A model for spatio-temporal network planning
Introduction
GIS is used today for holding a wide range of data relating to spatial networks—from utility and communications infrastructure companies to transport operators the network is recorded and modelled within a GIS. This data is then used for a variety of tasks from basic inventory to operations management, and as a basis for planning the future of the network (Waters, 1999). For many of these tasks a spatio-temporal model may be advantageous, for network planning the temporal aspect is likely to be of even greater importance. However, most temporal GIS research appears focussed on recording historical or transaction-time data, or both in bitemporal systems (Worboys, 1995). Such data, however, has different characteristics to data relating to future plans.
In this paper we present a spatio-temporal model for network planning allowing multivariate optimisation incorporating spatial, temporal, financial and other aspects. We show how this model could be used as part of a decision-support process applied to a scenario of planning a network of cycle paths and discuss some possibilities for design optimisation using the model.
Section snippets
Temporal models
In general, GIS systems do not incorporate a temporal model; they represent one state of the real world, usually the present. Most temporal GIS research (as outlined in, e.g. Peuquet, 2001) attempts to extend the representation to include a series of states, either of the database (a snapshot model) or of individual objects within the database (e.g. the ST-object model (Worboys, 1992)), allowing the changes in the real world to be recorded. This reproduces a linear model of time in which there
The temporal topology model
The “temporal topology” model was developed from extending a simple branching model of time to allow branches to re-join—i.e. considering that given two events, B and C which may occur in either order BC or order CB, the end result is the same. Whilst temporally this is obviously not the case, spatially it may well be—e.g. if events B and C are construction of two different sections of network then the end spatial result is that both are present, regardless of the order in which they were
Example application of temporal topology
The following example shows how the temporal topology model could be applied to a realistic scenario of planning improvements for cyclists along the route of a busy road. For simplicity only a small section is covered, showing a selection of different solutions, and how they could be broken down into events and relationships. Additionally, only the centrelines of the proposed routes are shown; a full model would include the associated crossings, signs, road markings, etc. which would be
Analysis of temporal topology systems
The aim of temporal topology analysis is to determine an optimal set and order of events based on the specified costs and satisfying all the given relationships and constraints. This gives the events a relative temporal location and discards all events deemed not to be in the optimal set. From the duration of the events in the sequence, and a given start or end time, an absolute temporal location for each can then be calculated if required. Each individual solution could therefore be considered
Implementation and testing of a temporal topology application
A decision-support application has been implemented based on the temporal topology theories outlined above. This implementation is built in the GE Smallworld GIS framework, using a metadata database to record the assignment of real world objects to temporal topology events and the relationships between these events. From this database, schematics of the temporal topology network can be produced which can then be analysed for single-variable optimisation using customised versions of the standard
Conclusions
This paper has presented a model, temporal topology, which aims to provide an efficient and useful method to represent and analyse alternative scenarios for network planning. The need for such a model was outlined, noting that most TGIS research has considered recording historical changes and has therefore concentrated upon linear models of time which cannot sensibly be used for modelling multiple scenarios. The perceived problems with standard branching models of time were highlighted and it
References (21)
Towards a general theory of action and time
Artificial Intelligence
(1984)- et al.
Recent developments in screening methods for nondominated solutions in multiobjective optimization
Computers & Operations Research
(1992) - Aristotle, ca. 350 BC. De Interpretatione (On...
- et al.
A unifying semantics for time and events
Artificial Intelligence
(2004) Integrating multi-criteria evaluation with geographical information systems
International Journal of Geographical Information Systems
(1991)Multiobjective Programming and Planning
(1978)A note on two problems in connexion with graphs
Numerische Mathematik
(1959)- et al.
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
(1995) - Holland, J.H., 1975. Adaptation in Natural and Artificial Systems, second ed. University of Michigan Press, 1992; MIT...
Multicriteria decision making and evolutionary computation
Cited by (6)
Introduction to Disaster and Emergency Management Science
2019, Advances in Science, Technology and InnovationModeling dynamic networks in temporal GIS and realization methods
2012, Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis PekinensisStudy on dynamical visualization of marine current data field based on base state with amendments spatio-temporal model
2009, Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09Spatially modelling pathways of migratory birds for nature reserve site selection
2008, International Journal of Geographical Information ScienceGIS-based multidimensional approach for modeling infrastructure interdependency
2006, Lecture Notes in Geoinformation and CartographyCycle network planning: Towards a holistic approach using temporal topology
2005, Transportation Planning and Technology