A model for spatio-temporal network planning

Temporal GIS research has tended to focus on representing a single history through a series of states. For planning future work involving alternative scenarios a branching model of time may be required, however for large systems such models soon become highly complex. In this paper we introduce the temporal topology model which allows sections of work and the spatial, temporal and logical relationships between them to be represented efficiently together with the associated costs. We then discuss how this model could be used for analysis to determine an optimal plan, illustrated with a case study involving cycle network planning, and briefly describe some practical results which have been obtained.

[1]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[2]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[3]  Nigel Waters,et al.  Transportation GIS: GIS-T , 2005 .

[4]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[5]  Mark Reynolds,et al.  Axioms for Branching Time , 2002, J. Log. Comput..

[6]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[7]  ZitzlerE.,et al.  Multiobjective evolutionary algorithms , 1999 .

[8]  Jonathan F. Bard,et al.  Recent developments in screening methods for nondominated solutions in multiobjective optimization , 1992, Comput. Oper. Res..

[9]  Donna Peuquet,et al.  Making Space for Time: Issues in Space-Time Data Representation , 2001, GeoInformatica.

[10]  H. Fawcett Manual of Political Economy , 1995 .

[11]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[12]  David J. Maguire,et al.  Geographical Information Systems , 1993 .

[13]  James F. Allen Towards a General Theory of Action and Time , 1984, Artif. Intell..

[14]  Stephen J. Carver,et al.  Integrating multi-criteria evaluation with geographical information systems , 1991, Int. J. Geogr. Inf. Sci..

[15]  Saul Kripke,et al.  A completeness theorem in modal logic , 1959, Journal of Symbolic Logic.

[16]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[17]  Jared L. Cohon,et al.  Multiobjective programming and planning , 2004 .

[18]  Antony Galton,et al.  A unifying semantics for time and events , 2004, Artif. Intell..

[19]  Michael F. Worboys,et al.  GIS : a computing perspective , 2004 .