Aggregated Metro Trip Patterns in Urban Areas of Hong Kong: Evidence from Automatic Fare Collection Records

AbstractThe automatic fare collection (AFC) system incorporating smart card technology is widely used in transportation systems for revenue management, but the data from this could be mined for wide-ranging applications. This paper used AFC data of eight metro stations in the central area of Hong Kong to analyze metro trip patterns at an aggregate level. Based on the ridership differences between days, time periods, and directed flows, empirical data were categorized into three groups to better understand the station area characteristics. Compared to previous studies, it was found that factors may play varied roles in determining trip quantities for divergent station areas. Shopping and recreational factors demonstrated a statistically significant relationship with metro trips during the afternoon peak at exit flow and the evening peak at entry flow in the commercial districts of Hong Kong, such as Causeway Bay, Tsim Sha Tsui, and Mong Kok. This suggests that the distinctive context characteristics of the...

[1]  Keemin Sohn,et al.  Factors generating boardings at Metro stations in the Seoul metropolitan area , 2010 .

[2]  Cecil C. Bridges,et al.  Hierarchical Cluster Analysis , 1966 .

[3]  Jianhe Du,et al.  Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues , 2007 .

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

[5]  B. Loo,et al.  Rail-Based Transit-Oriented Development: Lessons from New York City and Hong Kong , 2010 .

[6]  K. Axhausen,et al.  Habitual travel behaviour: Evidence from a six-week travel diary , 2003 .

[7]  M. Kuby,et al.  Factors influencing light-rail station boardings in the United States , 2004 .

[8]  Javier Gutiérrez,et al.  Transit ridership forecasting at station level: an approach based on distance-decay weighted regression , 2011 .

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

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

[11]  Daniel A. Badoe,et al.  Modeling Trip Generation with Data from Single and Two Independent Cross-Sectional Travel Surveys , 2004 .

[12]  Alexandra Millonig,et al.  Classifying trip characteristics for describing routine and non-routine trip patterns , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[13]  Luis González Abril,et al.  Trip destination prediction based on past GPS log using a Hidden Markov Model , 2010, Expert Syst. Appl..

[14]  Brian Canepa,et al.  Bursting the Bubble , 2007 .

[15]  Robert Cervero,et al.  The Half-Mile Circle: Does It Best Represent Transit Station Catchments? , 2012 .

[16]  Philippe Nitsche,et al.  A Strategy on How to Utilize Smartphones for Automatically Reconstructing Trips in Travel Surveys , 2012 .

[17]  Keemin Sohn,et al.  An analysis of Metro ridership at the station-to-station level in Seoul , 2012 .

[18]  Kees Maat,et al.  Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands , 2009 .

[19]  Janet Chang,et al.  Shopping and tourist night markets in Taiwan. , 2006 .

[20]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.