution type through goodness-of-fit indices. The results show that 1) the coefficients of variation of morning peak OD demands in the network are mainly distributed in the range of 0.2–0.6, and different OD pairs have different fluctuations, and the degree of demand fluctuation decreases as the mean increases; 2) the probability distribution types of OD demands based on statistical characteristics are heterogeneous; and 3) the optimal distribution type of OD demands is Poisson, Lognormal/Gamma, and Normal for OD pairs characterized by a small mean and right-skewness, a small mean and skewness close to 0, and a large mean, respectively. In contrast to the simple average-based data processing of OD passenger demand in metro networks, this paper presents a new perspective of mining long-term continuous data to understand the inherent stochasticity of OD passenger demands. The results can provide more realistic and practical inputs and assumptions for theoretical research on stochastic OD demands in metro networks.

Public transport route choice modelling: Reducing estimation bias when using smart card data

Automated Fare Collection (AFC) data for public transport analyses has received much research interest recently, including its use for the estimation of passenger route choice preferences. However, an important problem persists since AFC data only includes information about the trip within the public transport system, that is stop-to-stop (tap-in to tap-out). Not knowing the full trip from door-to-door might lead to estimation bias, especially when estimating route choice models based on only the chosen stops, which is common practice in current research using AFC data. To avoid this, we propose an improved method for estimating route choice models in public transport using AFC data. The method is based on randomly generating pseudo origin (and destination) points in close vicinity of the actually chosen origin (and destination) stops, thus allowing pseudo access and egress times to be incorporated into the route choice model. The framework is compatible with any probability density function. We suggest using the Beta distribution for generating points when knowledge about access and egress distances are available, whereas the Uniform distribution is suggested when no knowledge is available. The method was applied on replicated AFC data based on traditional travel survey data from the Greater Copenhagen area in Denmark. The results of the model estimations confirm estimation bias in parameter estimates when not correcting for the lack of access/egress information. The proposed method notably improves in-vehicle-time parameter estimates of the route choice model compared to estimation assuming AFC stop-to-stop data, whereas access/egress time and hidden waiting time parameters are still biased, although to a lesser extent than a traditional naïve estimation based on stop-to-stop data.

Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China

The development of the automatic fare collection (AFC) systems provides significant support for predicting passenger flow on urban rail transit. This paper extracts passenger travel patterns using AFC data on urban rail transit in Chengdu, China, over a one-month period. Passengers are divided into two categories based on their travel habits and data mining models, and multinomial logit (MNL) models are separately used to predict their destinations. Furthermore, a two-way search algorithm is developed to search the optimal paths between origin-destination (OD) pairs by considering interchange constraints. Start a path search through the origin point and destination point, respectively, until the shortest path is found. The maximum effectiveness of a path is measured by travel time, interchange time, and the number of interchanges between the OD pairs. Finally, the validity of the proposed passenger flow path prediction method is verified by using the AFC data of Chengdu metropolitan rail transit from April 2018.

Data-Driven Real-Time Denied Boarding Prediction in Urban Railway Systems

Providing real-time crowding information in urban railways would enable informed travel decisions and encourage cooperative behavior of passengers, as well as improve operating efficiency and safety. However, the problem of real-time crowding prediction is not trivial because of the unavailability of ground-truth crowding data, particularly for the direct impact of crowding on passengers (e.g., denied boarding on platforms). This paper proposes a data-driven method for real-time denied boarding prediction in urban railway systems using automated fare collection (AFC) and automated vehicle location (AVL) data. It predicts the denied boarding probability distribution or its derived metrics as a function of explanatory variables, including demand, operations, and incident-related factors. The method is validated through a case study covering 18 months on Hong Kong Mass Transit Railways. The results highlight the model’s accurate and robust performance in predicting denied boarding on platforms using purely AFC and AVL data (e.g., an average of 6%–7% error) under both recurrent and non-recurrent situations, in which transfer demand-related factors contribute most to the prediction. The prediction model outputs can support proactive operations control and management, as well as provision of customer information without relying on expensive and time-consuming data collection efforts. Customer information, for example, can include the average number of trains to wait before boarding or the average waiting time on the platform.

Data-driven identification and analysis of passenger riding paths in megacity metro system

Purpose The paper aims to identify and analyze passengers’ riding paths for providing better operational support for digital transformation in megacity metro systems.Design/methodology/approach The authors develop a method to leverage certain passengers’ deterministic riding paths to corroborate other passengers’ uncertain paths. Using Automatic Fare Collection data and train schedules, a witness model is built to recover the actual riding paths for passengers whose paths are unknown otherwise. The identification and analysis of passenger riding paths between three different types of origin–destination) pairs reveal the complexity of passenger path choice.Findings The results show that passenger path choice modeling is usually characterized by complexity, experience and partial blindness. Some passengers choose paths that are not optimal due to their experience and limited access to overall metro system information. These passengers could be the subject of improved path guidance in light of riding efficiency improved through digital transformation.Originality/value This research contributes to the improvement of metro management and operations by leveraging ongoing digital transformation in megacity metro systems. Based on the riding paths and trip chains of a large number of individual passengers identified by the proposed method, metro operation management could prevent risks in areas with concentrated passenger flow in advance, optimally adjust train schedules on a daily basis and deliver real-time riding guidance station by station, which would greatly improve megacity metro systems’ service safety, quality and operational efficacy over time.

The equity of public transport crowding exposure

Public transport crowding exposure is known to cause discomfort, stress and dissatisfaction. However, the distribution and equity of crowding exposure across socioeconomic groups has been largely unexplored. This paper opens a new research topic connecting crowding exposure in public transport to travelers’ socioeconomic characteristics. We present a framework for assessing the equity of in-vehicle crowding exposure based on automatic data sources. Two metrics are considered for quantifying the travelers’ in-vehicle crowding exposure: (1) the excess perceived travel time and (2) the relative excess perceived travel time. The proposed methodology computes the two metrics based on travel diaries and in-vehicle loads inferred from automated fare collection data. We implement Lorenz curves, Gini and Suits coefficients to evaluate horizontal (across the population) and vertical equity (considering income as well as mobility ability and need). The vertical equity is further discussed using clusters of socioeconomic groups and results from spatial lag regression models to assess the distribution of crowding exposure across socioeconomic characteristics. The results for the Stockholm Region case study indicate that crowding exposure varies substantially across the service area, with the highest values found in the denser urban areas close to Stockholm City. We find that the distribution across socioeconomic groups is relatively even, but travelers from areas that are wealthier, higher educated, have higher share of rental housing or lower vehicle ownership areas tend to be exposed to more crowding. The paper provides tools to support public transport planners in decision-making, showing where to intervene to reduce crowding exposure efficiently to achieve urban equity and sustainability.

Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems

Public transport, especially bus and metro, are fundamental elements of sustainable transport systems. However, a dearth of research has been devoted to exploring the correlation between the built environment and the intermodal transfer modes that link bus and metro. To address this research gap, this study aims to explore the relationship between the built environment and transfer ridership by examining transfer ridership across different modes. First, this study uses Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) data collected in the city of Chengdu to identify the ridership of Metro-to-Bus (M-B) and Bus-to-Metro (B-M) transfer passengers using dynamic transfer time thresholds. A multi-scale geographically weighted regression model (MGWR) is employed to examine the impact of the built environment on M-B and B-M transfer modes and their scale effects. The findings demonstrate that the MGWR model is effective in capturing the spatial heterogeneity and scale effects of the interrelationships between different built environment factors in the M-B and B-M modes. Furthermore, the impact of different built environment factors on transfer ridership varies. In particular, the number of bus stops and lines have a more pronounced positive effect on promoting transfer ridership, while the density of non-motorway lanes has a significant negative effect. This research provides valuable insights for public transportation management and supports the seamless integration of bus and metro systems to optimize transfer services.

Dynamic Modeling for Metro Passenger Flows on Congested Transfer Routes

With the rapid development of urbanization, the metro becomes more and more important for people’s travel in big cities. To quantitatively describe metro passenger flows on congested transfer routes, this paper introduces a dynamic model based on automated data from the automatic fare collection (AFC) and automatic vehicle location (AVL) systems. An expectation maximization (EM) algorithm is proposed to compute the maximum likelihood estimates of unknown parameters in our model. Our model can yield a systematic analysis of one-transfer passenger flows on both population and individual aspects. Important characteristics, including transfer time, boarding probabilities, walking time, passenger-to-train assignment probabilities, and total travel time, can be inferred using only the AFC and AVL data. We provide a case study on the Beijing metro. Detailed analysis results based on our model are given. We also present a cross-validation method to validate our model with real data.

Network-Wide Link Travel Time and Station Waiting Time Estimation Using Automatic Fare Collection Data: A Computational Graph Approach

Urban rail transit (URT) system plays a dominating role in many megacities like Beijing and Hong Kong. Due to its important role and complex nature, it is always in great need for public agencies to better understand the performance of the URT system. This paper focuses on an essential and hard problem to estimate the network-wide link travel time and station waiting time using the automatic fare collection (AFC) data in the URT system, which is beneficial to better understanding the system-wide real-time operation state. The emerging data-driven techniques, such as the computational graph (CG) method in the machine learning field, provide a new solution for solving this problem. In this study, we first formulate a data-driven estimation optimization framework to estimate the link travel time and station waiting time. Then, we cast the estimation optimization model into a CG-based framework to solve the optimization problem and obtain the estimation results. The methodology is verified on a synthetic URT network and applied to a real-world URT network using the synthetic and real-world AFC data, respectively. Results show the robustness and effectiveness of the CG-based framework. To the best of our knowledge, this is the first time that the CG is applied to the URT. This study can provide critical insights to better understand the operational state of URT.

Synergistic Optimization Method for URT Network Train Connection Scheme in Peak and Off-Peak Periods

In this study, we developed a method for coordinating and optimizing the train connection plans of different lines under the conditions of urban rail transit (URT) network operation. The method allows trains of different lines to form good connections at transfer stations, which can shorten the waiting time of passengers for transfers and reduce passenger retention. A mathematical model was developed to simulate the interaction between passengers and trains. Two optimization models were developed for the train connection plan of network transfer stations based on different optimization objectives during peak and off-peak hours. Subsequently, a corresponding solution method based on a genetic algorithm and simulation was designed. Finally, the Suzhou URT network was used as a case study, and the passenger flow of the transfer station was simulated and calculated using relevant automatic fare collection (AFC) data. The results indicated that the average waiting time and the number of passengers stranded were reduced using the proposed method. The calculation example demonstrated the effectiveness of the model and algorithm, which can guide the coordinated preparation of a network train connection plan.

Ex Post Path Choice Estimation for Urban Rail Systems Using Smart Card Data: An Aggregated Time-Space Hypernetwork Approach

This paper proposes an ex post path choice estimation framework for urban rail systems using an aggregated time-space hypernetwork approach. We aim to infer the actual passenger flow distribution in an urban rail system for any historical day using the observed automated fare collection (AFC) data. By incorporating a schedule-based dynamic transit network loading (SDTNL) model, the framework captures the crowding correlation among stations and the interaction between the path choice and passenger left behind, which is important for the path choice estimation in a “near-capacity” operated urban rail system. The path choice estimation is formulated as an optimization problem, which aims to minimize the difference between the model-derived and observed information with path choice parameters as decision variables. The original problem is intractable because of nonlinear (logit model) and nonanalytical (SDTNL) constraints. A solution procedure is proposed to decompose the original problem into three tractable subproblems, which can be solved efficiently. Solving the decomposed problem is equivalent to finding a fixed point. We prove that the solution to the original problem is the same as the decomposed problem (i.e., the fixed point) when passenger path choices follow the predefined behavior model. If this condition does not hold, the solution of the original problem is proved to be an “almost fixed point” for the decomposed problem. The model is validated using both synthetic and real-world AFC data from a major urban railway system. The analysis with synthetic data validates the model’s effectiveness in estimating path choice parameters and left behind probabilities, which outperforms state-of-art simulation-based optimization methods and probabilistic models in both accuracy and efficiency. The analysis using actual data shows that the estimated path shares are more reasonable than the baseline uniform path shares and survey-derived path shares. The model estimation is robust to different initial parameter values and AFC data from various dates. Funding: The project is partially funded by the TRENoP Strategic Research funding at KTH. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1177 .

RAILDASH: A DASHBOARD SYSTEM TO ANALYZE EFFECTS OF EVENTS ON RAILWAY TRAFFIC USING BIG GPS DATA

Abstract. The big events cause substantial deviations in the city traffic, especially railway transportation which happens to be the dominant mode of transportation for most of the major cities. Analysis of big events’ impact on railway transportation, therefore, is of great importance to urban planning and transport management, yet is quite challenging because of the lack of readily available data about railway passengers’ citywide flow and event participants’ choice of transportation mode. Previous works have mainly relied on precise but limited data like sensors, AFC (Automated Fare Collection), or smart-card get-on get-off data to estimate the railway passengers, and did not take a holistic multi-dimensional approach to analyze railway traffic congestion caused by the events. To tackle these challenges, we propose a novel interactive Dashboard system that utilizes millions of smartphone GPS records across Japan. The dashboard can estimate and visualize railway passengers for the stations nearby the event venue as well as other relevant event participants’ information. We also introduce a Congestion Index to measure the increase in congestion of stations during events. The dashboard can be highly useful for event organizers, railway administrators, and city planners to assess and compare the impact of big events on railway traffic.

Relationship analysis of short-term origin–destination prediction performance and spatiotemporal characteristics in urban rail transit

Accurately predicting passengers’ origin and destination (OD) demand and analyzing their spatiotemporal characteristics are the key to efficient operation and management of urban rail transit. While obtaining these spatiotemporal characteristics and thus making a short-term OD prediction are a big challenge for a model due to its high dimensionality and uncertainty. This paper proposes a pattern match algorithm based on t-distribution stochastic neighbor embedding and K nearest neighbors (TSNE-KNN) to promote the prediction performance and introduces similarity indicators to explore these features of OD flow and their relationship with the forecasting performance. Analysis of automatic fare collection data of Beijing rail transit shows that the TSNE-KNN model is superior to other state-of-the-art approaches, even including deep neural network models, and the similarity, which is affected by the functional attributes of station, the surrounding land use attributes and the degree of development of the road network, can be a universal indicator to indirectly reflect the time–space properties of the OD flow. It is found that as the similarity of daily OD flows decreases, the performance of short-term OD prediction of decreases, and rail transit stations are gradually shifting from the periphery to the center of the city.

Quantifying MyCiTi supply usage using Big Data and Agent-Based Modelling

Cape Town's Bus Rapid Transit (BRT) system, MyCiTi, uses an Automated Fare Collection (AFC) system that generates large volumes of transactional data on a daily basis. This data can be considered Big Data. The AFC data in its raw format, however, is incapable of supporting supply and demand analysis (e.g. studying bus occupancy rates). Agent-Based Modelling (ABM) can be used to analyse such data for that purpose. This paper discusses the development and calibration of a MATSim-based ABM to analyse AFC data for Cape Town's BRT system. It is shown that data-formatting algorithms are critical in the preparation of data for modelling activities. Furthermore, the development of appropriate ABM calibration parameters requires careful consideration in terms of appropriate data collection, simulation testing, and justification, which are discussed. The paper furthermore shows that the calibrated ABM can generate outputs such as bus on-board volumes, a system-demand overview, and even individual commuter path choice behaviour. Finally, a validation exercise shows that the model developed for this study is able to provide good estimates of on-board bus volumes (R2 = 0.85). It is, however, recommended that further research be conducted into studying agent path choices through simulation.

A deep learning and ensemble learning based architecture for metro passenger flow forecast

AbstractAccurate short‐term forecast of metro outbound passenger flow is of great significance for real‐time traffic control and guidance. A good forecast method should have high accuracy, timeliness and practicality. Based on deep learning and ensemble learning technology, this study proposes an end‐to‐end forecast hybrid architecture for metro outbound passenger flow that integrates multiple passenger flow features. The architecture innovatively integrates bagging ensemble learning strategy and transfer learning with deep learning, and includes multiple extensible passenger flow feature processing components. In addition, this study presents a new coding method to incorporate the operating characteristics of the metro into the forecasting architecture. Use the automatic fare collection (AFC) data of Chengdu Metro Tianfu Square Station for training and verify the forecast architecture on workdays, weekends and holidays. The results reveal that compared with other widely used passenger flow forecast models, the architecture proposed in this study has achieved the highest forecast accuracy in the above‐mentioned different time types. Furthermore, the fusion of transfer learning improves the accuracy of forecast model while significantly speeding up the convergence of training, increasing its timeliness and practicability.

Clustering-Based Travel Pattern for Individual Travel Prediction of Frequent Passengers by Using Transit Smart Card

Individual travel prediction is very important for the construction of intelligent urban transportation systems. Previous studies mainly focus on the improvement of algorithms, but pay little attention to the mining of data information. In this paper, the concept of the travel pattern is introduced into the field of individual travel prediction of frequent bus passengers. The travel pattern of passengers refers to the trip with similar boarding time and similar boarding and alighting stations of the same person. Through clustering the travel pattern by DBSCAN algorithm, the regularity of passenger travel can be better exploited and travel information can be integrated into a unified unit as well. In the process of prediction, we first predict whether the passenger will travel, and then, if so, predict the probability distribution of the next trip conditional on the previous one. The proposed method is tested using the Automatic Fare Collection data of Chengdu’s frequent bus passengers in May 2019. Based on travel pattern, the average accuracy of travel information prediction is about 41%, which is 13% higher than the method without using travel pattern. Furthermore, this paper also discusses the influence of spatial threshold in clustering on the prediction results.

A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger-Train Matching Probability.

The ever-increasing travel demand has brought great challenges to the organization, operation, and management of the subway system. An accurate estimation of passenger flow distribution can help subway operators design corresponding operation plans and strategies scientifically. Although some literature has studied the problem of passenger flow distribution by analyzing the passengers’ path choice behaviors based on AFC (automated fare collection) data, few studies focus on the passenger flow distribution while considering the passenger–train matching probability, which is the key problem of passenger flow distribution. Specifically, the existing methods have not been applied to practical large-scale subway networks due to the computational complexity. To fill this research gap, this paper analyzes the relationship between passenger travel behavior and train operation in the space and time dimension and formulates the passenger–train matching probability by using multi-source data including AFC, train timetables, and network topology. Then, a reverse derivation method, which can reduce the scale of possible train combinations for passengers, is proposed to improve the computational efficiency. Simultaneously, an estimation method of passenger flow distribution is presented based on the passenger–train matching probability. Finally, two sets of experiments, including an accuracy verification experiment based on synthetic data and a comparison experiment based on real data from the Beijing subway, are conducted to verify the effectiveness of the proposed method. The calculation results show that the proposed method has a good accuracy and computational efficiency for a large-scale subway network.

Route choice estimation in rail transit systems using smart card data: handling vehicle schedule and walking time uncertainties

Several cities around the world rely on urban rail transit systems composed of interconnected lines, serving massive numbers of passengers on a daily basis. Accessing the location of passengers is essential to ensure the efficient and safe operation and planning of these systems. However, passenger route choices between origin and destination pairs are variable, depending on the subjective perception of travel and waiting times, required transfers, convenience factors, and on-site vehicle arrivals. This work proposes a robust methodology to estimate passenger route choices based only on automated fare collection data, i.e. without privacy-invasive sensors and monitoring devices. Unlike previous approaches, our method does not require precise train timetable information or prior route choice models, and is robust to unforeseen operational events like malfunctions and delays. Train arrival times are inferred from passenger volume spikes at the exit gates, and the likelihood of eligible routes per passenger estimated based on the alignment between vehicle location and the passenger timings of entrance and exit. Applying this approach to automated fare collection data in Lisbon, we find that while in most cases passengers preferred the route with the least transfers, there were a significant number of cases where the shorter distance was preferred. Our findings are valuable for decision support among rail operators in various aspects such as passenger traffic bottleneck resolution, train allocation and scheduling, and placement of services.