Spatio-temporal autocorrelation of road network data

Modelling autocorrelation structure among space–time observations is crucial in space–time modelling and forecasting. The aim of this research is to examine the spatio-temporal autocorrelation structure of road networks in order to determine likely requirements for building a suitable space–time forecasting model. Exploratory space–time autocorrelation analysis is carried out using journey time data collected on London’s road network. Through the use of both global and local autocorrelation measures, the autocorrelation structure of the road network is found to be dynamic and heterogeneous in both space and time. It reveals that a global measure of autocorrelation is not sufficient to explain the network structure. Dynamic and local structures must be accounted for space–time modelling and forecasting. This has broad implications for space–time modelling and network complexity.

[1]  Billy M. Williams,et al.  Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results , 2003, Journal of Transportation Engineering.

[2]  D. T. Lee,et al.  Travel-time prediction with support vector regression , 2004, IEEE Transactions on Intelligent Transportation Systems.

[3]  J. Paul Elhorst,et al.  Specification and Estimation of Spatial Panel Data Models , 2003 .

[4]  Malcolm M. Dow,et al.  Global, Regional, and Local Network Autocorrelation in the Standard Cross-Cultural Sample , 2008 .

[5]  Yi Zhang,et al.  Short-term traffic flow forecasting of urban network based on dynamic STARIMA model , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[6]  Jiaqiu Wang,et al.  STARIMA for journey time prediction in London , 2010 .

[7]  Géraldine Pflieger,et al.  Introduction. Urban Networks and Network Theory: The City as the Connector of Multiple Networks , 2010 .

[8]  Daniel A. Griffith.  Modeling spatio-temporal relationships: retrospect and prospect , 2010, J. Geogr. Syst..

[9]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[10]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[11]  F. Stetzer,et al.  Specifying Weights in Spatial Forecasting Models: The Results of Some Experiments , 1982 .

[12]  B. Jiang.  A topological pattern of urban street networks: Universality and peculiarity , 2007, physics/0703223.

[13]  Michel Mouchart,et al.  The local spatial autocorrelation and the kernel method for identifying black zones. A comparative approach. , 2003, Accident; analysis and prevention.

[14]  Dominique Peeters,et al.  Network Autocorrelation: Network Autocorrelation , 2009 .

[15]  William R. Black,et al.  ACCIDENTS ON BELGIUM'S MOTORWAYS: A NETWORK AUTOCORRELATION ANALYSIS. , 1998 .

[16]  Alexander Klippel,et al.  Analysing spatio-temporal autocorrelation with LISTA-Viz , 2010, Int. J. Geogr. Inf. Sci..

[17]  P. Pfeifer,et al.  A Three-Stage Iterative Procedure for Space-Time Modeling Phillip , 2012 .

[18]  R. Geary,et al.  The Contiguity Ratio and Statistical Mapping , 1954 .

[19]  P. R. Rider,et al.  ON THE DISTRIBUTION OF THE CORRELATION COEFFICIENT IN SMALL SAMPLES , 1932 .

[20]  Steven Farber,et al.  Topology and Dependency Tests in Spatial and Network Autoregressive Models , 2009 .

[21]  H. J. Van Zuylen,et al.  Accurate freeway travel time prediction with state-space neural networks under missing data , 2005 .

[22]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[23]  Yang Yue,et al.  Spatiotemporal Traffic-Flow Dependency and Short-Term Traffic Forecasting , 2008 .

[24]  Martin Suter,et al.  Small World , 2002 .

[25]  PATRICK DOREIAN,et al.  Network Autocorrelation Models , 1984 .

[26]  K. Reitz,et al.  Galton's Problem as network autocorrelation , 1984 .

[27]  P. Moran.  Notes on continuous stochastic phenomena. , 1950, Biometrika.

[28]  Douglas R. White,et al.  Galton's Problem as Network Autocorrelation in Social Structure and Social Relations. , 1984 .

[29]  J. Rodgers,et al.  Thirteen ways to look at the correlation coefficient , 1988 .

[30]  Roger Th.A.J. Leenders,et al.  The specification of weight structures in network autocorrelation models of social influence , 2002 .

[31]  Gerard B. M. Heuvelink,et al.  Deriving Space-Time Variograms from Space-Time Autoregressive (STAR) Model Specifications , 2010, SDH.

[32]  Mark Dougherty,et al.  SHORT TERM INTER-URBAN TRAFFIC FORECASTS USING NEURAL NETWORKS , 1997 .

[33]  Yongwan Chun,et al.  Modeling network autocorrelation within migration flows by eigenvector spatial filtering , 2008, J. Geogr. Syst..

[34]  David M Levinson,et al.  Measuring the Structure of Road Networks , 2007 .

[35]  Malcolm M. Dow,et al.  Galton's Problem as Multiple Network Autocorrelation Effects , 2007 .

[36]  Karl Pearson,et al.  ON THE DISTRIBUTION OF THE CORRELATION COEFFICIENT IN SMALL SAMPLES. APPENDIX II TO THE PAPERS OF “STUDENT” AND R. A. FISHER. A COOPERATIVE STUDY , 1917 .

[37]  Eleni I. Vlahogianni,et al.  Short‐term traffic forecasting: Overview of objectives and methods , 2004 .

[38]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[39]  Manuel Castells,et al.  Globalisation, Networking, Urbanisation: Reflections on the Spatial Dynamics of the Information Age , 2010 .

[40]  Haitham Al-Deek,et al.  Cross-Correlation Analysis and Multivariate Prediction of Spatial Time Series of Freeway Traffic Speeds , 2008 .

[41]  Darren M. Scott,et al.  Weight matrices for social influence analysis: An investigation of measurement errors and their effect on model identification and estimation quality , 2008, Soc. Networks.

[42]  Zuo Zhang,et al.  Urban traffic network modeling and short-term traffic flow forecasting based on GSTARIMA model , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[43]  P. Pfeifer,et al.  A Three-Stage Iterative Procedure for Space-Time Modeling , 1980 .

[44]  Alessandro Chessa,et al.  Time evolution of complex networks: commuting systems in insular Italy , 2011, J. Geogr. Syst..

[45]  Kay W. Axhausen,et al.  Predicting road system speeds using spatial structure variables and network characteristics , 2007, J. Geogr. Syst..

[46]  Raymond J.G.M. Florax,et al.  The Impacts of Misspecified Spatial Interaction in Linear Regression Models , 1995 .

[47]  Daniel Z. Sui,et al.  Small-world characteristics on transportation networks: a perspective from network autocorrelation , 2007, J. Geogr. Syst..

[48]  Daniel A. Griffith,et al.  On the quality of likelihood-based estimators in spatial autoregressive models when the data dependence structure is misspecified , 1998 .

[49]  William R. Black,et al.  Network Autocorrelation in Transport Network and Flow Systems , 2010 .

[50]  Ganapati P. Patil,et al.  Impacts and Wider Impacts on Statistics , 2009 .

[51]  Xi Fu Wang,et al.  Forecasting Traffic Volume with Space-Time ARIMA Model , 2010 .

[52]  Yiannis Kamarianakis,et al.  Space-time modeling of traffic flow , 2002, Comput. Geosci..

[53]  H. V. van Zuylen,et al.  Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters , 2006 .

[54]  Mark S. Mizruchi,et al.  The effect of density on the level of bias in the network autocorrelation model , 2008, Soc. Networks.

[55]  J. Olden,et al.  Cross-correlation bias in lag analysis of aquatic time series , 2001 .

[56]  Kelvyn Jones,et al.  Generalized Additive Models, Graphical Diagnostics, and Logistic Regression , 2010 .

[57]  L. Anselin.  Local Indicators of Spatial Association—LISA , 2010 .

[58]  A. Getis,et al.  Constructing the Spatial Weights Matrix Using a Local Statistic , 2004 .

[59]  Mark S. Mizruchi,et al.  Structure and bias in the network autocorrelation model , 2010, Soc. Networks.

[60]  P. I. Richards.  Shock Waves on the Highway , 1956 .