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Drawing on transit smart card data allied with local weather station records over a 12-month period, this paper takes Brisbane, Australia as the study context and examines the way in which weather impose influences on public transit passenger's travel behaviour. In terms of transit passenger's spatiotemporal origin–destination (OD) information, a new travel behaviour indicator called stickiness is developed to reflect passenger's travel similarity in using the transit service over a period. By applying a suite of regression models, it shows that weather's effects on transit passenger's travel behaviour vary by temporal period and passenger type. In general, weather is shown to exert a stronger effect on passenger's stickiness during midday off-peak hours in comparison with either AM peak or PM peak. Across all types of passengers, child passengers are found to be most tolerant to weather changes and even be stickier to their regular travel pattern during AM peak. The modelling results also reveal that when it relates to journey-to-work or journey-to-school, weather's effects on alighting location are much smaller than other three OD-related travel behaviour features (i.e., boarding time, boarding location, and alighting time). In contrast with the postponement during AM peak, transit passengers are prone to bring forward their trip back home during PM peak in response to poor weather conditions. Moreover, this paper verifies that weather parameters are not singly perceived by transit passengers but as an interrelated unity in shaping transit passenger's travel behaviour. By enriching the weather–travel behaviour scholarship, the empirical findings of this study are helpful to render a holistic understanding of the weather–travel behaviour relationship and have important implications for transit operators in building a more weather-resilient transit system. [ABSTRACT FROM AUTHOR]

This paper presents a data fusion method to infer the origin and destination zones of transit trips from smart card data. The fusion framework has disaggregate mixed multinomial logit models at its core that predict the most probable origin and destination zones of individual transit trips using smart card transaction records, land use data, and transit system characteristics. The logit models are estimated using revealed trip origin and destination responses from a travel survey that are augmented by land use and transit system data to provide contexts about the zones' trip generation and attraction potentials. For empirical analysis, the methodology is applied to the smart card system of the Greater Toronto and Hamilton Area. Specifically, it is tested under different system configurations (tap-on-only and tap-on-and-off) and for networks with substantial shares of automobile and walk access/egress. When applied to transit trips constructed from the smart card transactions, the estimated models successfully capture the spatial distribution of trip origin and destination at the traffic analysis zone level. The empirical analysis also demonstrates that the proposed fusion method can be appropriately used to reconcile information provided by transit smart card and travel surveys to generate up-to-date transit demand data necessary for public transport planning and operations. [ABSTRACT FROM AUTHOR]

Many human activities, including daily travel, show a mix of stable, intermittent and changing patterns in demand by individuals over time. However, the lack of continuous, long-term, passenger-linked data for public transport (PT) journeys means that we do not know how passenger ridership evolves in real-world networks. This paper proposes the CIAM model for classifying long-term passenger engagement with PT. CIAM is a data-driven model combining year-on-year churn (C), monthly intensity (I), annual (A) and multi-year (M) engagement. Parameter search algorithms are used to ensure that the learned features are distinctive and robust. We evaluated CIAM using a 5-year dataset from a PT network with over 300 million journeys. CIAM identified distinct patterns of long-term ridership at multiple time scales. Although the total number of annual journeys was relatively stable over the five years, we found long-term differences between passenger subgroups. Churn of passengers was a major factor in ridership with only 55% of passengers retained from year to year. Patterns of annual engagement are often intermittent, so short-term snapshots of a few weeks are typically not good indicators for longer term engagement. Only 27% of high-frequency, full-fare riders still have the same level of engagement four years later, compared with 55% who continue high-frequency engagement after only one year. • CIAM learns long-term patterns of PT engagement at multiple temporal scales. • Churn is a major factor of ridership with year on year passenger retention of only 55%. • Annual engagement is a mixture of contrasting short-term intensities. • Only 27% of high-frequency, full-fare passengers maintain that engagement for 5 years. • Data-driven CIAM identifies underlying context transitions from movement data alone. [ABSTRACT FROM AUTHOR]

Service bottlenecks are a key barrier to building a resilient public transport system. In this paper, we propose a new approach to automatically extract the role of a station in dynamical public transport flow networks based on the emerging role discovery method in network science. The term "role" in this study refers to the distinctive position or function that a station plays within the public transport flow network. Using smart card data from Nanjing public bikesharing agencies, we first construct dynamical public transport flow networks with notions of dynamical graph and edge. We then develop a dynamical algorithm to recursively compute the structural flow characteristics of nodes in passenger flow networks. Non-negative Matrix Factorization is conducted to extract the role memberships from the derived structural feature matrix and interpret each role in terms of measurements with practical values. The network hubs and potential service bottlenecks are then identified based on their operating characteristics and dynamics. Furthermore, the day-to-day and within-day role dynamics of public transport stations over time are unveiled. The results contribute to a better understanding of the interplay between stations in the network, and the identification of roles provides insight for public transport agencies to improve service resilience. [ABSTRACT FROM AUTHOR]

Travel activities influence individual travel location, time, and frequencies. Understanding individual travel regularity under different activities is crucial for individual mobility prediction and urban facilities planning. Existing studies need further improvement as they have not adequately taken into account the impact of activity factors on individual travel patterns. In this paper, we propose an innovative framework for exploring travel regularity and the correlation with urban region attributes at the individual level. The framework's novelty and uniqueness lie in its three layers: (i) A trip pre-processing layer for extracting every complete public transit trip and trip purposes (e.g. commuting activity and non-commuting activity) by combining individual's travel characteristics and public transport network. (ii) An entropy-based travel regularity measurement and K-means based multiple-view clustering layer to assess the extent of individual travel repetition over time for various travel activity and investigate the similarities and differences among users. (iii) A region dependence correlation analysis layer for exploring the correlation between individual travel regularity and regions attributes of two key locations: home and workplace More importantly, by employing the framework, we gained empirical insights based on a large-scale dataset (covering 0.64 million users) collected from public traffic smart cards. For instance, commuters can be categorized into four groups based on the regularity of their commuting activities: Regular Workers (24%), Flex-time Workers (21%), Overtime Workers (35%), and Other Workers (20%). The distribution of these four groups is associated with workplace's administrative characteristics, with a Pearson correlation of 0.503. In addition, individuals can also be classified into two groups based on their travel regularity of non-commuting activities: Limited Visitors (37%) and Active Explorers (63%). The distribution of these individuals is correlated with the coverage ratio of public transit facility in their home locations, showing a Pearson correlation of 0.604. • A framework exploring individual travel regularity under different purpose. • Entropy-Space K-means for travel activity-based individual classification. • Inter-personal and intra-personal variability of individual travel patterns. • Correlation between individual travel regularity and urban region attributes. [ABSTRACT FROM AUTHOR]

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements. [ABSTRACT FROM AUTHOR]

• Estimated the causal economic impact of an off-peak fare discount in Hong Kong. • Applied the difference-in-difference method to control for confounding bias. • Found the aggregate effect to be small but statistically significant and heterogeneous. • Responsiveness was governed by travel costs and crowding. • Produced novel causal estimates of trip rescheduling elasticities. This paper quantifies the causal impact of differential pricing on the trip-scheduling of regular commuters using the Mass Transit Railway (MTR) in Hong Kong. It does so by applying a difference-in-difference (DID) method to large scale smart card data before and after the introduction of the Early Bird Discount (EBD) pricing intervention. We find statistically significant but small effects of the EBD in the form of earlier departure times. Leveraging the granularity of the data, we also allow for the treatment effect to vary over observed travel characteristics. Our empirical results suggest that fares and crowding are the key determinants of commuter responsiveness to the EBD policy. [ABSTRACT FROM AUTHOR]

In recent years smartcards have been implemented in many transit systems around the world as a means by which passengers pay for travel. In addition to allowing speedier boardings there are many secondary benefits of smartcard systems including better understanding of travel patterns and behaviour of travellers. Such research is dependent on the smartcard correctly recording the boarding stop, and where available the alighting stop. It is also dependent on the smartcard system correctly aggregating individual rides into trips. This paper identifies causes for why smartcard systems may not correctly record such information. The first contribution of the paper is to propose a set of rules to aggregate individual rides into a single trip. This is critical in the research of activity based modelling as well as for correctly charging the passenger. The second contribution of the paper is to provide an approach to identify erroneous tap-out data, either caused by system problems or by the user. An approach to detecting this phenomenon is provided. The output from this analysis is then used to identify faulty vehicles or data supply using the “comparison against peers approach”. This third contribution of the paper identifies where transit agencies and operators should target resources to improve performance of their Automatic Vehicle Location systems. This method could also be used to identify users who appear to be tapping out too early. The approaches are tested using smartcard data from the Singapore public transport network from one week in April 2011. The results suggest that approximately 7.7% of all smartcard rides recorded the passenger as alighting one stop before the bus stop that they most probably alighted at. A further 0.7% of smartcard rides recorded the passenger as alighting more than one stop before the bus stop that they most probably alighted at. There was no evidence that smartcards overestimated the distance travelled by the passenger. [ABSTRACT FROM AUTHOR]

• An heterogeneous corpus of data sources is now available to estimate travel demand. • We compare three independent data sources to estimate public transit OD matrices. • Automatic fare collection data is not error-free. • Household travel survey may significantly underestimate the volume of trips. • Better understanding of the available data sources and the potential trade-offs. Origin–destination (OD) matrices are one of the key elements in travel behaviour analysis. For decades, transportation researchers have mostly used data obtained by active solicitation such as surveys to construct these matrices but new data sources like automatic fare collection (AFC) are now available and can be used to measure OD flows. As a result, a more heterogeneous corpus of data sources is now available to estimate travel demand. However, little research examines how comparable the estimated demands may be. In this paper, three data sources namely a household travel survey, a large scale origin–destination survey and entry only automated fare collection are processed to derive typical weekday public transit OD trip matrices. Various elements of the resulting matrices are then compared. While all the matrices share some common characteristics, there are also substantial differences that must be addressed. AFC data is not error-free and needs to be supplemented with data from other sources to construct a representative OD trip matrix. This is because not all destinations can be inferred, the smart card penetration rate is far less than 100% and fare evasion cannot be ignored. Our empirical results suggest that scaling an AFC matrix with automated passenger counts may be a viable solution. The results also indicate that the household travel survey significantly underestimates the volume of public transit trips compared to the other sources. The findings of this research contribute to a better understanding of the available data sources for public transit demand estimation. They can help practitioners to improve the quality and accuracy of OD matrices. [ABSTRACT FROM AUTHOR]

• A new concept to understand the mobility and location features of subway stations. • Transplant Language Processing to understand subway stations as compound words. • Obtain mobility and location semantics from neural net and affinity propagation. • Interpretsemantic results in an urban planning aspect. Rapid urbanization and modern civilization require sound integration with public transportation systems. In the same time, the volume and complexity of public transportation network are increasing, making it harder to understand the public transportation dynamics. As a first step, understanding the similarity among subway stations is imperative. In this paper, we proposed a semantic framework inspired from natural language processing (NLP) to interpret subway stations as compound words. Specifically, we transplanted context and literal meaning of compound words into mobility and location attributes of stations. Using smart card data, we trained stacked autoencoders (SAE) with designed flow matrices as an embedding method to learn the mobility attributes. Subsequently, to discover the location attributes, we have applied affinity propagation clustering to classify 9 point of interest (POI) categories. Combined with urban planning knowledge, we manage to comprehend the land use meanings of 9 POI clusters. The location semantics is chosen from those categories reflecting its urban land use pattern. By choose meaningful combination of mobility and location semantics for stations' similarity case studies, we summarized potential applications of this semantic framework. [ABSTRACT FROM AUTHOR]

• Proposes a general approach for the design of promotion-based transit TDM strategies. • Formulates the optimal promotion design as a 0-1 integer optimization problem. • Recognizes explicitly the role of effective and ineffective users in the formulation. • Evaluates the performance profile of various structures in space, time and discount. Travel demand management (TDM) is used for managing congestion in urban areas. While TDM is well studied for car traffic, its application in transit is still emerging. Well-structured transit TDM approaches can help agencies better manage the available system capacity when the opportunity and investment to expand are limited. However, transit systems are complex and the design of a TDM scheme, deciding when, where, and how much discount or surcharge is implemented, is not trivial. The paper proposes a general framework for the optimal design of promotion based TDM strategies in urban rail systems. The framework consists of two major components: network performance and optimization. The network performance model updates the origin-destination (OD) demand based on the response to the promotion strategy, assigns it to the network, and estimates performance metrics. The optimization model allocates resources to maximize promotion performance in a cost effective way by better targeting users whose behavioral response to the promotion improves system performance. The optimal design of promotion strategies is facilitated by the availability of smart card (automated fare collection, AFC) data. The proposed approach is demonstrated with data from a busy urban rail system. The results illustrate the value of the method, compare the effectiveness of different strategies, and highlight the limits of the effectiveness of such strategies. [ABSTRACT FROM AUTHOR]

Abstract: Micropayment is common in retail outlets and public transport systems. This paper considers the situation where smart-card-based e-micropayment scheme starts off as single-purpose scheme and is progressing to be a multi-purpose scheme. User deciding whether to use such a scheme is a multiple criteria decision-making (MCDM) problem and depends on factors such as transaction cost, availability of discount, ease of use, convenience, reliability, and participation by transport-based companies, retail outlets, large organizations, as well as whether others are using it. Through a case study of successful smart-card-based single-purpose e-micropayment schemes in Taiwan and from the user’s perspective, the dynamic picture of the progression is obtained to analyze how different factors interact and collectively contribute to the successful transformation and diffusion of the scheme. Since this is a MCDM problem, Decision Making Trial and Evaluation Laboratory, a MCDM method, is used. Hence, this paper contributes to extending practical applications of DEMATEL to the field of electronic payment system. It is found that the dynamic picture of the progression of single-purpose smart-card-based e-micropayment scheme to multi-purpose scheme is indeed complex. The factors are definitely not independent of one another and they interact with one another. Ease of use, discount and participation by retail outlets are net causer factors. Top-up locations, reliability of the scheme, participation by transport companies and large organizers are net receiver factors. Factors related to participants of the scheme (such as user base, retail-outlet and transport companies) also exhibit the presence of network effect. The scheme operator faces a chicken and egg situation but the operator has to start somewhere to kick-start the process, end the waiting game, and hopefully to achieve the bandwagon effect. A possible starting point is to focus a lot of the energy on getting more retail outlets onboard. To further accelerate the progression to multi-purpose scheme, a multi-pronged approach to manage the transformation is also advised. To attract more users and to push them to use the scheme to make payment at the participating retail outlets, discount can be offered. [Copyright &y& Elsevier]

• A agent-based simulation optimization model is proposed to optimize the release of passenger guidance information in a rail network. • A hybrid heuristic solution algorithm, integrated with passenger simulator and genetic algorithm, is developed. • A case study of Beijing subway is carried out with the large-scale smart card data. The passenger flow guidance is an effective demand management strategy to alleviate the excessive congestion in the urban rail transit network. In order to determine the scope and the timing, a simulation-based optimization model is proposed to optimize the release of passenger flow guidance information in the rail transit network in this paper. In the optimization model, we mainly focus on three aspects namely; where, when and what type of the guidance information should be released to the passengers. In the simulation model, the passenger choice behavior is captured by the agent-based simulation method, which responses to the congestion and the guidance information. Based on this, the dynamic passenger flow distribution can be derived. Furthermore, the adoption rate of the displayed guidance information on passenger information system as well as its impact on passenger travel behavior are also considered in the model. A hybrid heuristic solution algorithm, integrated with passenger simulator and genetic algorithm, is developed to solve the proposed simulation-based optimization model. Finally, a case study of Beijing subway is carried out with the large-scale smart card data. The numerical study shows that the passenger flow demand affects the guidance effect significantly and the best guidance effect can be met with sufficiently high passenger flow demand. And the guidance rate is also found to affect the guidance results. The results also show that the proposed model can provide a detailed guidance scheme for every station at selected time intervals. The results show that the dynamic releasing scheme can save up to a total of 46,319 min in passenger travel time during a single guidance period. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of local weather conditions on adult transit ridership across three transit modes. Drawing on smart card data and half hourly weather station records for a 12 month period, analysis reveals that weather imposes an effect on adult transit ridership and that its influence varies by mode. Ferry ridership is found to be more sensitive to changes in weather compared to either bus or train ridership. Findings also reveal that weather's influence on ridership varies across the course of a day. During morning and evening peak hours, weather is shown to exert a weaker effect than other periods throughout the day. We argue that our findings are important in their capacity to contribute to a new evidence base with the potential to inform the (re)design of more weather-resilient transit systems by shedding new light on the weather–transit ridership relationship. [ABSTRACT FROM AUTHOR]

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. • Four public transport crowding measures that proxy virus exposure risk are proposed. • A case study is conducted in Santiago, Chile, using GPS and smart card data. • Three periods, before, during, and after COVID-19 lockdowns are compared. • Governmental policies diminished crowding considerably during lockdowns. • Poorer municipalities returned faster to pre-pandemic crowding levels. [ABSTRACT FROM AUTHOR]

Smart card data (SCD) allow analyzing mobility at a fine level of detail, despite the remaining challenges such as identifying trip purpose. The use of the SCD may improve the understanding of transit users' travel patterns from precarious settlements areas, where the residents have historically limited access to opportunities and are usually underrepresented in surveys. In this paper, we explore smart card data mining to analyze the temporal and spatial patterns of the urban transit movements from residents of precarious settlements areas in São Paulo, Brazil, and compare the similarities and differences in travel behavior with middle/high-income-class residents. One of our concerns is to identify low-paid employment travel patterns from the low-income-class residents, that are also underrepresented in transportation planning modeling due to the lack of data. We employ the k-means clustering algorithm for the analysis, and the DBSCAN algorithm is used to infer passengers' residence locations. The results reveal that most of the low-income residents of precarious settlements begin their first trip before, between 5 and 7 AM, while the better-off group begins from 7 to 9 AM. At least two clusters formed by commuters from precarious settlement areas suggest an association of these residents with low-paid employment, with their activities placed in medium / high-income residential areas. So, the empirical evidence revealed in this paper highlights smart card data potential to unfold low-paid employment spatial and temporal patterns. [ABSTRACT FROM AUTHOR]

Smart card logs constitute a valuable source of information to model a public transportation network and characterize normal or abnormal events; however, this source of data is associated to a high level of noise and missing data, thus, it requires robust analysis tools. First, we define an anomaly as any perturbation in the transportation network with respect to a typical day: temporary interruption, intermittent habit shifts, closed stations, unusual high/low number of entrances in a station. The Parisian metro network with 300 stations and millions of daily trips is considered as a case study. In this paper, we present four approaches for the task of anomaly detection in a transportation network using smart card logs. The first three approaches involve the inference of a daily temporal prototype of each metro station and the use of a distance denoting the compatibility of a particular day and its inferred prototype. We introduce two simple and strong baselines relying on a differential modeling between stations and prototypes in the raw-log space. We implemented a raw version (sensitive to volume change) as well as a normalized version (sensitive to behavior changes). The third approach is an original matrix factorization algorithm that computes a dictionary of typical behaviors shared across stations and the corresponding weights allowing the reconstruction of denoised station profiles. We propose to measure the distance between stations and prototypes directly in the latent space. The main advantage resides in its compactness allowing to describe each station profile and the inherent variability within a few parameters. The last approach is a user-based model in which abnormal behaviors are first detected for each user at the log level and then aggregated spatially and temporally; as a consequence, this approach is heavier and requires to follow users, at the opposite of the previous ones that operate on anonymous log data. On top of that, our contribution regards the evaluation framework: we listed particular days but we also mined RATP 1 1 Parisian transport authority Twitter account to obtain (partial) ground truth information about operating incidents. Experiments show that matrix factorization is very robust in various situations while the last user-based model is particularly efficient to detect small incidents reported in the twitter dataset. [ABSTRACT FROM AUTHOR]

Although smart card fare data has recently become more prevalent as a rich, comprehensive and continuous source of information, there is still some missing information which inhibits its capability in the research field. One key missing piece of information is the passengers’ trip purpose. This paper investigates the potential of the smart card data to infer passengers’ trip purpose, thereby reducing the use of the expensive and time-consuming Household Travel Surveys (HTS). On this basis, an improved model has been proposed, calibrated and validated for trip purpose inference by integrating different data sources, namely: HTS, a land use database, the South East Queensland Strategic Transport Model (SEQSTM), the General Transit Feed Specification (GTFS) data, O-D survey data, and most importantly the unique smart card fare data from Brisbane, Queensland. As smart card fare data does not record passengers’ trip purpose, the calibration and validation procedures are performed on HTS data. Based on the validation results, the proposed methodology shows a strong capability to predict trip purpose at a high level of accuracy. The results show an overall 67% correct inference after applying spatial attributes, but the correct inference increases to 78% after applying temporal attributes. Different trip purposes show different sensitivities to the applied spatial and temporal attributes. Work and home trips have the highest correct inference results, at 92% and 96%, respectively. Furthermore, the results of correct inference for shopping and education trips improved after applying the temporal attributes. [ABSTRACT FROM AUTHOR]

Transit oriented development (TOD) has been an important topic for urban transportation planning research and practice. This paper is aimed at empirically examining the effect of rail transit station-based TOD on daily station passenger volume. Using integrated circuit (IC) card data on metro passenger volumes and cellular signaling data on the spatial distribution of human activities in Shanghai, the research identifies variations in ridership among rail transit stations. Then, regression analysis is performed using passenger volume in each station as the dependent variable. Explanatory variables include station area employment and population, residents’ commuting distances, metro network accessibility, status as interchange station, and coupling with commercial activity centers. The main findings are: (1) Passenger volume is positively associated with employment density and residents’ commuting distance around station; (2) stations with earlier opening dates and serving as transfer nodes tend to have positive association with passenger volumes; (3) metro stations better integrated with nearby commercial development tend to have larger passenger volumes. Several implications are drawn for TOD planning: (1) TOD planning should be integrated with rail transit network planning; (2) location of metro stations should be coupled with commercial development; (3) high employment densities should be especially encouraged as a key TOD feature; and (4) interchange stations should be more strategically positioned in the planning for rail transit network. [ABSTRACT FROM AUTHOR]

User profiles, considered as one of the fundamental inputs of recommendation systems and customized services, can be rationally applied in the public transport domain to represent passengers' characteristics and behavioral preferences. A user profile of a bus passenger, termed as a bus passenger profile (BPP), is an assortment of labels containing passengers' travel features. This paper proposes a data-driven framework for developing BPP so as to provide guidance on how to create and estimate user profiles for bus passengers based on smart card data. The proposed method comprises three steps. (i) Data preprocessing aimed at extracting key information and preparing passenger profiling. (ii) A tag system aimed at storing the estimated travel features of passengers. (iii) Knowledge graphs aimed at connecting various BPP with semantic edges for practical application of prior knowledge in downstream tasks. The developed framework is implemented in a case study of the Beijing bus system. Deployment of the developed framework has demonstrated that it can satisfactorily develop BPP, while prior knowledge from the BPP-based knowledge graphs can benefit downstream tasks. [ABSTRACT FROM AUTHOR]

In recent decades, transit-oriented development (TOD) has been considered as an effective way to alleviate the negative impacts of rapid urbanization. The ridership of rail stations is an important indicator for understanding the relationship between rail transit and land use, which helps enhance their coordinated development. How to regulate such development is a fundamental question that planning of rail stations and surrounding land use endeavors to answer. Understanding the relationship between rail transit and land use is the basis of effective planning. From smart card data (SCD), the boarding and alighting ridership time series can be extracted to understand their relationship from a new perspective. Current studies mainly consider the temporal similarity of ridership time series between individual stations but ignore spatial dependency between a station and its adjacent stations. To fill this gap, this paper proposes a novel method for refining the understanding from a spatiotemporal similarity perspective. First, we measure the temporal similarity of ridership time series between two individual stations using the autocorrelation function method. Second, we determine the spatiotemporal similarity between two stations by integrating the temporal similarity from their adjacent stations based on spatial dependency. Finally, the extracted spatiotemporal similarity is used for spectral clustering. As a case study, we analyze SCD on five consecutive weekdays at 289 rail stations in Shanghai, China. The land use functions of rail station areas are classified into 6 clusters: employment-oriented stations, residential-oriented stations, mixed stations, etc. By comparing the enrichment factors of each type of points of interest (POIs) in these clusters, the dominant land use function between our classification and POI information is demonstrated to be consistent. Furthermore, using three clustering performance indexes and several typical stations, the priority of our proposed method with integration of the spatial dependency can be confirmed to be superior to those without considering such important information. We conclude that the proposed method and findings are beneficial to balancing the needs of public transportation development and promoting the integration of transportation and land use for TOD implementation. • A novel method is proposed for inferring land use functions of station areas. • The proposed method is based on spatiotemporal similarity in ridership data. • Validation of the proposed method is verified by a case study in Shanghai. • The inferred land use of station areas spatially varies in Shanghai. • This study refines understanding of relationship between rail transit and land use. [ABSTRACT FROM AUTHOR]

In recent years, there has been increased interest in using completely anonymized data from smart card collection systems to better understand the behavioural habits of public transport passengers. Such an understanding can benefit urban transport planners as well as urban modelling by providing simulation models with realistic mobility patterns of transit networks. In particular, the study of temporal activities has elicited substantial interest. In this regard, a number of methods have been developed in the literature for this type of analysis, most using clustering approaches. This paper presents a two-level generative model that applies the Gaussian mixture model to regroup passengers based on their temporal habits in their public transportation usage. The strength of the proposed methodology is that it can model a continuous representation of time instead of having to employ discrete time bins. For each cluster, the approach provides typical temporal patterns that enable easy interpretation. The experiments are performed on five years of data collected by the Société de transport de l’Outaouais. The results demonstrate the efficiency of the proposed approach in identifying a reduced set of passenger clusters linked to their fare types. A five-year longitudinal analysis also shows the relative stability of public transport usage. [ABSTRACT FROM AUTHOR]

Smartcard and other forms of electronic ticketing have become integral to modern public transport systems. While aggregate ridership figures have generally been positive, little is known about the drivers behind these changes because of a lack of travel information on individuals before and after implementation of such systems. The current paper presents analysis from a naturalistic travel behaviour study of inner-city Sydney residents that coincided with the phased introduction of the Opal smartcard system. Using a differences-in-differences methodology, results indicate significant reductions in car use of around 10 min/day with commensurate increases in train use and incidental walking. This trend holds across income groups and is more pronounced for older residents. Results add further weight to the merits of simplifying ticket purchasing as part of a package of policy measures designed to increase public transport usage. [ABSTRACT FROM AUTHOR]

Bus arrival time is usually estimated using the boarding time of the first passenger at each station. However, boarding time data are not recorded in certain double-ticket smart card systems. As many passengers usually swipe the card much before their alighting, the first or the average alighting time cannot represent the actual bus arrival time, either. This lack of data creates difficulties in correcting bus arrival times. This paper focused on developing a model to calculate bus arrival time that combined the alighting swiping time from smart card data with the actual bus arrival time by the manual survey data. The model was built on the basis of the frequency distribution and the regression analysis. The swiping time distribution, the occupancy and the seating capacity were considered as the key factors in creating a method to calculate bus arrival times. With 1011 groups of smart card data and 360 corresponding records from a manual survey of bus arrival times, the research data were divided into two parts stochastically, a training set and a test set. The training set was used for the parameter determination, and the test set was used to verify the model’s precision. Furthermore, the regularity of the time differences between the bus arrival times and the card swiping times was analyzed using the “trend line” of the last swiping time distribution. Results from the test set achieved mean and standard error rate deviations of 0.6% and 3.8%, respectively. The proposed model established in this study can improve bus arrival time calculations and potentially support state prediction and service level evaluations for bus operations. [ABSTRACT FROM AUTHOR]

Crowding discomfort is an external cost of public transport trips imposed on fellow passengers that has to be measured in order to derive optimal supply-side decisions. This paper presents a comprehensive method to estimate the user cost of crowding in terms of the equivalent travel time loss, in a revealed preference route choice framework. Using automated demand and train location data we control for fluctuations in crowding conditions on the entire length of a metro journey, including variations in the density of standing passengers and the probability of finding a seat. The estimated standing penalty is 26.5% of the uncrowded value of in-vehicle travel time. An additional passenger per square metre on average adds 11.9% to the travel time multiplier. These results are in line with earlier revealed preference values, and suggest that stated choice methods may overestimate the user cost of crowding. As a side-product, and an important input of the route choice analysis, we derive a novel passenger-to-train assignment method to recover the daily crowding and standing probability pattern in the metro network. [ABSTRACT FROM AUTHOR]

In urban areas, public transport can improve sustainable transport by reducing vehicles and congestion, and improving accessibility. Mass rapid transit (MRT) is especially important for large cities, such as Taipei City, Taiwan. In particular, MRT aims to improve mobility and reliability, but has limitations in providing first and last mile accessibility to final destinations or origins. Taipei City, Taiwan has introduced a public bike sharing scheme to service this gap. However, few studies have addressed how a public bike sharing scheme assists first and last mile accessibility. This study uses Taipei City as a case study to investigate this issue by comparing the demand and supply of the public bike sharing scheme, YouBike, at a detailed spatial scale. The supply of YouBike is represented by the time saving compared to walking for each identified origin and destination pair in the study area (i.e., Point of Interests (POIs) and MRT stations). The demand for YouBike is total trips from each YouBike station to a MRT station using public transport smart card data. By comparing the demand and supply for over 400 zones or villages, service gaps and areas of unbalanced service can be identified. The results show that YouBike does provide first and last mile service for the MRT network with some evidence of service mismatch in the study area, i.e., high service for low demand and vice versa. The conclusions of the paper can help cities wanting to introduce a bike sharing schemes to improve first and last mile transport. [ABSTRACT FROM AUTHOR]

Bus bunching severely deteriorates the quality of transit service with poor on-time performance and excessive waiting time. To mitigate bus bunching, this paper presents a predictive framework to capture the stop-level headway irregularity based on transit smart card data. Historical headway, passenger demands, and travel time are utilized to model the headway fluctuation at the following stops. A Least Squares Support Vector Machine regression is established to detect bus bunching with the predicted headway pattern. An empirical experiment with two bus routes in Beijing is conducted to demonstrate the effectiveness of the proposed approach. The predictive method can successfully identify more than 95% of bus bunching occurrences in comparison with other well-established prediction algorithms. Moreover, the detection accuracy does not significantly deteriorate as the prediction lead time increases. Instead of regularizing the headways at all costs by adopting certain correction actions, the proposed framework can provide timely and accurate information for potential bus bunching prevention and inform passengers when the next bus will arrive. This feature will greatly increase transit ridership and reduce operating costs for transit authorities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Predictions on Public Transport (PT) ridership are beneficial as they allow for sufficient and cost-efficient deployment of vehicles. On an operational level, this relates to short-term predictions with lead times of less than an hour. Where conventional data sources on ridership, such as Automatic Fare Collection (AFC) data, may have longer lag times and contain no travel intentions, in contrast, trip planner data are often available in (near) real-time and are used before traveling. In this paper, we investigate how such data from a trip planner app can be utilized for short-term bus ridership predictions. This is combined with AFC data (in this case smart card data) to construct a ground truth on actual ridership. Using informative variables from the trip planner dataset through correlation analysis, we develop 3 supervised Machine Learning (ML) models, including k-nearest neighbors, random forest, and gradient boosting. The best-performing model relies on random forest regression with trip planner requests. Compared with the baseline model that depends on the weekly trend, it reduces the mean absolute error by approximately half. Moreover, using the same model with and without trip planner data, we prove the usefulness of trip planner data by an improved mean absolute error of 8.9% and 21.7% and an increased coefficient of determination from a 5-fold cross-validation of 7.8% and 18.5% for two case study lines, respectively. Lastly, we show that this model performance is maintained even for the trip planner requests with prediction lead times up to 30 min ahead, and for different periods of the day. We expect our methodology to be useful for PT operators to elevate their daily operations and level of service as well as for trip planner companies to facilitate passenger replanning, in particular during peak hours. • Empirical analysis of correlation in bus ridership with short-term trip planner data (advance travel advice requests). • Several interpretable machine-learning prediction models with and without trip planner data are estimated. • Two case studies with different line characteristics show trip planner data can reduce mean absolute error by 9 to 22%. • Performance of best prediction model is robust across all periods of day and lead times up to 30 min ahead. • Developed prediction methodology is applicable to both public transport operators and trip planner providers. [ABSTRACT FROM AUTHOR]

Over the past two decades, smart card data have received increasing interest from transport researchers as a new source of data for travel behaviour investigation. Collected by smart card systems, smart card data surpass traditional travel survey data in providing more comprehensive spatial–temporal information about urban public transport-based (UPT) trips. However, the utility of smart card data has arguably yet to be exploited fully in terms of extracting and exploring the spatial–temporal dynamics of UPT passenger travel behaviour. To advance previous work in this area, this paper demonstrates a multi-step methodology in order to render more insightful spatial–temporal patterns of UPT passenger travel behaviour. Drawing on the Brisbane, Australia, bus network as the case study, a smart card dataset was first processed in combination with General Transit Specification Feed (GTFS) data to reconstruct travel trajectories of bus passengers at bus stop level of spatial granularity. By applying geographical information system-based (GIS) techniques, this dataset was used to create flow-comaps to visualise the aggregate flow patterns at a network level. The flow-comaps uncovered the major pathways of bus passengers and its variations over a one-day period. The differences within the flow-comaps were also quantified to produce weighted flow-comaps that highlighted the major temporal changes of passenger flow patterns along a number of stop-to-stop linkages of the bus network. The proposed methodology visually unveiled the spatial–temporal travel behaviour dynamics of UPT passengers and, in doing so, showed the potential to contribute to a new evidence base with the capacity to inform local public transport policy. [ABSTRACT FROM AUTHOR]

Abstract: MIFARE Classic is a contactless smart card which is widely used in several public transport systems. The researchers had presented different methods to clone a card in a practical card-only scenario. Among them, they recover the second or subsequent sector key by trying to accurately estimate the time information between two consecutive authentication attempts in a nested authentication. In this paper, we study the security of the MIFARE Classic in another practical scenario, where the adversary only communicates with a legitimate reader. The worst scenario to recover the second or subsequent sector key in a nested authentication only requires about 8 authentication attempts to the legitimate reader on average and the off-line search in about 328 s on Garcia’s ordinary computer without estimating the time information between two consecutive authentications. Following this result, it is possible for the attackers to simulate or forge a legal card to authenticate successfully with a legitimate reader. To avoid this weakness, the reader must verify some information on the legal card at the beginning and it requires to be protected in some sense. [Copyright &y& Elsevier]

Abstract: The potential of smart-card data for measuring the variability of urban public transit network use is the focus of this paper. Data collected during 277 consecutive days of travel on a Canadian transit network are processed for this purpose. The organization of data using an object-oriented approach is discussed. Then, measures of spatial and temporal variability of transit use for various types of card are defined and estimated using the data sets presented. Data mining techniques are also used to identify transit use cycles and homogenous days and weeks of travel among card segments and at various times of the year. [Copyright &y& Elsevier]

Smart card (SC) data has become one of the major data sources for transit passengers' behavior analysis, network modeling, and control optimization. Origin–destination (O–D) estimation has been recognized as a requisite step before utilizing the smart card data to investigate transit passengers' spatiotemporal dynamics or conduct other SC data-based transit modeling. In the recent decade, the extant literature has proposed various trip-chain-based methods for transit O-D estimation using SC data. However, one problem of the conventional trip-chaining estimation approach has been noticed but not paid enough attention to: O-D estimation of single transactions cannot be conducted since the trip-chain method generally requires at least two trip records per day to proceed with. Such a flaw in the classic trip-chain approach might lead to a considerable amount of data loss and inaccurate O-D estimation. This paper improved the existing trip-chain O-D estimation method by introducing a new framework based on the Minimum Entropy Rate (MER) criterion. The proposed MER-based method adopts a similar mechanism of noise reduction in information theory. The basic idea of our approach is to infer the alighting location of single trips using alternative stops that preserve passengers' travel regularity exhibiting in their mobility sequences. Our enhanced approach can estimate alighting stops for single trips with decent accuracy, thus preventing a potential massive data loss. Moreover, the study also provides an in-depth insight into the relationship between entropy rates estimated using trip sequences and passengers' travel regularity. The estimation results can further benefit future transit studies with reliable data sources. • Improving the classic trip-chain method by leveraging the travel regularity information of transit passengers. • Inferring the alighting of single transactions usually excluded in previous studies. • Investigating the factors influencing the estimation accuracy of single trips. • Developing scalable algorithms to estimate entropy rates directly from mobility sequences. [ABSTRACT FROM AUTHOR]

• Developing a novel approach for using trip sequences instead of separate trips. • Developing a unique similarity measure for the trip sequences. • Focusing only on the temporal attributes of the trip sequences. • Running the method on real-world case studies. • Evaluating the performance of the developed method. • Comparing the outcome with the existing literature. • Achieving a considerable improvement in inferring the trip purpose. Smart card transactions are known as a rich and continuous source of public transit data, but they miss some important attributes about trips and passengers. One of these missing attributes is the trip purpose attribute. This paper proposes a novel method to infer the trip purpose attribute from the sequences of trips of passengers instead of separate trips. The proposed method infers the trip purpose attribute (a missing attribute in the smart card data) from the temporal attributes (available attributes in the smart card data). First, the relation between the temporal attributes and the trip purpose attribute is learnt by discovering clusters of passengers in the Household Travel Survey dataset while each passenger is represented by one sequence of trips. Then, the discovered clusters are utilized to infer the trip purpose of smart card transactions by allocating each passenger to the closest clusters. The proposed method is implemented on the smart card and HTS datasets from southeast Queensland, Australia. The evaluation results showed a considerable improvement in inferring the trip purpose compared to the results published in the literature. Notably, the effect of considering the trip sequence was more significant than considering land use variables. [ABSTRACT FROM AUTHOR]

Many studies have evaluated the influence of the built environment on public transport. Some studies assign subjective weights to environmental factors, which could oversimplify spatial heterogeneity and overlook the temporal dimension. On the other hand, the spatial-interaction network of public transport system is seldom considered. In this paper, we propose an improved framework to explore how individual factors unevenly affect public transport demand over space and time using a geographically and temporally weighted regression (GTWR) model. The proposed framework extends the local built environmental factors by including two network factors extracted from the spatial-interaction network of the public transport system. We conduct a case study in Beijing, China using 686 traffic analysis zones (TAZs). The actual usage of public transport, namely the public transport index (PTI), is estimated by passenger flow divided by the total amount of human flow in a given TAZ. The daily patterns of the spatial heterogeneity in some selected places in the study area is identified and analyzed. It is also found that the estimated coefficient of the variables of the spatial-interaction network is significantly larger than other static environmental factors, indicating that spatial-interaction network can more effectively reflect spatiotemporal heterogeneity in public transport demand. This study provides a better decision-making support for more accurately identifying which factors are most worthy of development, and when and where they can be implemented to improve public transit services. [ABSTRACT FROM AUTHOR]

Understanding the travel behaviors and activity patterns of vulnerable people is important for addressing social equity in urban and transportation planning. With the increasing availability of large-scale individual tracking data, new opportunities have emerged for studying people's travel behaviors and activity patterns. However, the data has not been fully exploited to examine the travel characteristics of vulnerable people and their implications for understanding transport-related disadvantage. This study proposes a methodological framework based on the concept of activity space that enables a comprehensive examination of vulnerable people's spatiotemporal travel characteristics and an investigation of the geographies of transport disadvantage. Using the proposed framework, a case study that investigates the bus activities of the vulnerable population using four-month smart card data is carried out in the city of Wuhu, China. The case study suggests that vulnerable people possess distinct travel behaviors that differ considerably from the mainstream population and that the implications of transport disadvantage, as revealed by the participation in bus activities, vary across different demographic groups and across different spatial contexts. Some of the empirical insights obtained from this study also differ from conclusions drawn from previous studies and will enrich our understandings of vulnerable people's activities. Overall, the paper makes two major contributions. Methodologically, the proposed framework can overcome some of the deficiencies of activity space-based approaches for understanding transport disadvantage and contribute broadly to the studies of travel behaviors and activities patterns using individual-level tracking data. Empirically, the study identifies varying spatial and temporal implications of transport disadvantage associated with different vulnerable groups, which could further shed light on public transit planning and service design. [ABSTRACT FROM AUTHOR]

• Proposing a framework to analyze emission patterns of buses and changes in post-COVID-19. • 2056 buses with 1.5 million ridership and 7589 taxis with 0.2 million trips are used for analysis. • 224 social surveys are collected and show a 56.3% ridership reduction in post-COVID-19. • We find buses cannot be "greener" travel modal than cars if ridership reduces by more than 40%. Emission benefits of transit buses depend on ridership. Declines in ridership caused by COVID-19 leads uncertainty about the emission reduction capacity of buses. This paper provides a method framework for analyzing spatio-temporal emission patterns of buses in combination with real-time ridership and potential emission changes in the post-COVID-19 future. Based on GPS trajectory and Smart Card data of 2056 buses from 278 routes covering 1.5 million ridership in Qingdao, China, spatio-temporal emissions characteristics of buses are studied. 7589 taxis with 0.2 million passengers' trips are used for acquiring private cars' emissions to evaluate the emissions difference between buses and cars. Empirical results show that the average difference between buses and cars with 2 persons can reach up to 117 g/km-person during 7:00–8:59 and 115 g/km-person during 17:00–18:59. However, buses have various emission benefits around the city at different periods. A double increase in emissions during non-rush hours can be observed compared with rush hours. 224 online survey data are used to study the potential ridership reduction trend in post-COVID-19. Results show that 56.3% of respondents would decrease the usage of buses in the post-COVID-19 future. Based on this figure, our analysis shows that per kilometer-person emissions of buses are higher than cars during non-rush hours, however, still lower than cars during rush hours. We conclude that when ridership reduces by more than 40%, buses cannot be "greener" travel modal than cars as before. Finally, several feasible policies are suggested for this potential challenge. Our study provides convincing evidence for understanding the emission patterns of buses, to support better buses investment decisions and promotion on eco-friendly public transport service in the post-COVID-19 future. [ABSTRACT FROM AUTHOR]

Automated Fare Collection (AFC) systems in rail transit services collect enormous amounts of detailed data on on-board transactions. A better understanding of travelers' commuting and transfer behavior based on those massive volumes of AFC data would enable the rail service operators to evaluate their service quality and optimize operation strategies. This paper proposes an efficient and effective data mining procedure to figure out the association rules, aiming to extract connectivity and correlation of passenger flow among different services lines in urban rail transit networks. A case study based on the Beijing Subway network is conducted to demonstrate the applicability of the proposed method. Using up to 28 million AFC smart card transaction data, we match and analyze travelers' trip chains to investigate the commuting trip patterns in terms of spatio-temporal distribution characteristics. An innovational non-nigh-to-five commuting behavior and traditional nine-to-five commuting behavior are divided by the obtained associated rules to ensure a more nuanced description of commuting behaviors. Further, the results indicated by stronger association rules (2-frequent itemset and 3-frequent itemset) also provide a better understanding of transfer behaviors, like the frequent transfers among different service lines, and potentially vulnerable stations in the network. The research outcomes can be used to assist rail transit service operators in developing optimal operation strategies like timetabling design to enhance the transfer performance between different rail lines. • An efficient and effective data mining procedure is developed to figure out the association rules, aiming to extract connectivity and correlation of passenger flow among different services lines. • An innovational non-nigh-to-five commuting behavior and traditional nine-to-five commuting behavior are divided by the obtained associated rules to ensure a more nuanced description of commuting behaviors. • A case study based on the Beijing Subway network is conducted to demonstrate the applicability of the proposed method. • Experimental results indicated by strong association rules provide a better understanding of transfer coordination in urban rail transit networks. [ABSTRACT FROM AUTHOR]

• Developing a method based on Temporal Motif to identify individual travel patterns. • Inferring the correlation between travel-activity chains and different travel motifs. • Extracted individual travel motifs provide extra information for trip prediction. • Bridging the concepts of travel regularity and temporal network complexity. In this paper, we proposed a new method to extract travel patterns for transit riders from different public transportation systems based on temporal motif, which is an emerging notion in network science literature. We then developed a scalable algorithm to recognize temporal motifs from daily trip sub-sequences extracted from two smart card datasets. Our method shows its benefits in uncovering the potential correlation between varying topologies of trip combinations and specific activity chains. Commuting, different types of transfer, and other travel behaviors have been identified. Besides, varying travel-activity chains like "Home → Work → Post-work activity (for dining or shopping) → Back home" and the corresponding travel motifs have been distinguished by incorporating the land use information in the GIS data. The analysis results contribute to our understanding of transit riders' travel behavior. We also present application examples of the travel motif to demonstrate the practicality of the proposed approach. Our methodology can be adapted to travel pattern analysis using different data sources and lay the foundation for other travel-pattern related studies. [ABSTRACT FROM AUTHOR]

Massive electronic trip records have recently been utilized to infer people's trips for healthcare. Many inferential methods were developed to derive healthcare trips by taxi using GPS trajectory records, but little attention is paid to public transit, as a common travel mode for healthcare. This paper proposes a method to fill this gap by mining a big data of smart transit cards with spatio-temporal constraints. We demonstrate and validate this method in Beijing, China. The inferred trips achieve a high degree of consistency, in space and time, with empirically observed trips from a survey. The inferred trips are further used to identify spatial disparities in transit-based access to healthcare, which might have been overlooked by health policy makers. [ABSTRACT FROM AUTHOR]

Geodemographics, providing the information of population's characteristics in the regions on a geographical basis, is of immense importance in urban studies, public policy-making, social research and business, among others. Such data, however, are difficult to collect from the public, which is usually done via census, with a low update frequency. In urban areas, with the increasing prevalence of public transit equipped with automated fare payment systems, researchers can collect massive transit smart card (SC) data from a large population. The SC data record human daily activities at an individual level with high spatial and temporal resolutions. It can reveal frequent activity areas (e.g., residential areas) and travel behaviours of passengers that are intimately intertwined with personal interests and characteristics. This provides new opportunities for geodemographic study. This paper seeks to develop a framework to infer travellers' demographics (such as age, income level and car ownership, et al.) and their residential areas for geodemographic mapping using SC data with a household survey. We first use a decision tree diagram to detect passengers' residential areas. We then represent each individual's spatio-temporal activity pattern derived from multi-week SC data as a 2D image. Leveraging this representation, a multi-task convolutional neural network (CNN) is employed to predict multiple demographics of individuals from the images. Combing the demographics and locations of their residence, geodemographic information is further obtained. The methodology is applied to a large-scale SC dataset provided by Transport for London. Results provide new insights in understanding the relationship between human activity patterns and demographics. To the best of our knowledge, this is the first attempt to infer geodemographics by using the SC data. • We propose a novel framework for geodemographic prediction using smart card data. • A decision tree diagram is proposed for residential area detection from smart card data. • We represent the smart card data as 2D images and propose a multi-task CNN model for demographic prediction. • Geodemographic mapping can be produced by integrating the identified home location and inferred demographics. • An empirical study using Oyster card data in Greater London, UK validates the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Accessibility metrics are gaining momentum in public transportation planning and policy-making. However, critical user experience issues such as crowding discomfort and travel time unreliability are still not considered in those accessibility indicators. This paper aims to apply a methodology to build spatiotemporal crowding data and estimate travel time variability in a congested public transport network to improve accessibility calculations. It relies on using multiple big data sources available in most transit systems such as smart card and automatic vehicle location (AVL) data. São Paulo, Brazil, is used as a case study to show the impact of crowding and travel time variability on accessibility to jobs. Our results evidence a population-weighted average reduction of 56.8% in accessibility to jobs in a regular workday morning peak due to crowding discomfort, as well as reductions of 6.2% due to travel time unreliability and 59.2% when both are combined. The findings of this study can be of invaluable help to public transport planners and policymakers, as they show the importance of including both aspects in accessibility indicators for better decision making. Despite some limitations due to data quality and consistency throughout the study period, the proposed approach offers a new way to leverage big data in public transport to enhance policy decisions. • Smart card big data is explored to reveal spatiotemporal crowding patterns; • Multiple days of data are analyzed to infer travel time variability; • Longitudinal and cross-sectional analyses are done to find regularity in travel patterns; • Crowding discomfort and travel time variability on accessibility metrics are considered; • Policy implications of crowding valuation in accessibility are discussed. [ABSTRACT FROM AUTHOR]