Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-24T00:25:17.606Z Has data issue: false hasContentIssue false
  • English
  • Français

Towards a Multi Objective Path Optimisation for Car Navigation

Published online by Cambridge University Press:  25 November 2011

Ching-Sheng Chiu  and
Chris Rizos
Ching-Sheng Chiu*
Affiliation:
(Department of Urban Planning & Spatial Information, Feng Chia University)
Chris Rizos
Affiliation:
(School of Surveying & Spatial Information Systems, The University of New South Wales)
*
(E-mail:cschiu@fcu.edu.tw)
Get access Rights & Permissions [Opens in a new window]

Abstract

In a car navigation system the conventional information used to guide drivers in selecting their driving routes typically considers only one criterion, usually the Shortest Distance Path (SDP). However, drivers may apply multiple criteria to decide their driving routes. In this paper, possible route selection criteria together with a Multi Objective Path Optimisation (MOPO) model and algorithms for solving the MOPO problem are proposed. Three types of decision criteria were used to present the characteristics of the proposed model. They relate to the cumulative SDP, passed intersections (Least Node Path – LNP) and number of turns (Minimum Turn Path – MTP). A two-step technique which incorporates shortest path algorithms for solving the MOPO problem was tested. To demonstrate the advantage that the MOPO model provides drivers to assist in route selection, several empirical studies were conducted using two real road networks with different roadway types. With the aid of a Geographic Information System (GIS), drivers can easily and quickly obtain the optimal paths of the MOPO problem, despite the fact that these paths are highly complex and difficult to solve manually.

Keywords

Shortest pathLeast nodeMinimum turnCar navigation each
Type
Research Article
Information
The Journal of Navigation , Volume 65 , Issue 1 , January 2012 , pp. 125 - 144
Copyright
Copyright © The Royal Institute of Navigation 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bellman, R. (1958). On a routing problem, Quarter Application Mathematic, 16, 87–90.Google Scholar
Benayoun, R., Montgolfier, J., Tergny, J., and Laritchev, O. (1971). Linear programming with multiple objective functions: step method (STEM), Mathematical Programming, 1, 366–375.CrossRefGoogle Scholar
Ben-Tal, A. (1980). Characterization of pareto and lexicographic optimal solutions, in Fandel, G, and Gal, T, editors, Multiple Criteria Decision Making Theory and Application, volume 177 of Lecture Notes in Economics and Mathematical Systems, 1–11, Springer-Verlag, Berlin.Google Scholar
Berger, J. O. (1980). Statistical decision theory: foundations, concepts and methods, Springer-Verlag, New York.CrossRefGoogle Scholar
Brownlow, S. A., and Watson, S. R. (1987). Structuring multi-attribute value hierarchies, Journal of the Operational Research Society, 38, 309–317.CrossRefGoogle Scholar
Chakraborty, B., Maeda, T., and Chakraborty, G. (2005). Multiobjective route selection for car navigation system using genetic algorithm, Proceedings of the 2005 IEEE mid-summer workshop on soft computing in industrial applications, Espoo, Finland.Google Scholar
Charnes, A., Cooper, W. W., Niehaus, R. J., and Stedry, A. (1969). Static and dynamic assignment models with multiple objectives and some remarks on organization design, Management Science, 15, B365–B375.CrossRefGoogle Scholar
Climaco, J., and Martins, E. (1982). A bicriterion shortest path algorithm, European Journal of Operation Research, 11, 399–404.CrossRefGoogle Scholar
Cohon, J. L. (1978). Multiobjective programming and planning, Academic Press, New York.Google Scholar
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs, Numerische Mathematil, 1, 269–271.CrossRefGoogle Scholar
Goicoechea, A., Duckstein, L., and Fogel, M. (1979). Multiple objective under uncertainty: an illustrative application of PROTRADE, Water Resources Research, 15, 203–210.CrossRefGoogle Scholar
Goicoechea, A., Hansen, D. R., and Duckstein, L. (1982). Multiobjective analysis with engineering and business applications, John Wiley and Sons, New York.Google Scholar
Hallam, C., Harrison, K. J., and Ward, J. A. (2001). A multiobjective optimal path algorithm, Digital Singnal Processing, 11, 133–143.CrossRefGoogle Scholar
Haimes, Y. (1973). Integrated system identification and optimization, in Leondes, C editor, Control and Dynamic Systems: Advances in Theory and Applications, 9, 435–518, Academic Press, New York.Google Scholar
Hansen, P. (1980). Bicriterion path problems, in Fandel, G, and Gal, T, editors, Multiple Criteria Decision Making Theory and Applications, Proceedings 1979, 109–127.CrossRefGoogle Scholar
Henig, M. I. (1985). The shortest path problem with two objective functions, European Journal of Operational Research, 25, 281–291.CrossRefGoogle Scholar
Hwang, C. L., and Lin, M. J. (1987). Group decision-making under multiple criteria, Springer, New York.CrossRefGoogle Scholar
Jeong, C. W., Chung, Y. J., Joo, S. C., and Lee, J. H. (2006). Tourism guided information system for location-based services, Lecture Note in Computer Science, 3842, 749–755.CrossRefGoogle Scholar
Keeney, R. L., and Raiffa, H. (1976). Decisions with multiple objectives: preferences and value trade-offs, John Wiley and Sons, New York.Google Scholar
Monarchi, D. E., Kisiel, C. C., and Duckstein, L. (1973). Interactive multiobjective programming in water resources: a case study, Water Resources Research, 9, 837–850.CrossRefGoogle Scholar
Moore, E. F. (1957). The shortest through a maze, in Proceeding of the International Symposium on the Theory of Switching, 285–292.Google Scholar
Rao, S. S. (1996). Engineering optimization, John Wiley and Sons, New York.Google Scholar
Salukvadze, M. E. (1974). On the existence of solutions in problems of optimization under vector value criteria, Journal of Optimization Theory and Applications, 12, 203–217.CrossRefGoogle Scholar
Sarma, G. V., Sellami, L., and Houam, K. D. (1993). Application of lexicographic goal programming in production planning – two case studies, Operational Research, 30, 141–162.Google Scholar
Zadeh, L. A. (1963). Optimality and non-scalar-valued performance criteria, IEEE Transactions on Automatic Control. AC-8, 59–60.CrossRefGoogle Scholar