A Subjectively Influenced Router for Vehicles in a Four-Junction Traffic System
A subjectively influenced router for vehicles in a fourjunction
traffic system is presented. The router is based on a 3-layer
Backpropagation Neural Network (BPNN) and a greedy routing
procedure. The BPNN detects priorities of vehicles based on the
subjective criteria. The subjective criteria and the routing procedure
depend on the routing plan towards vehicles depending on the user.
The routing procedure selects vehicles from their junctions based on
their priorities and route them concurrently to the traffic system. That
is, when the router is provided with a desired vehicles selection
criteria and routing procedure, it routes vehicles with a reasonable
junction clearing time. The cost evaluation of the router determines
its efficiency. In the case of a routing conflict, the router will route
the vehicles in a consecutive order and quarantine faulty vehicles.
The simulations presented indicate that the presented approach is an
effective strategy of structuring a subjective vehicle router.
[1] K.G. Anilkumar and T. Tanprasert, "Neural Network Based Generalized
Job-Shop Scheduler", in Proc. of 2nd IMT-GT Regional Conference on
Mathematics, statistics and Applications, Penang, Malaysia, 2006, pp.
53-58.
[2] K.G Anilkumar and T. Tanprasert, "Neural Network Based Greedy Job
Scheduler", in Proc of National Computer Science and Engineering
Conference (NCSEC 2006), Thailand, 2006, pp. 257-262.
[3] K.G Anilkumar and T. Tanprasert, "Generalized Job-shop Scheduler
Using Feed Forward Neural Network and Greedy Alignment
Procedure", in Proc. of IASTED Conference on Artificial Intelligence
and Applications, AIA-2007, Austria, pp. 115-120.
[4] I. Okhrin and K. Richter, "The Real-Time Vehicle Routing Problem",
Operation Research proceedings 2007, Springer Berlin Heidelberg,
2007, pp. 141-146.
[5] D. Bertsimas, P. Chervi and M. Peterson, "Computational Approaches to
Stochastic Vehicle Routing Problems", Journal of Transportation
Science, vol. 29, 1995, pp. 342-352.
[6] V. B. Rao and H. V. Rao, Neural Networks & Fuzzy logic, BPB
Publications, Delhi, 1996.
[7] S. Stinson, An Introduction to the Design and Analysis of Algorithms,
Cambridge Press, 1980, pp. 70-92.
[8] T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to
Algorithms, the MIT Press: McGraw-Hill, 2001, pp. 370-399.
[9] R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical
Analysis, 5th edition, NJ: Prentice Hall, NJ, 2002, pp. 668-719.
[1] K.G. Anilkumar and T. Tanprasert, "Neural Network Based Generalized
Job-Shop Scheduler", in Proc. of 2nd IMT-GT Regional Conference on
Mathematics, statistics and Applications, Penang, Malaysia, 2006, pp.
53-58.
[2] K.G Anilkumar and T. Tanprasert, "Neural Network Based Greedy Job
Scheduler", in Proc of National Computer Science and Engineering
Conference (NCSEC 2006), Thailand, 2006, pp. 257-262.
[3] K.G Anilkumar and T. Tanprasert, "Generalized Job-shop Scheduler
Using Feed Forward Neural Network and Greedy Alignment
Procedure", in Proc. of IASTED Conference on Artificial Intelligence
and Applications, AIA-2007, Austria, pp. 115-120.
[4] I. Okhrin and K. Richter, "The Real-Time Vehicle Routing Problem",
Operation Research proceedings 2007, Springer Berlin Heidelberg,
2007, pp. 141-146.
[5] D. Bertsimas, P. Chervi and M. Peterson, "Computational Approaches to
Stochastic Vehicle Routing Problems", Journal of Transportation
Science, vol. 29, 1995, pp. 342-352.
[6] V. B. Rao and H. V. Rao, Neural Networks & Fuzzy logic, BPB
Publications, Delhi, 1996.
[7] S. Stinson, An Introduction to the Design and Analysis of Algorithms,
Cambridge Press, 1980, pp. 70-92.
[8] T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to
Algorithms, the MIT Press: McGraw-Hill, 2001, pp. 370-399.
[9] R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical
Analysis, 5th edition, NJ: Prentice Hall, NJ, 2002, pp. 668-719.
@article{"International Journal of Information, Control and Computer Sciences:54988", author = "Anilkumar Kothalil Gopalakrishnan", title = "A Subjectively Influenced Router for Vehicles in a Four-Junction Traffic System", abstract = "A subjectively influenced router for vehicles in a fourjunction
traffic system is presented. The router is based on a 3-layer
Backpropagation Neural Network (BPNN) and a greedy routing
procedure. The BPNN detects priorities of vehicles based on the
subjective criteria. The subjective criteria and the routing procedure
depend on the routing plan towards vehicles depending on the user.
The routing procedure selects vehicles from their junctions based on
their priorities and route them concurrently to the traffic system. That
is, when the router is provided with a desired vehicles selection
criteria and routing procedure, it routes vehicles with a reasonable
junction clearing time. The cost evaluation of the router determines
its efficiency. In the case of a routing conflict, the router will route
the vehicles in a consecutive order and quarantine faulty vehicles.
The simulations presented indicate that the presented approach is an
effective strategy of structuring a subjective vehicle router.", keywords = "Backpropagation Neural Network, Backpropagationalgorithm, Greedy routing procedure, Subjective criteria, Vehiclepriority, Cost evaluation, Route generation", volume = "5", number = "3", pages = "266-9", }