Smart Card Data Research Articles

Bus Line Shift Behaviour: Evidence of Influential Factors based on Smart Card Data

This paper aims to analyse the factors that influence the behaviour in relation to bus line shift with the support of large-scale smart card datasets, focussing on a case study of the public transport network in Belo Horizonte, Brazil. We developed a binary discrete choice model to assess the factors affecting the changes of the bus line, including the time of day, peak periods, day of the week, number of trips taken by users, type of user, and destination of the trip. We address some research gaps identified in the recent literature by examining factors that influence user behaviour in addition to uncovering urban mobility patterns. The results of a Logit choice model are discussed considering the attributes of the service and of the journey to support evidence-based policy making for adapting bus service design, drawing insights from extensive smart card dataset analysis. Our findings indicate that users are generally inclined to bus line shifts than using the same lines, with such changes occurring more frequently during late hours and inter-peak periods compared to the morning and afternoon peak hours. Additionally, regular users are more likely to change lines than occasional users, and trips with discounts and smart card usage for transfers on trips home tend to involve different lines. Several policy measures can be considered to mitigate passenger discomfort associated with changing bus lines and to reduce operational costs arising from multiple lines serving the same locations. These include investing in promotional efforts to enhance the image of specific bus lines, prioritising certain lines during the day to minimise travel times and retain passengers on the same bus, adjusting line frequencies to reduce the need for transfers, and offering additional incentives to frequent users. Implementing discounts in corridors with shorter travel times can also encourage passengers to remain on the same buses.

An Unsupervised Learning Approach for Robust Denied Boarding Probability Estimation Using Smart Card and Operation Data in Urban Railways

An Unsupervised Learning Approach for Robust Denied Boarding Probability Estimation Using Smart Card and Operation Data in Urban Railways

Understanding passenger route choice behavior under the influence of detailed route information based on smart card data

Understanding passenger route choice behavior under the influence of detailed route information based on smart card data

What Affects Bus Passengers’ Travel Time? A View from the Built Environment and Weather Condition

The study aims to examine the impact of the built environment and weather conditions on travel time for bus passengers in Weinan, China. Various sources of data, including smart card data, bus GPS data, bus station data, road information data, and smart card swiping time, were integrated and analyzed. The study employed the light gradient boosting machine (LightGBM) model and SHapley Additive exPlanations (SHAP) value to assess the feature importance and nonlinear effects of different types of POI density, weather conditions, and time series on bus passengers’ travel time. The study findings indicate that several factors are associated with bus passengers’ travel time, including destination residential density, destination diversity, destination life service density, origin science and education density, origin residential density, origin diversity, humidity, visibility, boarding time between 7 and 8 a.m., and precipitation. This study also reveals nonlinear threshold effects. The study findings provide valuable insights that can be utilized to optimize the bus network and develop low-carbon-oriented land-use planning.

Modeling virus transmission risks in commuting with emerging mobility services: A case study of COVID-19

Commuting is an important part of daily life. With the gradual recovery from COVID-19 and more people returning to work from the office, the transmission of COVID-19 during commuting becomes a concern. Recent emerging mobility services (such as ride-hailing and bike-sharing) further deteriorate the infection risks due to shared vehicles or spaces during travel. Hence, it is important to quantify the infection risks in commuting. This paper proposes a probabilistic framework to estimate the risk of infection during an individual’s commute considering different travel modes, including public transit, ride-share, bike, and walking. The objective is to evaluate the probability of infection as well as the estimation errors (i.e., uncertainty quantification) given the origin-destination (OD), departure time, and travel mode. We first define a general trip planning function to generate trip trajectories and probabilities of choosing different paths according to the OD, departure time, and travel mode. Then, we consider two channels of infections: 1) infection by close contact and 2) infection by touching surfaces. The infection risks are calculated on a trip segment basis. Different sources of data (such as smart card data, travel surveys, and population data) are used to estimate the potential interactions between the individual and the infectious environment. A first-order approximation is used to simplify the computational complexity. We also derive the closed-form formulation for the estimation errors, enabling us to quantify the uncertainty of the estimation results. The model is implemented in the MIT community as a case study. We evaluate the commute infection risks for employees and students. Results show that most of the individuals have an infection probability close to zero. The maximum infection probability is around 1.3%, implying that the probability of getting infected during the commuting process is low. Individuals with larger travel distances, traveling in transit, and traveling during peak hours are more likely to get infected. Practical implementations of the model are also discussed.

Spatiotemporal analysis of CO2 emissions and emission reduction potential of Beijing buses using smart card data

Human activities, primarily carbon dioxide emissions, have undeniably caused global warming. The transportation sector contributes about a quarter of global CO2 emissions. While replacing traditional buses with electric ones has reduced emissions, it is crucial to consider the indirect emissions resulting from electricity consumption. This study proposes a framework for modeling bus emissions using smart card data, integrating spatiotemporal distribution characteristics and emission reduction potentials. Our analysis reveals that routes spanning 10–30 km contribute to 81% of total bus emissions, with an average emission rate of 56.2 gCO2/per-km for residents traveling by bus. Bus emissions also exhibit cyclical variations during holidays, weekdays, and weekends, indicating spatial clustering and trends. Although the area within Beijing's 4th Ring Road constitutes only 13% of the total area within the 6th Ring Road, it generates almost half of the CO2 emissions. With urban expansion, total bus emissions increase gradually, but emission intensity decreases. This study emphasizes the potential for reducing emissions through improved public transportation operations. It recommends fully electrifying the bus fleet and employing low grid emission factors, which could reduce emissions by up to 71% compared to diesel options. Electrification of buses and optimizing power generation on the grid are essential priorities for emission reduction.

Revealing Senior Mobility Patterns and Activities in Urban Transit Systems

Urban mobility plays a crucial role in maintaining seniors’ well-being and quality of life, with urban transit systems serving as their primary mode of travel. This study focuses on revealing seniors’ travel patterns and activities and predicting their demands in transit systems. We propose a spatio-temporal travel pattern model that extends the structural topic model from text mining, to detect senior mobility patterns from trip sequences at the rail station level and discover their activities hidden in passively collected data from a probabilistic generative procedure. The proposed model can incorporate a wide range of spatio-temporal covariates that help estimate the relative importance of travel patterns across stations and over time. Numerical experiments are conducted using transit smart card data collected in Nanjing, China. The results demonstrate that our proposed methodology successfully transforms massive mobility records into informative travel patterns, each of which characterizes a distinct distribution of trip attributes. We combine land use characteristics of station areas with detected patterns to explore seniors’ behavioral strategies and activities at multiple spatial and temporal scales. Moreover, by applying prior detected patterns to the station-level ridership prediction, we significantly improve the predictive performance of direct ridership models. This study contributes to detecting seniors’ travel patterns, discovering latent activities, predicting station-level travel demands, and creating senior-friendly mobility systems.

Estimation of the aggregation degree of public transport use among elderly people based on urban built environment

With the accelerated aging process, the increased willingness of elderly people to travel, and the concessionary bus travel policy implementation, public transport, especially buses, has become one of the important ways for China's elderly people to participate in social activities. Meanwhile, the increased use of public transport by elderly people results in serious conflicts with commuters. In order to understand details about when, where and why public transport trips of the elderly become large, this study investigates the aggregation degree of public transport use among the elderly as well as its determinants mainly from the viewpoint of spatial characteristics. Based on Smart card data and Baidu Map API, the aggregation degree of public transport use in elderly people is rated on a scale of 1 to 5. And then its correlation to the microscopic built environment is analyzed by using negative binomial regression. The results indicate that public transport use by elderly people is highly aggregative in both spatial and temporal dimensions. It is also found that the urban built environment has a significant effect on the aggregation degree of public transport use in the elderly and significant variables for four time periods on both weekdays and weekends are quite different. The proximity of housing estates, elderly activity centers, universities for the elderly, and medical and shopping facilities near bus stations increases the aggregation degree of public transport use by elderly people. This study will provide a reference basis for optimizing the public transport system in the aging society.

Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data

The increasing severity of global climate change has made reductions in carbon emissions an urgent global issue. The relative lack of carbon footprint analyses of urban public transportation systems (UPTS) is therefore surprising, given that UPTS is an important component of urban transportation and one that may play a crucial role in carbon emission reduction. This study conducts a spatio-temporal analysis of carbon footprints for UPTS during the COVID-19 pandemic based on smart card data in Beijing. Since the core of carbon footprint calculation is to estimate travellers' trip trajectories and the ridership of urban rail transit (URT) and buses, we construct a novel multi-layer urban rail network model to calculate passenger volume and travellers' trajectories through a traffic assignment model. Furthermore, we utilize the Generalized Additive Model (GAM) to analyse the correlation relationship between the carbon footprints of buses and URT. Additionally, we conduct statistical analysis of the carbon footprint of UPTS. The results of the spatio-temporal analysis of carbon footprints for UPTS show significantly lower carbon emissions during holidays compared to those on working days, and emissions during peak hours contribute approximately half of the total daily UPTS emissions, while there are notable variations in the distribution of the carbon footprint among different districts. Moreover, our analysis reveals a positive correlation between the carbon footprints of buses and URT. The statistical analysis reflects different patterns of carbon footprint distribution on different dates during the pandemic, but the carbon footprint distributions on selected dates all follow a power-law distribution. This study may facilitate the understanding to the impacts of UPTS on the environment during the COVID-19 pandemic, and also provide important guidance and reference for the development of carbon emission reduction strategies.

The impact of COVID-19 on travel mobility of older passengers on Xi'an urban rail in China

The COVID-19 outbreak has led to unprecedented changes in people's lives and travel. Public transportation operators must understand individual mobility during COVID-19. Few studies have focused on the impact of COVID-19 on the travel behaviors of vulnerable older adults. To fill this research gap, this study is conducted using smartcard data from the Xi'an urban rail transit during the time of COVID-19 from February 1, 2020 to April 30, 2020. This study first divides the research objects, that is, passengers, into two groups of young adults and older adults, and periods into three stages: the outbreak, post-pandemic, and recovery. Indicators of travel distance, travel frequency, and activity duration were introduced for the two passenger groups to quantify their travel mobility in different stages. To evaluate the spatiotemporal performance of travel mobility, the travel time distribution of the two groups of passengers in each stage was continuously tracked and the returning passengers were proposed to measure the spatial distribution of station-level ridership. In summary, this study provides not only a deeper understanding of the mobility of older passengers during COVID-19 but also useful insights for urban rail transit managers to make more effective decisions for older adults.

Benchmarking Travel Time and Demand Prediction Methods Using Large-scale Metro Smart Card Data

Urban mass transit systems generate large volumes of data via automated systems established for ticketing, signalling, and other operational processes. This study is motivated by the observation that despite the availability of sophisticated quantitative methods, most public transport operators are constrained in exploiting the information their datasets contain. This paper intends to address this gap in the context of real-time demand and travel time prediction with smart card data. We comparatively benchmark the predictive performance of four quantitative prediction methods: multivariate linear regression (MVLR) and semiparametric regression (SPR) widely used in the econometric literature, and random forest regression (RFR) and support vector machine regression (SVMR) from machine learning. We find that the SVMR and RFR methods are the most accurate in travel flow and travel time prediction, respectively. However, we also find that the SPR technique offers lower computation time at the expense of minor inefficiency in predictive power in comparison with the two machine learning methods.

A spatiotemporal analysis of metro‐stop based accessibility to bus services in Shanghai, China

AbstractGround‐level bus offers flexible services with relatively inexpensive fares and high‐level accessibility, thus is the dominant feeder mode for metro in the majority of metropolitan areas in China. Intermodal transfer between the metro and bus networks is therefore a crucial element in the successful operation of an integrated transit system. In this circumstance, a key challenge is lack of appropriate methodologies to evaluate the spatial–temporal disparities of intermodal transfers between metro and bus. To address this issue, this research aims to promote an existing two‐step floating catchment area (2SFCA) model by incorporating the temporal variability of service supply, demand, and travel time to provide a more realistic estimation of accessibility using smart card data and automatic vehicle location data. The proposed methodology was applied in a case study of the metropolitan area of Shanghai in the specific context of metro‐stop based accessibility to bus services. The results of the case study show that the daily fluctuations in output metro‐to‐bus transfer accessibility values are highly sensitive to temporal dynamics of transfer time, service supply, and demand. It is hoped the results output here could give planners and policymakers greater insight into spatiotemporal dynamics on transfer accessibility, and help to establish an effective and efficient integrated transit system.

Data-driven analysis and modeling of individual longitudinal behavior response to fare incentives in public transport

AbstractIncentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers’ behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers’ responses to fare incentives, they focused on passengers’ short-term behavioral responses. Limited studies explore passengers’ longitudinal behavioral responses for different types of adopters, which is important for policy assessment and adjustment. This paper explores and models passengers’ longitudinal behavior response to a pre-peak fare discount incentive using 18 months of smartcard data in public transport in Hong Kong. We classified adopters into six types based on their temporal travel pattern changes before and after the promotion. The longitudinal analysis reveals that among all adopters, 19% of users change their departure times to take advantage of fare discounts but do not contribute to the goal of reducing peak-hour travel. However, these adopters are more likely to sustain their changed behavior in a long term which is not desired by the incentive program. The spatial analysis shows that the origin station distribution of late adopters is relatively more diverse than the early adopters with more trips starting from distant areas. The diffusion modeling shows that the majority adopters are innovators and the word-of-mouth diffusion effect (imitators) is marginal. The discrete choice model results highlight the heterogeneous impact of factors on different types of adopters and their values of time changes. The significant factors common to adopters are: departure time flexibility, the expected money savings, the required departure time changes, and work locations. The findings are useful for public transport planners and policymakers for informed incentive design and management.

Exploring Activity Patterns and Trip Purposes of Public Transport Passengers from Smart Card Data

With the development of technology, there is an increasing number of automatically collected data sources applied in human mobility research. Although these data sets can record travel spatiotemporal information, the semantical information of travel cannot be reflected, e.g., activity pattern or trip purpose. In this paper, we proposed a methodological framework to explore the activity patterns and trip purposes of public transit riders using smart card data in an unsupervised way. First, the heuristic rules are proposed to identify home/working activity based on the multidays travel regularity of passengers. Second, we use a modified latent Dirichlet allocation (LDA) model to explore the activity patterns for the remaining activities based on four activity attributes (including arrival time, duration, day of the week, and destination station functional attribute). In this model, the trip attributes of each passenger are considered as a word in the document with specific topics that correspond to different activity characteristics, based on which the trip purpose of each topic is inferred to interpret travel behavior. The proposed methodology is demonstrated using transit smart card data from Beijing. The performance of our model is compared with two baselines based on perplexity and the result shows that our model achieved the best. Besides, the proportions of inferred trip purposes are compared to the values from travel survey in 2020 Beijing Transport Development Annual Report. The reliability of the results is further confirmed. This work can be extended to other automated travel datasets without ground-truth labels and used to understand and predict travel demand.Practical ApplicationsThis paper proposes an integrated methodological framework to using trip records to explore activity behavior and infer the trip purpose of public transport passenger from smart card data in an unsupervised way. The methodology is implemented on the smart card data in Beijing and demonstrated the availability. The results show that the performance of our model is superior to the other two baselines, moreover, the proportions of inferred different trip purposes are approximate to the ground-truth data from travel survey in the 2020 Beijing Transport Development Annual Report. The methodology can be extended to other automated travel datasets without ground-truth labels and the result can be used to understand and predict travel demand. The study makes it possible to enrich human mobility data, which eventually would be meaningful for comprehensive city transport planning and management and can initiate a new wave of innovative applications in the public transit network, such as passenger portrayal and targeted advertising.

Understanding the mobility patterns of Mass Rapid Transit (MRT) passengers amid COVID‐19 in Singapore using smart card data

The Mass Rapid Transit (MRT) is one of the major modes of public transportation in Singapore. Understanding the mobility patterns of MRT passengers has implications for improving transportation efficiency. As a city‐state with a high population density, Singapore provides a representation of balanced urban dynamics that informs smart urban planning. In this paper, we investigated and visualized (using both static maps and dynamic web map applications) the spatiotemporal characteristics of Singapore's MRT commuting patterns before the COVID‐19 pandemic (January 2020) and during the first outbreak (May 2020) and the Omicron wave of the pandemic (February 2022), using MRT smart card data. We also investigated the relationship between the passenger flows of individual MRT stations and the nearby land use types. Our results showed that the spatial patterns of Singapore's MRT commuters match the polycentric urban structure. In addition to central areas, several regional centres were identified as passenger hotspots in multiple time periods. Furthermore, during the outbreak of the pandemic, especially in the period of the ‘circuit breaker’, there was a major decline in MRT passenger flows and a decrease in average MRT commuting distances during weekend/holiday peak hours. Lastly, correlations between passenger flows of MRT stations and the proportion of nearby land use types have been identified.

Establishment of Optimal Scheduling Using Smart Card Data to Solve the Concentration of Demand for the Red Bus during Peak times

Establishment of Optimal Scheduling Using Smart Card Data to Solve the Concentration of Demand for the Red Bus during Peak times

Understanding the Spatiotemporal Impacts of the Built Environment on Different Types of Metro Ridership: A Case Study in Wuhan, China

As the backbone of passenger transportation in many large cities around the world, it is particularly important to explore the association between the built environment and metro ridership to promote the construction of smart cities. Although a large number of studies have explored the association between the built environment and metro ridership, they have rarely considered the spatial and temporal heterogeneity between metro ridership and the built environment. Based on metro smartcard data, this study used EM clustering to classify metro stations into five clusters based on the spatiotemporal travel characteristics of the ridership at metro stations. And the GBDT model in machine learning was used to explore the nonlinear association between the built environment and the ridership of different types of stations during four periods in a day (morning peak, noon, evening peak, and night). The results confirm the obvious spatial heterogeneity of the built environment’s impact on the ridership of different types of stations, as well as the obvious temporal heterogeneity of the impact on stations of the same type. In addition, almost all built environment factors have complex nonlinear effects on metro ridership and exhibit obvious threshold effects. It is worth noting that these findings will help the correct decisions be made in constructing land use measures that are compatible with metro functions in smart cities.

Modeling the Duration of the Impact of Unplanned Disruptions on Passenger Trips Using Smartcard Data in Urban Rail Systems

Many urban rail systems operate near capacity given the rapid increase in passenger demand, and unplanned disruptions are unavoidable. From a passenger perspective, the duration of trip delays is a major concern, and passenger trip delays may be longer than the train delays. Several studies have focused on predicting train delays, but the research on the duration of the disruption impacts on passenger trips is limited given that the duration is not observed directly. This paper proposes a probabilistic method to estimate the disruption impact duration using smartcard data, explores statistical and machine learning models to predict the duration of impacts on passengers, and identifies influencing factors including incident characteristics, operating conditions, infrastructure, external factors, and demand. The results highlight that prediction accuracies are acceptable for multiple linear regression, accelerated failure time, and random forest models. Disruptions caused by power failures have longer impact durations than other causes, followed by platform screen doors. The fixed block signaling system leads to a larger disruption duration than the moving block system. The study provides, for the first time, a data-driven approach to understanding the duration of the impact of disruptions on passenger trips using smartcard data which can facilitate timely and informed decision-making under unplanned disruptions.

Identifying human mobility patterns using smart card data

ABSTRACT Human mobility is subject to collective dynamics that are the outcome of numerous individual choices. Smart card data which originated as a means of facilitating automated fare collection has emerged as an invaluable source for analysing mobility patterns. A variety of clustering and segmentation techniques has been adopted and adapted for applications ranging from market segmentation to the analysis of urban activity locations. In this paper we provide a systematic review of the state-of-the-art on clustering public transport users based on their temporal or spatial-temporal characteristics as well as studies that use the latter to characterise individual stations, lines or urban areas. Furthermore, a critical review of the literature reveals an important distinction between studies focusing on the intra-personal variability of travel patterns versus those concerned with the inter-personal variability of travel patterns. We synthesise the key analysis approaches as well as substantive findings and subsequently identify common trends and shortcomings and outline related directions for further research.

Profiling Public Transit Passenger Mobility Using Adversarial Learning

It is important to capture passengers’ public transit behavior and their mobility to create profiles, which are critical for analyzing human activities, understanding the social and economic structure of cities, improving public transportation, assisting urban planning, and promoting smart cities. In this paper, we develop a generative adversarial machine learning network to characterize the temporal and spatial mobility behavior of public transit passengers, based on massive smart card data and road network data. The Apriori algorithm is extended with spatio-temporal constraints to extract frequent transit mobility patterns of individual passengers based on a reconstructed personal trip dataset. This individual-level pattern information is used to construct personalized feature vectors. For regular and frequent public transit passengers, we identify similar transit mobility groups using spatio-temporal constraints to construct a group feature vector. We develop a generative adversarial network to embed public transit mobility of passengers. The proposed model’s generator consists of an auto-encoder, which extracts a low-dimensional and compact representation of passenger behavior, and a pre-trained sub-generator containing generalization features of public transit passengers. Shenzhen City is taken as the study area in this paper, and experiments were carried out based on smart card data, road network data, and bus GPS data. Clustering analysis of embedding vector representation and estimation of the top K transit destinations were conducted, verifying that the proposed method can profile passenger transit mobility in a comprehensive and compact manner.