Application of gravity model on the Korean urban bus network

Mobility models have been studied to describe the underlying mechanism of human mobility. The mobility patterns in various transportation systems were understood with the gravity model by estimating the traffic as a simple function of population and distance. Compared to most studies on large-scale systems, we focused on the validity and characteristics of gravity model for intraurban mobility. Several variations of gravity model are applied on the urban bus systems of five medium-sized cities in Korea. The gravity model successfully estimates the intraurban traffic without universal exponents for cities. From the change of exponents by predictor types, we figure out the effect by a non-trivial relation between traffic and population in the urban areas.

[1]  M. Batty,et al.  Gravity versus radiation models: on the importance of scale and heterogeneity in commuting flows. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  N. F. Stewart,et al.  The Gravity Model in Transportation Analysis - Theory and Extensions , 1990 .

[3]  Yanguang Chen,et al.  The distance-decay function of geographical gravity model: Power law or exponential law? , 2015 .

[4]  Segun Goh,et al.  Emergence of Criticality in the Transportation Passenger Flow: Scaling and Renormalization in the Seoul Bus System , 2014, PloS one.

[5]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[6]  Sang Hoon Lee,et al.  Matchmaker, Matchmaker, Make Me a Match: Migration of Populations via Marriages in the Past , 2013, bioRxiv.

[7]  F. Calabrese,et al.  Urban gravity: a model for inter-city telecommunication flows , 2009, 0905.0692.

[8]  Jari Saramäki,et al.  Inferring human mobility using communication patterns , 2014, Scientific Reports.

[9]  Wen-Xu Wang,et al.  Universal predictability of mobility patterns in cities , 2013, Journal of The Royal Society Interface.

[10]  M. Barthelemy,et al.  Modeling the polycentric transition of cities. , 2013, Physical review letters.

[11]  Santo Fortunato,et al.  World citation and collaboration networks: uncovering the role of geography in science , 2012, Scientific Reports.

[12]  Taha Yasseri,et al.  The distorted mirror of Wikipedia: a quantitative analysis of Wikipedia coverage of academics , 2013, EPJ Data Science.

[13]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[14]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[15]  Carlo Ratti,et al.  The impact of social segregation on human mobility in developing and industrialized regions , 2014, EPJ Data Science.

[16]  M. Y. Choi,et al.  Modification of the gravity model and application to the metropolitan Seoul subway system. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Petter Holme,et al.  Predictability of population displacement after the 2010 Haiti earthquake , 2012, Proceedings of the National Academy of Sciences.

[18]  Alessandro Vespignani,et al.  The role of the airline transportation network in the prediction and predictability of global epidemics , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Alessandro Vespignani,et al.  Multiscale mobility networks and the spatial spreading of infectious diseases , 2009, Proceedings of the National Academy of Sciences.

[20]  Chaoming Song,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[21]  Nathan Eagle,et al.  Limits of Predictability in Commuting Flows in the Absence of Data for Calibration , 2014, Scientific Reports.

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

[23]  Woo-Sung Jung,et al.  Intercity express bus flow in Korea and its network analysis , 2012 .

[24]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[25]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[26]  Zoltán Toroczkai,et al.  Predicting commuter flows in spatial networks using a radiation model based on temporal ranges , 2014, Nature Communications.

[27]  G. Zipf.  The P 1 P 2 D Hypothesis: On the Intercity Movement of Persons , 1946 .

[28]  K. Kaski,et al.  The International Trade Network: weighted network analysis and modelling , 2007, 0707.4343.

[29]  Xiao Liang,et al.  Unraveling the origin of exponential law in intra-urban human mobility , 2012, Scientific Reports.

[30]  T. Sørensen,et al.  A method of establishing group of equal amplitude in plant sociobiology based on similarity of species content and its application to analyses of the vegetation on Danish commons , 1948 .

[31]  Michael T. Gastner,et al.  The complex network of global cargo ship movements , 2010, Journal of The Royal Society Interface.

[32]  H. Stanley,et al.  Gravity model in the Korean highway , 2007, 0710.1274.

[33]  D. Helbing.  Traffic and related self-driven many-particle systems , 2000, cond-mat/0012229.

[34]  D. Helbing,et al.  The Hidden Geometry of Complex, Network-Driven Contagion Phenomena , 2013, Science.