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Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data

https://doi.org/10.1109/tits.2017.2679179

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Fair Fare Policies: Pricing Policies that Benefit Transit-Dependent Riders

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Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

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On detection of emerging anomalous traffic patterns using GPS data

inferredcites →https://doi.org/10.1016/j.tranpol.2005.06.008openalex-snapshot

The potential of public transport smart card data

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Measuring transit use variability with smart-card data

inferredcites →https://doi.org/10.1016/j.trc.2010.12.003openalex-snapshot

Smart card data use in public transit: A literature review

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Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile

inferredcites →https://doi.org/10.1016/j.trc.2013.07.010openalex-snapshot

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inferredcites →https://doi.org/10.1016/j.trc.2014.06.003openalex-snapshot

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A Survey of Outlier Detection Methodologies

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Development of a Data-Driven Platform for Transit Performance Measures Using Smart Card and GPS Data

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Understanding metropolitan patterns of daily encounters

inferredcites →https://doi.org/10.1080/15472450601122256openalex-snapshot

Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System

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Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems

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Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system

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Catch Me If You Can

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inferredcites →https://doi.org/10.3141/2105-01openalex-snapshot

Driver-Assisted Bus Interview

inferredcites →https://doi.org/10.3182/20060517-3-fr-2903.00211openalex-snapshot

MINING PUBLIC TRANSPORT USER BEHAVIOUR FROM SMART CARD DATA

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A Stochastic Public Transport Trip Assignment Model Estimated Using Passive Data from Santiago, Chile

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MDLF: A Multi-View-Based Deep Learning Framework for Individual Trip Destination Prediction in Public Transportation Systems

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GLTC: A Metro Passenger Identification Method Across AFC Data and Sparse WiFi Data

inferred← citeshttps://doi.org/10.1109/tits.2022.3179024openalex-snapshot

PAC: Partial Area Clustering for Re-Adjusting the Layout of Traffic Stations in City’s Public Transport

inferred← citeshttps://doi.org/10.1109/tits.2022.3181381openalex-snapshot

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

inferred← citeshttps://doi.org/10.1109/tits.2023.3276704openalex-snapshot

Predicting Urban Region Heat via Learning Arrive-Stay-Leave Behaviors of Private Cars

inferred← citeshttps://doi.org/10.1109/tits.2024.3475295openalex-snapshot

An Ensemble of Deep Clustering Models With Autoencoders to Mine Travel Patterns From Smart Card Data

inferred← citeshttps://doi.org/10.1109/tkde.2020.2985952openalex-snapshot

Online Spatio-Temporal Crowd Flow Distribution Prediction for Complex Metro System

inferred← citeshttps://doi.org/10.1109/tkde.2022.3187690openalex-snapshot

STP-TrellisNets+: Spatial-Temporal Parallel TrellisNets for Multi-Step Metro Station Passenger Flow Prediction

inferred← citeshttps://doi.org/10.1109/tnse.2020.3025529openalex-snapshot

An Empirical Study of Travel Behavior Using Private Car Trajectory Data

inferred← citeshttps://doi.org/10.1111/tgis.12665openalex-snapshot

Analysis of passenger flow characteristics and their relationship with surrounding urban functional landscape pattern

inferred← citeshttps://doi.org/10.1134/s2079970526600046openalex-snapshot

Spatial Patterns of Long-Term Passenger Flow Dynamics at Moscow Metro Stations

inferred← citeshttps://doi.org/10.1145/3184558.3186356openalex-snapshot

Activity-based Mobility Profiling

inferred← citeshttps://doi.org/10.1145/3269206.3271757openalex-snapshot

Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization

inferred← citeshttps://doi.org/10.1145/3284557.3284565openalex-snapshot

Mining Spatial-Temporal Travel Patterns from Highway Transaction Data

inferred← citeshttps://doi.org/10.1145/3340531.3411874openalex-snapshot

STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction

inferred← citeshttps://doi.org/10.1145/3558521openalex-snapshot

CrowdAtlas: Estimating Crowd Distribution within the Urban Rail Transit System

inferred← citeshttps://doi.org/10.1145/3615886.3627749openalex-snapshot

Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering

inferred← citeshttps://doi.org/10.1155/2019/3689389openalex-snapshot

Analyses of the Imbalance of Urban Taxis’ High-Quality Customers Based on Didi Trajectory Data

inferred← citeshttps://doi.org/10.1155/2020/8810753openalex-snapshot

Simultaneous Incomplete Traffic Data Imputation and Similarity Pattern Discovery with Bayesian Nonparametric Tensor Decomposition

inferred← citeshttps://doi.org/10.1155/2022/2271650openalex-snapshot

[Retracted] Analysis of Nostalgia Emotional Experience, Rural Tourism Environment Cognition, and Consumption Willingness Based on Big Data Technology

inferred← citeshttps://doi.org/10.1155/2022/7096153openalex-snapshot

A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events

inferred← citeshttps://doi.org/10.1155/2023/2206625openalex-snapshot

Introducing Autonomous Shuttle Services Based on Travel Patterns for the Elderly

inferred← citeshttps://doi.org/10.1155/2023/3463330openalex-snapshot

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

inferred← citeshttps://doi.org/10.1155/2023/6529819openalex-snapshot

Identification and Classification of Bus and Subway Passenger Travel Patterns in Beijing Using Transit Smart Card Data

inferred← citeshttps://doi.org/10.11606/d.3.2018.tde-29032022-100650openalex-snapshot

Analysis of travel patterns from precarious settlements transit users in São Paulo through smart card data mining.

inferred← citeshttps://doi.org/10.11606/d.3.2024.tde-10072024-082128openalex-snapshot

Análises espaço-temporais de viagens por transporte público a partir de matriz Origem-Destino gerada por meio de bilhetagem eletrônica.

inferred← citeshttps://doi.org/10.1177/03611981211047835openalex-snapshot

Identifying Subway Passenger Flow under Large-Scale Events Using Symbolic Aggregate Approximation Algorithm

inferred← citeshttps://doi.org/10.1177/03611981231152256openalex-snapshot

User Segmentation Based on Travel Regularity in E-Scooter Sharing Service

inferred← citeshttps://doi.org/10.1186/s12544-019-0365-5openalex-snapshot

Combining ITS and optimization in public transportation planning: state of the art and future research paths

inferred← citeshttps://doi.org/10.1371/journal.pone.0342450openalex-snapshot

A refined maximum predictability for next location prediction with fusion knowledge

inferred← citeshttps://doi.org/10.1680/jtran.21.00066openalex-snapshot

Modelling public transportation multi-modal route choice using smart card big data

inferred← citeshttps://doi.org/10.21203/rs.3.rs-1972527/v1openalex-snapshot

Spatiotemporal trip pattern analysis in a regional bus network with limited automated fare collection data: a case study of the Pioneer Valley

inferred← citeshttps://doi.org/10.21203/rs.3.rs-6349757/v1openalex-snapshot

A data-driven approach to inferring travel trajectory during peak hours in urban rail transit systems

inferred← citeshttps://doi.org/10.2139/ssrn.6000334openalex-snapshot

Clustering-Based Behavioral Analysis of Transit Agency Mobile Ticketing Data

inferred← citeshttps://doi.org/10.22214/ijraset.2023.50349openalex-snapshot

A Research Paper on Smart Authentication System for Identity Verification

inferred← citeshttps://doi.org/10.33564/ijeast.2022.v07i07.023openalex-snapshot

A SURVEY PAPER ON SMART AUTHENTICATION SYSTEM FOR IDENTITY VERIFICATION

inferred← citeshttps://doi.org/10.3390/app12083867openalex-snapshot

A Deterministic Methodology Using Smart Card Data for Prediction of Ridership on Public Transport

inferred← citeshttps://doi.org/10.3390/e26050388openalex-snapshot

A Spatiotemporal Probabilistic Graphical Model Based on Adaptive Expectation-Maximization Attention for Individual Trajectory Reconstruction Considering Incomplete Observations

inferred← citeshttps://doi.org/10.3390/electronics11010106openalex-snapshot

Travel Time Prediction and Explanation with Spatio-Temporal Features: A Comparative Study

inferred← citeshttps://doi.org/10.3390/en13102670openalex-snapshot

Passenger Travel Patterns and Behavior Analysis of Long-Term Staying in Subway System by Massive Smart Card Data

inferred← citeshttps://doi.org/10.3390/ijgi11010018openalex-snapshot

Measuring the Similarity of Metro Stations Based on the Passenger Visit Distribution

inferred← citeshttps://doi.org/10.3390/ijgi8010023openalex-snapshot

Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression

inferred← citeshttps://doi.org/10.3390/ijgi8060271openalex-snapshot

Who, Where, Why and When? Using Smart Card and Social Media Data to Understand Urban Mobility

inferred← citeshttps://doi.org/10.3390/ijgi8100434openalex-snapshot

Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

inferred← citeshttps://doi.org/10.3390/ijgi8100440openalex-snapshot

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

inferred← citeshttps://doi.org/10.3390/ijgi9110651openalex-snapshot

Exploring Travel Patterns during the Holiday Season—A Case Study of Shenzhen Metro System During the Chinese Spring Festival

inferred← citeshttps://doi.org/10.3390/info9010018openalex-snapshot

Smart Card Data Mining of Public Transport Destination: A Literature Review

inferred← citeshttps://doi.org/10.3390/land13020238openalex-snapshot

Regional Policies, Practices, Tools, and Strategies to Implement Polycentric Development: Comparative Case Studies of Portland, Seattle, and Denver

inferred← citeshttps://doi.org/10.3390/math11081788openalex-snapshot

Research on Relative Threshold of Abnormal Travel in Subway Based on Bilateral Curve Fitting

inferred← citeshttps://doi.org/10.3390/proceedings2019031025openalex-snapshot

Data Framework for Road-Based Mass Transit Systems Data Mining Project

inferred← citeshttps://doi.org/10.3390/s22010017openalex-snapshot

Data-Driven Performance Evaluation Framework for Multi-Modal Public Transport Systems

inferred← citeshttps://doi.org/10.3390/su10103489openalex-snapshot

Smart Card Data Mining to Analyze Mobility Patterns in Suburban Areas

inferred← citeshttps://doi.org/10.3390/su10124725openalex-snapshot

Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway

inferred← citeshttps://doi.org/10.3390/su11051429openalex-snapshot

Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles

inferred← citeshttps://doi.org/10.3390/su12041475openalex-snapshot

Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China

inferred← citeshttps://doi.org/10.3390/su12062574openalex-snapshot

A Method to Estimate URT Passenger Spatial-Temporal Trajectory with Smart Card Data and Train Schedules

inferred← citeshttps://doi.org/10.3390/su14127234openalex-snapshot

Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021

inferred← citeshttps://doi.org/10.3390/su14137918openalex-snapshot

Optimization Method for Conventional Bus Stop Placement and the Bus Line Network Based on the Voronoi Diagram

inferred← citeshttps://doi.org/10.3390/su14159564openalex-snapshot

Spatiotemporal Evolution of Travel Pattern Using Smart Card Data

inferred← citeshttps://doi.org/10.3390/su14159666openalex-snapshot

How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method

inferred← citeshttps://doi.org/10.3390/su15107891openalex-snapshot

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

inferred← citeshttps://doi.org/10.3390/su18178635openalex-snapshot

Unraveling Bus Passenger OD Flows Through the Lens of Industrial Spatial Structure with Proximity and Scarcity Metrics: A Case Study of Beijing, China

inferred← citeshttps://doi.org/10.3390/urbansci10070392openalex-snapshot

Analyzing Night Shift Workers’ Commute Burden and Nighttime Mobility Patterns: A Case Study in Baltimore

inferred← citeshttps://doi.org/10.4467/2543859xpkg.20.010.12112openalex-snapshot

Przestrzenne i czasowe zróżnicowanie natężenia potoków pasażerskich w kolejowych połączeniach dojazdowych do Wrocławia

inferred← citeshttps://doi.org/10.46855/energy-proceedings-7350openalex-snapshot

Potential taxi ride-sharing analysis based on GPS trajectory data: a case of Qingdao

inferred← citeshttps://doi.org/10.46855/energy-proceedings-7430openalex-snapshot

Potential taxi ride-sharing analysis based on GPS trajectory: a case of Qingdao

inferred← citeshttps://doi.org/10.48084/etasr.8899openalex-snapshot

Clustering Commuter Behavior based on Automated Fare Collection (AFC)

inferred← citeshttps://doi.org/10.53846/goediss-8347openalex-snapshot

User Attribute Inference via Mining User-Generated Data

inferred← citeshttps://doi.org/10.59543/comdem.v2i.11363openalex-snapshot

Data-driven Analysis of Taxi and Ride-hailing Services: Case Study in Chengdu, China

inferred← citeshttps://doi.org/10.70675/4639f245z850fz45caz9f7fz3a88269fac35openalex-snapshot

Forecast and anomaly detection on time series with dynamic context : Application to the mining of transit ridership data

inferred← citeshttps://doi.org/10.7163/przg.2021.4.3openalex-snapshot

Przewozy pasażerskie w regionalnym systemie transportu kolejowego w województwie dolnośląskim w 2018 r. = Spatial distribution of passenger flows in a regional system of rail transport. A case study involving Poland’s Dolnośląskie Voivodeship

inferred← citeshttps://doi.org/10.7307/ptt.v32i1.3052openalex-snapshot

Understanding Daily Travel Patterns of Subway Users – An Example from the Beijing Subway

inferred← citeshttps://doi.org/10.7708/ijtte.2021.11(3).09openalex-snapshot

PRELIMINARY INVESTIGATION OF BOARDING BEHAVIORS OF TRANSIT USERS: A CASE OF IZMIRS’S INTEGRATED FARE SYSTEM

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.