Smart Card Data Research Articles

Analysis of links between dockless bikeshare and metro trips in Beijing

In this study, we examine the factors associated with the integrated use of dockless bikesharing (DBS) and metros. Special attention is paid to the nonlinear effects of these factors, using machine learning; in this case, a random forest model and accumulated local effects (ALE) plots. We measure the integrated use of DBS and metros based on DBS data and metro smart card data in Beijing. Explanatory variables include metro station characteristics, road infrastructure, public transportation services, land use, and urban density, as well as meteorological variables. We find that metro ridership and DBS density around service areas are the most crucial factors for promoting cycling access/ egress near metro stations. After reaching the thresholds of 6,000 trips for metro ridership and 200 bikes/ per km2 for DBS density, notable joint effects are evident between the two variables, which are negative in egress trips but positive in access trips. Bicycle parking and bus transfer distance and their connectivity to metro stations are important for integrated use. We find that buses will be less competitive when exceeding 25 lines per station. Population density has a nonlinear effect, with a positive association below 9,000 persons/ km2 and a decreasing effect above this level. The land-use mix index was also found to be negatively associated with integrated use, and bicycle lanes are not associated with integrated use. Our results demonstrate that public transit service planning should consider the constraints of resources and public space, and we provide recommendations for governments and stakeholders to reallocate DBS and other public transit services around metro stations.

I can board, but I’d rather wait: Active boarding delay choice behaviour analysis using smart card data in metro systems

I can board, but I’d rather wait: Active boarding delay choice behaviour analysis using smart card data in metro systems

Synthetic Generation of Trip Data: The Case of Smart Card

While individual data are key for epidemiology, social simulation, economics, and various other fields, data owners are increasingly required to protect the personally identifiable information from data. Simple data de-identification or ‘data masking’ measures are limited, because they both reduce the utility of the dataset and are not sufficient to protect individual confidentiality. This paper provides detail on the creation of a synthetic trip data in transportation, with the Smart Card data as the case study. It discusses and compares two machine learning methods, a Generative Adversarial Network and a Bayesian Network for modelling and generating this dataset. The synthetic data retain important utility of the real dataset, e.g., the origin, destination, and time of travel, while each data point does not represent a real trip in the original dataset. The synthetic dataset can be used in various applications, including microsimulation of public transport systems, analysing travel behaviours, model predictive control of transit flows, or evaluation of transport policies.

Exploring the public bus ridership loss and recovery during the COVID-19 pandemic: a spatiotemporal analysis using smart card data

ABSTRACT The COVID-19 pandemic has had a dramatic impact on the demand of public transport systems. Nevertheless, the global decrease and recovery of users has been uneven both in time and space. It is important to understand the extent of the unequal resilience of service areas with different characteristics. The objective of this study is to map the patterns of variations in the use of public bus at different scales, urban and regional. The data used were collected by an Automated Fare Collection system based on the use of smart travel cards in the Camp de Tarragona region (Southern Catalonia, Spain). Our approach has the potential to be applied in multiscale studies in other areas with similar data sources. This study also illustrates the potential that data generated by Automated Fare Collection systems have for a better understanding of uneven spatial and temporal patterns of public bus ridership during crises such as a pandemic.

Using smart card data to model public transport user profiles in light of the COVID-19 pandemic

The COVID-19 pandemic caused an unprecedented impact on public transport demand. Even though several studies have investigated the change in the use of public transport during the pandemic, most existing studies where large passive datasets have been considered focus on the drop in ridership at the aggregate level. To address this gap, this research aims to identify and model profiles of passengers considering their public transport recovery after the long-term lockdown in Santiago, Chile, during the early stage of the pandemic. The methodology proposed a three-stage approach associated with the analysis of smart card records. First, cardholder residential areas were identified to enrich the available data by integrating demographic information from the census. Then, a clustering analysis was applied to recognise distinctive classes of users based on their public transport usage change between the pre-pandemic and the post-lockdown phase. Finally, two different models were implemented to uncover the relationships between class membership and travellers’ characteristics (i.e. travel history and demographic characteristics of their residential area). Results revealed a heterogeneous recovery of public transport usage among passengers, summarising them into two recognisable classes: those who mainly returned to their pre-pandemic patterns and those who adapted their mobility profiles. A statistically significant association of travel history with the mobility adaptation profile was found, as well as with aggregate socio-demographic attributes. These insights about the extent of heterogeneity and its drivers can help in the formulation of specific policies associated with public transport supply in the post-pandemic era.

Algorithm for Correcting Error in Smart Card Data Using Bus Information System Data

Algorithm for Correcting Error in Smart Card Data Using Bus Information System Data

A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events

Accurate prediction of individual mobility is crucial for developing intelligent transportation systems. However, while previous models usually focused on predicting individual mobility under ordinary conditions, the models that are applicable to large crowding events are still lacking. Here, we employ the smart card data of 6.5 million subway passengers of the Shenzhen Metro to develop a Markov chain-based individual mobility prediction model (i.e., SCMM) applicable to both ordinary and anomalous passenger flow situations. The proposed SCMM model improves the Markov chain model by incorporating the station-level anomalous passenger flow index and the collective mobility patterns of similar passengers. Compared with the benchmark models, the SCMM model achieves the highest prediction accuracy in both ordinary conditions and large crowding events. Our results highlight the importance of combining an individual’s own historical mobility data with collective mobility data and suggest the appropriate weights of individual and collective information considered in individual mobility modeling.

User-station attention inference using smart card data: a knowledge graph assisted matrix decomposition model

Understanding human mobility in urban areas is important for transportation, from planning to operations and online control. This paper proposes the concept of user-station attention, which describes the user’s (or user group’s) interest in or dependency on specific stations. The concept contributes to a better understanding of human mobility (e.g., travel purposes) and facilitates downstream applications, such as individual mobility prediction and location recommendation. However, intrinsic unsupervised learning characteristics and untrustworthy observation data make it challenging to estimate the real user-station attention. We introduce the user-station attention inference problem using station visit counts data in public transport and develop a matrix decomposition method capturing simultaneously user similarity and station-station relationships using knowledge graphs. Specifically, it captures the user similarity information from the user-station visit counts matrix. It extracts the stations’ latent representation and hidden relations (activities) between stations to construct the mobility knowledge graph (MKG) from smart card data. We develop a neural network (NN)-based nonlinear decomposition approach to extract the MKG relations capturing the latent spatiotemporal travel dependencies. The case study uses both synthetic and real-world data to validate the proposed approach by comparing it with benchmark models. The results illustrate the significant value of the knowledge graph in contributing to the user-station attention inference. The model with MKG improves the estimation accuracy by 35% in MAE and 16% in RMSE. Also, the model is not sensitive to sparse data provided only positive observations are used.

Research of Metro Stations with Varying Patterns of Ridership and Their Relationship with Built Environment, on the Example of Tianjin, China

The metro station ridership features are associated significantly with the built environment factors of the pedestrian catchment area surrounding metro stations. The existing studies have focused on the impact on total ridership at metro stations, ignoring the impact on varying patterns of metro station ridership. Therefore, the reasonable identification of metro station categories and built environment factors affecting the varying patterns of ridership in different categories of stations is very important for metro construction. In this study, we developed a data-driven framework to examine the relationship between varying patterns of metro station ridership and built environment factors in these areas. By leveraging smart card data, we extracted the dynamic characteristics of ridership and utilized hierarchical clustering and K-means clustering to identify diverse patterns of metro station ridership, and we finally identified six main ridership patterns. We then developed a newly built environment measurement framework and adopted multinomial logistic regression analysis to explore the association between ridership patterns and built environment factors. (1) The clustering analysis results revealed that six station types were classified based on varying patterns of passenger flow, representing distinct functional characteristics. (2) The regression analysis indicated that diversity, density, and location factors were significantly associated with most station function types, while destination accessibility was only positively associated with employment-oriented type stations, and centrality was only associated with employment-oriented hybrid type station. The research results could inform the spatial planning and design around metro stations and the planning and design of metro systems. The built environment of pedestrian catchment areas surrounding metro stations can be enhanced through rational land use planning and the appropriate allocation of urban infrastructure and public service facilities.

Crowding on public transport using smart card data during the COVID-19 pandemic: New methodology and case study in Chile

Most crowding measures in public transportation are usually aggregated at a service level. This type of aggregation does not help to analyze microscopic behavior such as exposure risk to viruses. To bridge such a gap, our paper proposes four novel crowding measures that might be well suited to proxy virus exposure risk at public transport. In addition, we conduct a case study in Santiago, Chile, using smart card data of the buses system to compute the proposed measures for three different and relevant periods of the COVID-19 pandemic: before, during, and after Santiago’s lockdown. We find that the governmental policies diminished public transport crowding considerably for the lockdown phase. The average exposure time when social distancing is not possible passes from 6.39 min before lockdown to 0.03 min during the lockdown, while the average number of encountered persons passes from 43.33 to 5.89. We shed light on how the pandemic impacts differ across various population groups in society. Our findings suggest that poorer municipalities returned faster to crowding levels similar to those before the pandemic.

Identifying the Spatiotemporal Metro-to-Bus Transfer Deserts in Shanghai, China

In the majority of metropolitan areas in China, the ground-level bus is the dominating feeder mode for metro systems. For enhancing the intermodal integration, it is necessary to evaluate the transfer accessibility between metro and bus considering the spatiotemporal dimensions with finer resolution. This study developed a methodology to identify the metro-to-bus transfer desert by considering the spatiotemporal dynamics on both service supply and passenger demand based on smart card data and automatic vehicle location data. In order to provide a more accurate and detailed depiction of results and stronger policy implications, this research aggregates to a higher spatial resolution (i.e., metro stops), instead of utilizing census tracts or administrative zones used in available research. The proposed methodology was implemented in the case study of the metropolitan area of Shanghai. Our empirical analysis highlights transfer service deficiency existing across the metropolitan area of Shanghai throughout the day, and varies both on spatially and temporally. Peak hours are characterized by better transfer accessibility levels on average but worse performances on metro stops with high transfer volume compared with off-peak hours. Because the developed methodology makes use of widely available input data, policymakers and transit agencies could replicate such gap analysis of local transit services in other contexts around the world.

Measuring activity-based social segregation using public transport smart card data

Measuring activity-based social segregation using public transport smart card data

Quantifying spatially varying impacts of public transport on NO$$_2$$ concentrations with big geo-data

Anthropogenic NO[Formula: see text] concentrations cause climate change and human health issues. Previous studies have focused on the contribution of traffic factors to NO[Formula: see text] emissions but have ignored the spatially varying impact of public transport supply and demand on high-resolution NO[Formula: see text] concentrations. This study first applies a two-stage interpolation model to generate a high-resolution urban NO[Formula: see text] concentration map originating from satellite measurement products. Then, we formulate 12 explanatory indicators derived from a fusion of massive big geo-data including smart card data and point of interest information, to represent the specific degree of public transport supply and citizens' demand. Furthermore, a geographically weighted regression is applied to quantify the spatial variation in the effect of these indicators on the urban NO[Formula: see text] concentrations. The result shows that public transportation coverage, frequency, and capabilities as public transport supply indicators in metropolitan and suburban areas have a two-way influence on the NO[Formula: see text] emissions. However, among public transport demand indicators, the economic level has a significant positive impact in most areas. Our findings can provide policy implications for public transportation system optimization and air quality improvement.

How is Intraday Metro Ridership related to Station Centrality in Athens, Greece?

In this study, intraday correlations between station centralities and ridership at stations of the Athens metro system in Greece are explored. An unweighted L-space representation of the physical metro network is developed, and degree, closeness and betweenness are selected as station centrality measures. Hourly smart-card data are used for representing passenger flows. For station classification, principal component analysis and k-means clustering are utilized. The findings suggest that centrality and ridership usually move in opposite directions, morning peak-hour boardings are completely uncorrelated with station centrality, and metro stations can be classified as ‘central destinations’, ‘averagely central origins’, and ‘underutilized peripheral stations’.

Validation of a multi-modal transit route choice model using smartcard data

Validation of travel demand models, although recognised as important, is seldom undertaken. This study adds to the scarce literature in this field by undertaking an external validation of a multi-modal transit route choice model. The model was estimated using smart card data for the urban transit network of Amsterdam before the introduction of a new metro line and is used to predict changes in travel behaviour after the network change. To validate, the model was checked for changes in estimated parameters between the two time periods, and predictive ability was evaluated at different aggregation levels. Although most model parameters were found to be unstable between the two contexts, the predictive performance at all levels was similar to the locally estimated model. Moreover, individual choices and transit mode-share predictions were found to be close to the observed ones. The errors were relatively larger for the link and route-level predictions, some of which could be attributed to the assumptions made regarding consideration choice set given as input to the model. On comparing alternative model specifications, using generic instead of mode-specific travel attributes lead to a strong degradation in predictive performance. Conversely, a model incorporating overlap between routes, with a better model fit in the base period, did not offer a clear improvement in prediction performance. The study highlights the need to validate transit route choice models before using them for deriving policy recommendations, especially in this data-rich age in which it can often be undertaken at a relatively low additional cost.

Reconstruction of incomplete public transportation check-out records by heuristic approaching

Smart cards, integrated circuit cards, are frequently used to pay transportation fees in many countries such as the United States, the European Union, Korea and others. Smart card transaction histories in public transportation have been used in making diverse traffic policies. Unfortunately, data preprocessing is required to organize records to extract useful information since the raw data contain artifacts and incomplete information. The paired data, i.e. a boarding and its associated alighting, are one of the key pieces of information required to yield the city traffic demand. However, it is difficult to gather the paired data mainly because the card tagging policies and data collecting methods vary from city to city and from institution to institution. The broken trip links between the boarding and its associated alighting information are the most frequent incomplete data in the smart card histories. Although huge budgets are spent on annual traffic surveys in many countries, there are no accurate Smart card data analysis methods that cover all city cases due to high data complexity and limited base information. Among these two restrictions, limited information is more difficult to resolve because it has a large codomain, thousands of stops, for inference under tiny samples. It gives handicaps of applying well-known high-performance approaches such as deep learning and probability analysis. We propose a reconstruction method by a customized clustering algorithm based on coordinates of transactions-occurred stops, user trip histories and actual vehicle movement trajectories for incomplete trip pairs. The proposed method is derived from evaluating each passenger’s historical clusters and trip patterns. In a blind test 79.1% accuracy is achieved in predicting missing alighting stops while conventional methods reach only 42.3% accuracy.

Spatiotemporal trip profiles in public transportation reveal city modular structure

Understanding urban mobility patterns within public transportation (PT) systems is key for cities to improve services and promote sustainable mobility. Exploring daily PT riders’ traffic flows using anonymized big data is a first step to analyze when and to where people travel. Nevertheless, assessing the degree to which PT usage patterns correlate with the spatial distribution of points of interest (POI) and subsequently affect the city structure is less attempted. Classic approaches use questionnaires and survey data that are costly and often yielding limited statistical significance. The purpose of this research is to understand associations between travel patterns of urban commuters and the functional organization of a city. To this end, we propose a network-constrained temporal distance measure for modeling PT rider travel patterns from smart card data; and further introduce a fully autonomous approach to describe the span of services available at catchment areas around metro stations. The end result is a detailed analytical prescription of spatiotemporal commuting patterns in the city of Lisbon as well as an analytical contextual information that enables us to understand the functional modular structure of urban facilities. Using the city of Lisbon as the guiding case study, the gathered results confirm the hypothesis that PT rider’s daily flows along the PT network of metro stations reveal a city modular structure.

Predicting bus ridership based on the weather conditions using deep learning algorithms

This study proposes a new approach to predict bus ridership based on the weather conditions while accounting for additional factors such as large events, holidays, and bus cancellations. For this purpose, a deep learning algorithm - multilayer perceptron (MLP) networks - is implemented using smart-card data from the bus network in the Twente Region in the Netherlands. The prediction was carried out under three scenarios: (1) without the weather conditions (base model), (2) with the weather conditions of the same time as the boarding time into buses, and (3) with the weather conditions of an hour ahead of boarding time into buses. The results showed that the application of the MLP is very promising in forecasting bus ridership considering the meteorological parameters. The average errors were improved by 4.9% on weekdays and 2.8% on weekends as a result of including the meteorological parameters in the models. The improvements were even more prominent on weekdays with moderate to extreme weather conditions (for instance, heavy precipitation, strong wind speed, and low temperature). However, the models showed higher errors during the morning peak hours on days with heavy rainfall and strong wind speed.

Predicting Hourly Boarding Demand of Bus Passengers Using Imbalanced Records From Smart-Cards: A Deep Learning Approach

The tap-on smart-card data provides a valuable source to learn passengers’ boarding behaviour and predict future travel demand. However, when examining the smart-card records (or instances) by the time of day and by boarding stops, the positive instances (i.e. boarding at a specific bus stop at a specific time) are rare compared to negative instances (not boarding at that bus stop at that time). Imbalanced data has been demonstrated to significantly reduce the accuracy of machine-learning models deployed for predicting hourly boarding numbers from a particular location. This paper addresses this data imbalance issue in the smart-card data before applying it to predict bus boarding demand. We propose the deep generative adversarial nets (Deep-GAN) to generate dummy travelling instances to add to a synthetic training dataset with more balanced travelling and non-travelling instances. The synthetic dataset is then used to train a deep neural network (DNN) for predicting the travelling and non-travelling instances from a particular stop in a given time window. The results show that addressing the data imbalance issue can significantly improve the predictive model’s performance and better fit ridership’s actual profile. Comparing the performance of the Deep-GAN with other traditional resampling methods shows that the proposed method can produce a synthetic training dataset with a higher similarity and diversity and, thus, a stronger prediction power. The paper highlights the significance and provides practical guidance in improving the data quality and model performance on travel behaviour prediction and individual travel behaviour analysis.

Introducing Autonomous Shuttle Services Based on Travel Patterns for the Elderly

The transportation-disadvantaged population is rapidly increasing through aging; moreover, the mobility of the elderly has increased due to life expectancy improvements and lifestyle changes. To respond to these changes, a customized mobility service specifically tailored to the travel patterns of the elderly was developed in this study. In particular, an autonomous shuttle service that can improve the operational efficiency and the accessibility of existing transportation methods was proposed by considering the travel characteristics of elderly people, who mainly travel short distances. To this end, a study was conducted in Seongnam City, Republic of Korea, where mobility support services for the elderly are insufficient. Using smart card data, the elderly were classified according to their travel patterns, with autonomous shuttle routes suggested for each travel purpose. We derived four clusters via the Gaussian mixture model clustering, with travel purposes classified according to the spatiotemporal travel patterns. Finally, the major routes for each travel purpose were selected, and feasible road paths for autonomous shuttle operation were suggested using the concept of ODD (operational design domain). This holistic methodology is expected to contribute to the development of autonomous mobility services for the elderly and for the establishment of welfare policies based on the smart card data.