Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro

This paper contributes to the emerging applications of automatically collected data in revealing the aggregate patterns of passenger flows and monitoring system performance from the passengers’ perspective. The paper’s main objectives are to (1) analyze passenger flow characteristics and (2) evaluate travel time reliability for the Shanghai Metro network by visualizing the automatic fare collection (AFC) data. First, key characteristics of passenger flows are identified by examining three major aspects, namely, spatial distribution of trips over the network, temporal distribution of passenger entries at the line level and station inflow/outflow imbalances. Second, travel time reliability analyses from the users’ perspective are performed, after a new metric of travel time reliability is designed. Comparisons of travel time reliability at the OD level are provided and the network reliabilities across multiple periods are also evaluated. Thus, this paper provides a comprehensive and holistic view of passenger travel experiences. Although the case study focuses on Shanghai Metro, the same analysis framework can be applied to other transit networks equipped with similar AFC systems.

[1]  Mickaël Schil.  Measuring journey time reliability in London using automated data collection systems , 2012 .

[2]  Nelson Carrasco.  Quantifying Reliability of Transit Service in Zurich, Switzerland , 2012 .

[3]  Le Minh Kieu,et al.  Public Transport Travel-Time Variability Definitions and Monitoring , 2015 .

[4]  Gaël Varoquaux,et al.  Mayavi: 3D Visualization of Scientific Data , 2010, Computing in Science & Engineering.

[5]  Raja Sengupta,et al.  Quantifying transit travel experiences from the users’ perspective with high-resolution smartphone and vehicle location data: Methodologies, validation, and example analyses , 2015 .

[6]  Xiaolei Ma,et al.  Development of a Data-Driven Platform for Transit Performance Measures Using Smart Card and GPS Data , 2014 .

[7]  Jinhua Zhao,et al.  Estimating a Rail Passenger Trip Origin‐Destination Matrix Using Automatic Data Collection Systems , 2007, Comput. Aided Civ. Infrastructure Eng..

[8]  Catherine Morency,et al.  Smart card data use in public transit: A literature review , 2011 .

[9]  Takamasa Iryo,et al.  Estimation method for railway passengers’ train choice behavior with smart card transaction data , 2010 .

[10]  Francisco C. Pereira,et al.  Methods for pre-processing smartcard data to improve data quality , 2014 .

[11]  Alex Lu,et al.  Algorithm to Measure Daily Bus Passenger Miles Using Electronic Farebox Data for National Transit Database Section 15 Reporting , 2011 .

[12]  Alex Cui.  Bus passenger Origin-Destination Matrix estimation using Automated Data Collection systems , 2006 .

[13]  Robert R. Sokal,et al.  Significance Tests for Coefficients of Variation and Variability Profiles , 1980 .

[14]  Patricia Hendren,et al.  Transit Travel Time Reliability , 2015 .

[15]  David Louis Uniman,et al.  Service reliability measurement framework using smart card data : application to the London Underground , 2009 .

[16]  Ka Kee Alfred Chu,et al.  Enriching Archived Smart Card Transaction Data for Transit Demand Modeling , 2008 .

[17]  Yanshuo Sun,et al.  Rail Transit Travel Time Reliability and Estimation of Passenger Route Choice Behavior , 2012 .

[18]  Adam Rahbee.  Farecard Passenger Flow Model at Chicago Transit Authority, Illinois , 2008 .

[19]  Paul Schonfeld,et al.  Schedule-Based Rail Transit Path-Choice Estimation using Automatic Fare Collection Data , 2016 .

[20]  Wonjae Jang,et al.  Travel Time and Transfer Analysis Using Transit Smart Card Data , 2010 .

[21]  Mark Hickman,et al.  Trip purpose inference using automated fare collection data , 2014, Public Transp..

[22]  Adam Rahbee,et al.  Origin and Destination Estimation in New York City with Automated Fare System Data , 2002 .

[23]  J. Corcoran,et al.  Exploring Bus Rapid Transit passenger travel behaviour using big data , 2014 .

[24]  Arnab Majumdar,et al.  Assessing the Impact of Travel Path Choice on London's Rail Network Using an Automatic Fare Collection System , 2012 .

[25]  Robert L. Bertini,et al.  Using Travel Time Reliability Measures to Improve Regional Transportation Planning and Operations , 2008 .

[26]  Harilaos N. Koutsopoulos,et al.  Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data , 2013 .

[27]  Ka Kee Alfred Chu,et al.  Augmenting Transit Trip Characterization and Travel Behavior Comprehension , 2010 .

[28]  Xiaolei Ma,et al.  Mining smart card data for transit riders’ travel patterns , 2013 .

[29]  Graham Currie,et al.  Investigating Consistency in Transit Passenger Arrivals , 2008 .

[30]  Nigel H. M. Wilson,et al.  Unified estimator for excess journey time under heterogeneous passenger incidence behavior using smartcard data , 2013 .

[31]  Steven J. M. Jones,et al.  Circos: an information aesthetic for comparative genomics. , 2009, Genome research.

[32]  Ben Shneiderman,et al.  Network Visualization by Semantic Substrates , 2006, IEEE Transactions on Visualization and Computer Graphics.

[33]  Tamara Munzner,et al.  Visualization Analysis and Design , 2014, A.K. Peters visualization series.

[34]  Hesham Rakha,et al.  Trip Travel-Time Reliability: Issues and Proposed Solutions , 2010, J. Intell. Transp. Syst..

[35]  H. J. Van Zuylen,et al.  Monitoring and Predicting Freeway Travel Time Reliability: Using Width and Skew of Day-to-Day Travel Time Distribution , 2005 .