Application of The Spider Monkey Optimization Algorithm In A Class Of Traffic Delay Problem

International Journal of Computer Science and Engineering

© 2020 by SSRG - IJCSE Journal

Volume 7 Issue 2

Year of Publication : 2020

Authors : Sonny E. Ezekwere, V.I.E Anireh, Matthias Daniel

10.14445/23488387/IJCSE-V7I2P106

How to Cite?

Sonny E. Ezekwere, V.I.E Anireh, Matthias Daniel, "Application of The Spider Monkey Optimization Algorithm In A Class Of Traffic Delay Problem," SSRG International Journal of Computer Science and Engineering , vol. 7,  no. 2, pp. 48-56, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I2P106

Abstract:

Nature inspired algorithms have gained some level of popularity amongst researchers in recent times. They possess the ability to search and discover solutions to real-world optimization problems, which may have been difficult to solve using deterministic techniques. Spider Monkey Optimization (SMO) is in a class of such algorithm. It is one of the most recent Swarm Intelligence (SI) based algorithm, that was developed through the study of the food foraging behavior of a group of spider monkeys that mimic the Fission-Fusion Social System (FFSS) behavior. This study applies SMO to traffic delay minimization problem. Experiment includes simulation of 4-legged intersection and the result showed minimization of total travel time. This result was compared to the Artificial Bee Colony (ABC) algorithm. The SMO outperformed the ABC algorithm because of its decentralized, stochastic and self-organizational attribute that makes it suitable for the nature of traffic networks. Computer simulation results show that this method performs better when compared with conventional fully actuated control, especially under the condition of fairly saturated traffic condition.

Keywords:

Spider Monkey Optimization algorithm, Artificial Bee Colony optimization, traffic delay problem, Fission-fusion social structure.

References:

[1] Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M. (2014): Spider monkey optimization algorithm for numerical optimization. Memetic Comput.,
[2] Sharma, A., Sharma, H., Bhargava, A., Sharma, N. (2016): Optimal design of PIDA controller for induction motor using spider monkey optimization algorithm. Int. J. Metaheuristics 5(3–4),
[3] H.Z. Aashtiani, T.L. Magnanti, (1981) Equilibria on a congested transportation network, SIAM J. Algebr. Discrete Methods 2 (3).
[4] A. Jovanovic, M. Nikolic, D. Teodorovic, (2017): Area-wide urban traffic control: A bee colony approach.
[5] R.E. Allsop, E.D. Turner, Road casualties and public transport fares in London, (1986) Accid. Anal. Prev. 18 (2).
[6] E. Cascetta, G.E. Cantarella, (1991) A day-to-day and within-day dynamic stochastic assignment model, Transp. Res. Part A: General 25 (5).
[7] D. Charlesworth, B. Charlesworth, (1975) Theoretical genetics of batesian mimicry II. Evolution of Supergene, J. Theoret. Biol. 55 (2).
[8] S.W.Chiou, (2003) TRANSYT derivatives for area traffic control optimisation with network equilibrium flows,Trans.Res.B.37(3).
[9] S. Dafermos, (1980) Traffic equilibrium and variational inequalities, Transp. Sci. 14 (1).
[10] D.J. Dailey, (1993) Travel-time estimation using cross-correlation techniques, Transp. Res. B 27 (2).
[11] A.D. Fisk, W. Schneider, (1984) Memory as a function of attention, level of processing, and automatization, J. Exp. Psychol. Learn. Mem. Cogn. 10 (2).
[12] J.H. Holland, 1975 Adaptation in natural and artificial systems. An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence, University of Michigan Press, Ann Arbor, MI,
[13] J.Long, Z.Gao, H.Ren, A.Lian, (2008) Urban traffic congestion propagation and bottleneck identification,Sci.ChinaSer.F51(7).
[14] W. Ma, K. An, K.L. Hong, (2016) Multi-stage stochastic program to optimize signal timings under coordinated adaptive control, Transp. Res. C 72.
[15] T.L. Magnanti, R.T. Wong, (1984) Network design and transportation planning: Models and algorithms, Transp. Sci. 18 (1).
[16] P. Marcotte, (1983) Network optimization with continuous control parameters, Transp. Sci. 17 (2).
[17] A. Stevanovic, J. Stevanovic, K. Zhang, S. Batterman (2009), Optimizing traffic control to reduce fuel consumption and vehicular emissions: integrated approach with VISSIM, CMEM, and VISGAOST, Transp. Res. Rec.: J. Transp. Res. Board 2128 (2128) 105–113.
[18] M.A. Khamis, W. Gomaa (2014), Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multiagent framework, Eng. Appl. ArtifIntell. 29 134–151.
[19] Teklu, F., Sumalee, A., & Watling, D. (2007). A genetic algorithm approach for optimizing traffic control signals considering routing. Computer-Aided Civil and Infrastructure Engineering, 22, 31-43.
[20] Webster, F. V. (1958). Traffic Signal Settings Road Research Technical Paper. HMSO London [39].
[21] Baskan, O., Haldenbilen, S., Ceylan, H., & Ceylan, H. (2009). A new solution algorithm for improving performance of ant colony optimization. Applied Mathematics and Computation, 211(1), 75-84.
[22] Dion, F. et al (2004), Comparison of Delay Estimates at Under-Saturated and Over-Saturated Pre-Timed Signalized Intersections, Transportation Research Part B 38 (2004), 99–122.
[23] Fambro, D., and N. Rouphail (1997), Generalized Delay Model for Signalized Intersections and Arterials, Transportation Research Record 1572, 112-121.
[24] Mousa, R. M. (2002), Analysis and Modeling of Measured Delays at Isolated Signalized Intersections, Journal of Transportation Engineering Vol. 128 No. 4.
[25] Quiroga, C. A., and Bullock, D. (1999), Measured Control Delay at Signalized Intersections, Journal of Transportation Engineering Vol. 125 No. 4.
[26] Olszewski, P. (1993), Overall Delay, Stopped Delay, and Stops at Signalized Intersections, Journal of Transportation Engineering Vol. 119 No. 6. Prevedouros, [25] P. D., and Koga, C. A. (1996), Comparison of 1985 and 1994 Signalized Intersection Delay Estimates, ITE Journal Vol. 66 No. 7.
[27] Troutbeck, R. and Kittelson, W. (1998), An Overview of the 1997 HCM Update, ITE Journal Vol. 68 No. 7.
[28] Click, M. (2003), Variables Affecting the Stopped to Control Delay at Signalized Intersection, TRB 2003 Annual Meeting.
[29] Lucic, P., Teodorovic, D., (2001). Bee system: Modeling combinatorial optimization transportation engineering problems by swarm intelligence.