Agent-Base Modeling of Refueling Vehicles based on Demand Management Approach and Comparing its Result with the Stated Preferences Method in Tehran

Document Type : Research Article

Authors

Department of Civil Engineering, Transportation Planning, Imam Khomeini International University, Qazvin, Iran

Abstract

The increasing demand for private cars and the unbalanced and dense growth of land-uses in the north of Tehran metropolis have caused fuel supply stations (gas stations) to face the demand queue most of the time. On the other hand, the exorbitant price of land in these areas does not make the construction of a new location cost-effective. Demand management policies such as increasing the cost of using the facility are the possible solution to solve such problems. In this paper, the gas station users' data in the north of Tehran were collected using the stated preference method and analyzed using SPSS statistical analysis software to define the agent features and build agent-based modeling. The survey results were used to define the characteristics of factors and interactive rules in the NetLogo simulation software. Then, the existence of a fast-passing line (with five different pricing scenarios) was simulated compared to the normal condition. The results showed that although the price increase scenarios (scenarios 4 and 5) are more welcome by relying on the statistically-based stated preference method, the agent-based simulation shows the popularity of cheap scenarios (scenarios 1 and 2) based on the interactive behavior of people are more valid. This indicates that the stated preference method's responses cannot be reassured in cases where interactive behaviors exist. Also, due to the imbalance of supply and demand due to the region's context, creating a tolled high-speed passing line is an effective solution to adjust the queue length and reduce waiting time.

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Main Subjects

References

[1]  Y. Sheffi, Urban Transportation Networks. Equilibrium Analysis with Mathematical Programming Methods, (1985).
[2]  K.S. Kalid, J. Ahmad, Y.S. Peng, Y.K. Hooi, PETRONAS Petrol Station Fuel Consumption Forecast System, Proceedings of the Second International Conference on Artificial Intelligence in Engineering & Technology (2004.
[3]  D. Popović, M. Vidović, N. Bjelić, Simulation Model for Irp in Petrol Station Replenishment, 2nd Logistics International Conference (2015).
[4] A. Benantar, R. Ouafi, J. Boukachour, A petrol station replenishment problem: new variant and formulation, Logistics Research, 9(1) (2016) 6.
[5]  H.L. Khoo, G.P. Ong, W.C. Khoo, Short-term impact analysis of fuel price policy change on travel demand in Malaysian cities, Transportation Planning and Technology, 35(7) (2012) 715-736.
[6]  M. Khalilikhah, M. Habibian, K. Heaslip, Acceptability of increasing petrol price as a TDM pricing policy: A case study in Tehran, Transport Policy, 45 (2016) 136-144.
[7]  S. Mwenda, D.M. Oloko, Determinants of Motorists Choice of a Petrol Station in Kenya a Survey of Thika Sub County, International Journal of Social Science and Information Technology, Ii(Ix) (2016).
[8]  H.S. Dutsenwai, A. Abdullah, A.B.S.A. Jamak, A.M. Noor, Factors influencing customer loyalty in Malaysian petrol stations: moderating effect location, Journal of Scientific Research and Development, 2(12) (2015) 56-63.
[9]  P. Grunewald, M. Diakonova, Flexibility, dynamism and diversity in energy supply and demand: A critical review, Energy Research & Social Science, 38 (2018) 58-66.
[10]  J. Li, J.H. Stock, Cost pass-through to higher ethanol blends at the pump: Evidence from Minnesota gas station data, Journal of Environmental Economics and Management, 93 (2019) 1-19.
[11] A.A. Onoja, M.M. Kembe, G. Cj, J.C. Gbenimako, Application of Queueing Theory to Customers Purchasing Premium Motor Spirit (PMS) at a Filling Station, Statistics and Mathematical Sciences, 3 (2017) 10.
[12]  N. Madadi, A.H. Roudsari, K.Y. Wong, M.R. Galankashi, Modeling and Simulation of a Bank Queuing System, in:  2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, 2013, pp. 209-215.
[13]  A.K. Sharma, D.G.K. Sharma, Queueing Theory Approach with Queueing Model: A Study, International Journal of Engineering Science Invention, 2(2), (2016).
[14]  N.Balaji, Optimal Resource Model Using Matlab / Simulink Controlled Queuing System Using Multiserver at Major Fuel Stations, International Journal of Pure and Applied Mathematics, 113. 221-229, (2017).
[15]  A.A. Shojaie, M. Haddadi, F. Abdi, Hybrid Systems Modeling in Non Standard Queue and Optimization with the Simulation Approach in CNG Stations, Research Journal of Applied Sciences, Engineering and Technology 4(14) (2012) 2110-21.
[16]  A. Moazzam, M.R. Galankashi, A. Khademi, Simulation, Modeling and Analysis of a Petrol Station, International Review on Modelling and Simulations (I.RE.MO.S.), 6(1) (2013) 246-253.
[17]  A.O. Odior, Application of Queuing Theory to Petrol Stations in Benin-City Area ff Edo State, Nigeria, Nigerian Journal of Technology (Nijotech), 32 (2013) 325-332.
[18]  F. Wei, L. Zhang, T. Liu, X. Lu, K. Mori, Autonomous Community Architecture and Construction Technology for City Petrol Supply Management System, in:  2015 IEEE Twelfth International Symposium on Autonomous Decentralized Systems, 2015, pp. 109-113.
[19]  M.R. Galankashi, E. Fallahiarezoudar, A. Moazzami, N.M. Yusof, S.A. Helmi, Performance evaluation of a petrol station queuing system: A simulation-based design of experiments study, Advances in Engineering Software, 92 (2016) 15-26.
[20]  M.R. Galankashi, E. Fallahiarezoudar, A. Moazzami, S.A. Helmi, J.M. Rohani, N.M. Yusof, An efficient integrated simulation–Taguchi approach for sales rate evaluation of a petrol station, Neural Computing and Applications, 29(4) (2018) 1073-1085.
[21]  G. Xu, M. Xu, Y. Wang, Y. Liu, K. Assogba, Optimization of energy supply system under information variations based on gas stations queuing analyses, Systems Science & Control Engineering, 6(2) (2018) 10-23.
[22]  R. Hassin, J.H.P. Milo, On Rational Behavior in a Loss System with One Observable Queue and One Unobservable Queue, in, Springer International Publishing, Cham, 2019, pp. 166-182.
[23]  H. Zheng, Y.-J. Son, Y.-C. Chiu, L. Head, Y. Feng, H. Xi, S. Kim, M. Hickman, A Primer for Agent-Based Simulation and Modeling in Transportation Applications, U.S. Department of Transportation Federal Transit Administration, U.S, 2013.
[24]  D. Helbing, Chapter2, Agent-Based Modeling, in: D. Helbing (Ed.) Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 25-70.
[25] R. Axelrod, Chapter 33 Agent-based Modeling as a Bridge Between Disciplines, in: L. Tesfatsion, K.L. Judd (Eds.) Handbook of Computational Economics, Elsevier, 2006, pp. 1565-1584.
[26]  C.M. Macal, M.J. North, Tutorial on agent-based modeling and simulation, in:  Proceedings of the Winter Simulation Conference, IEEE, (2005). DOI: 10.1109/WSC.2005.1574234.
[27]  D.B. Fuller, E.F. de Arruda, V.J.M. Ferreira Filho, Learning-agent-based simulation for queue network systems, Journal of the Operational Research Society, 71(11) (2020) 1723-1739.
[28]  J.L. Adler, V.J. Blue, A cooperative multi-agent transportation management and route guidance system, Transportation Research Part C: Emerging Technologies, 10(5) (2002) 433-454.
[29]  W. Fei-Yue, Agent-based control for networked traffic management systems, IEEE Intelligent Systems, 20(5) (2015).
[30] M. Balmer, K.W. Axhausen, K. Nagel, Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations, Transportation Research Record, 1985(1) (2006) 125-134.
[31]  K.W. Axhausen, M. Balmer, K. Meister, M. Rieser, K. Nagel, Agent-based simulation of travel demand Structure and computational performance of MATSim-T, Conference Paper (2008).
[32]  J. Auld, M. Hope, H. Ley, V. Sokolov, B. Xu, K. Zhang, POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations, Transportation Research Part C: Emerging Technologies, 64 (2016) 101-116.
[33]  J. Holmgren, P. Davidsson, J.A. Persson, L. Ramstedt, TAPAS: A multi-agent-based model for simulation of transport chains, Simulation Modelling Practice and Theory, 23 (2012) 1-18.
[34]  R.B. Matthews, N.G. Gilbert, A. Roach, J.G. Polhill, N.M. Gotts, Agent-based land-use models: a review of applications, Landscape Ecology, 22(10) (2007) 1447-1459.
[35]  T. Zhang, S. Gensler, R. Garcia, A Study of the Diffusion of Alternative Fuel Vehicles: An Agent-Based Modeling Approach*, Journal of Product Innovation Management, 28(2) (2011) 152-168.
[36]  J. Gjerdrum, N. Shah, L.G. Papageorgiou, A combined optimization and agent-based approach to supply chain modelling and performance assessment, Production Planning & Control, 12(1) (2001) 81-88.
[37]  N. Julka, R. Srinivasan, I. Karimi, Agent-based supply chain management—1: framework, Computers & Chemical Engineering, 26(12) (2002) 1755-1769.
[38]  N. Julka, I. Karimi, R. Srinivasan, Agent-based supply chain management—2: a refinery application, Computers & Chemical Engineering, 26(12) (2002) 1771-1781.
[39] H.J. Ahn, H. Lee, An Agent-Based Dynamic Information Network for Supply Chain Management, BT Technology Journal, 22(2) (2004) 18-27.
[40]  S.O. Kimbrough, D.J. Wu, F. Zhong, Computers play the beer game: can artificial agents manage supply chains?, Decision Support Systems, 33(3) (2002) 323-333.
[41] W.-Y. Liang, C.-C. Huang, Agent-based demand forecast in multi-echelon supply chain, Decision Support Systems, 42(1) (2006) 390-407.
[42] P. Priya Datta, M. Christopher, P. Allen, Agent-based modelling of complex production/distribution systems to improve resilience, International Journal of Logistics Research and Applications, 10(3) (2007) 187-203.
[43] O. Labarthe, B. Espinasse, A. Ferrarini, B. Montreuil, Toward a methodological framework for agent-based modelling and simulation of supply chains in a mass customization context, Simulation Modelling Practice and Theory, 15(2) (2007) 113-136.
[44] O. Kwon, G.P. Im, K.C. Lee, MACE-SCM: A multi-agent and case-based reasoning collaboration mechanism for supply chain management under supply and demand uncertainties, Expert Systems with Applications, 33(3) (2007) 690-705.
[45] X. Xue, X. Li, Q. Shen, Y. Wang, An agent-based framework for supply chain coordination in construction, Automation in Construction, 14(3) (2005) 413-430.
[46] M. Galán José, A. López-Paredes, R. del Olmo, An agent-based model for domestic water management in Valladolid metropolitan area, Water Resources Research, 45(5), (2009).
[47] R. Garcia, Uses of Agent-Based Modeling in Innovation/New Product Development Research*, Journal of Product Innovation Management, 22(5), 380-398, (2005).
[48] E. Kiesling, M. Günther, C. Stummer, L.M. Wakolbinger, Agent-based simulation of innovation diffusion: a review, Central European Journal of Operations Research, 20(2) (2012) 183-230.
[49] E. Bonabeau, Agent-based modeling: Methods and techniques for simulating human systems, Proceedings of the National Academy of Sciences, 99(suppl 3) (2002) 7280.
[50] X. Pan, C.S. Han, K. Dauber, K.H. Law, A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations, AI & SOCIETY, 22(2) (2007) 113-132.
[51] C. Ren, C. Yang, S. Jin, Agent-Based Modeling and Simulation on Emergency Evacuation, in: J. Zhou (Ed.) Complex Sciences, Springer Berlin Heidelberg, Berlin, Heidelberg, 2009, pp. 1451-1461.
[52] X. Zheng, T. Zhong, M. Liu, Modeling crowd evacuation of a building based on seven methodological approaches, Building and Environment, 44(3) (2009) 437-445.
[53] V. Ha, G. Lykotrafitis, Agent-based modeling of a multi-room multi-floor building emergency evacuation, Physica A: Statistical Mechanics and its Applications, 391(8) (2012) 2740-2751.
[54] W. Yin, P. Murray-Tuite, S.V. Ukkusuri, H. Gladwin, An agent-based modeling system for travel demand simulation for hurricane evacuation, Transportation Research Part C: Emerging Technologies, 42 (2014) 44-59.
[55] X. Chen, F.B. Zhan, Agent-based modelling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies, Journal of the Operational Research Society, 59(1) (2008).
[56] R.V. Krejcie, D.W. Morgan, Determining Sample Size for Research Activities, Educational and Psychological Measurement, 30(3) (1970) 607-610.
Amirkabir Journal of Civil Engineering
Volume 54, Issue 9
December 2022
Pages 3411-3432

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