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Designing a Novel Transportation System Using Microscopic Models and Multi-Agent Approach

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Abstract

In this paper, we present a new dual-mode transportation system called MetroCar System (MCS) that produces solutions to transportation problems, especially in megacities. The proposed system combines some advantages of both modern and conventional transportation systems in order to establish a more effective, robust and safe transportation system. MCS is developed as a microscopic traffic simulation model by using a multi-agent approach. The model is prepared and simulated using NetLogo, which is a multi-agent programmable modeling environment. All processes and procedures of the MCS have been defined in detail using a multi-agent approach. Three types of agents perform these processes in a distributed manner. Some processes such as entrance, tracking, merging, direction control and exit have been tested. The simulation results show that the proposed model meets expectations and that traffic flows can be controlled without collision. Thus, a suitable system is emerged, which is more practical than conventional transportation systems and more feasible than the other advanced transportation systems.

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REFERENCES

  1. Naghawi, H.H. and Idewu, W., Analysing delay and queue length using microscopic simulation for the unconventional intersection design Superstreet, J. South Afr. Inst. Civ. Eng., 2014, vol. 56, no. 1, pp. 100–107.

    Google Scholar 

  2. Avram, C., Astilean, A., and Letia, T., Multiagent technology for an Urban Road Traffic Advisor, 2006 IEEE International Conference on Automation, Quality and Testing, Robotics, 2006.

  3. Hager, K., Rauh, J., and Rid, W., Agent-based modeling of traffic behavior in growing metropolitan areas, Transp. Res. Procedia, 2015, vol. 10, pp. 306–315.

    Article  Google Scholar 

  4. Boxill, S.A. and Yu, L., An Evaluation of Traffic Simulation Models for Supporting ITS Development, Report No: SWUTC/00/167602-1, Center for Transportation Training and Research, 2000.

    Google Scholar 

  5. Harri, J., Filali, F., and Bonnet, C., Mobility Models for Vehicular Ad Hoc Networks: A Survey and Taxonomy, Sophia-Antipolis: Inst. Eur’ecom Dep. Mobile Commun., 2007.

    Google Scholar 

  6. Xiao, J. and Wang, Z., Traffic speed cloud maps: A new method for analyzing macroscopic traffic flow, Physica A, 2018, vol. 508, pp. 367–375.

    Article  Google Scholar 

  7. Mihăiţă, A.S., Ortiz, M.B., Camargo, M., and Cai, C., Predicting air quality by integrating a mesoscopic traffic simulation model and simplified air pollutant estimation models, Int. J. Intell. Transp. Syst. Res., 2018, vol. 17, pp. 125–141.

    Google Scholar 

  8. Tu, R., Kamel, I., Wang, A., Abdulhai, B., and Hatzopoulou, M., Development of a hybrid modelling approach for the generation of an urban on-road transportation emission inventory, Transp. Res. Part D, 2018, vol. 62, pp. 604–618.

    Article  Google Scholar 

  9. Li, S., Cao, D., Wu, J., Sun, T., and Dang, W., Traffic state evaluation and intersection-movement-based incidents detection of expressway network, J. Transp. Saf. Secur., 2018, vol. 11, no. 6, pp. 1–19.

    Article  Google Scholar 

  10. Gettman, D. and Head, L., Surrogate Safety Measures from Traffic Simulation Models, Publication No: FHWA-RD-03-050, Research and Technology Report Center, 2003.

    Google Scholar 

  11. Jeannotte, K., Chandra, A., Alexiadis, V., and Skabardonis, A., Traffic Analysis Toolbox Volume II: Decision Support Methodology for Selecting Traffic Analysis Tools, Publication No: FHWA-HRT-04-039, Washington, DC: Federal Highway Administration, 2004.

    Google Scholar 

  12. Perraki, G., Roncoli, C., Papamichail, I., and Papageorgiou, M., Evaluation of an MPC strategy for motorway traffic comprising connected and automated vehicles, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018.

  13. Khondaker, B. and Kattan, L., Variable speed limit: A microscopic analysis in a connected vehicle environment, Transp. Res. Part C: Emerg. Technol., 2015, vol. 58, part A, pp. 146–159.

  14. Han, Z., Zhang, K., Yin, H., and Zhu, Y., An urban traffic simulation system based on multi-agent modeling, The 27th Chinese Control and Decision Conference (2015 CCDC), 2015, pp. 6378–6383.

  15. Al-dmour, N.A.A., TarffSim: Multiagent traffic simulation, Eur. J. Sci. Res., 2011, vol. 53, no. 4, pp. 570–575.

    Google Scholar 

  16. Janota, A., Rastocny, K., and Zahradnik, J., Multi-agent approach to traffic simulation in NetLogo environment—level crossing model, 5-th International Conference Transport Systems Telematics TST'05, 2005.

  17. Yang, K., Guler, S.I., and Menendez, M., Isolated intersection control for various levels of vehicle technology: Conventional, connected, and automated vehicles, Transp. Res. Part C, 2016, vol. 72, pp. 109–129.

    Article  Google Scholar 

  18. Cruz-Piris, L., Rivera, D., Fernandez, S., and Marsa-Maestre, I., Optimized sensor network and multi-agent decision support for smart traffic light management, Sensors, 2018, vol. 18, no. 435, pp. 1–22.

    Article  Google Scholar 

  19. Sun, L., Tao, J., Li, C., Wang, S., and Tong, Z., Microscopic simulation and optimization of signal timing based on multi-agent: A case study of the intersection in Tianjin, KSCE J. Civ. Eng., 2018, vol. 22, pp. 3373–3382.

    Article  Google Scholar 

  20. Joubert, J.W. and De Koker, N., Passing in multi-lane, heterogeneous traffic: Part 2, simulation, Procedia Comput. Sci., 2018, vol. 130, pp. 773–778.

    Article  Google Scholar 

  21. Arbabi, H. and Weigle, M.C., Using DTMon to monitor transient flow traffic, Vehicular Networking Conference, 2010, pp. 110–117.

  22. Grumert, E., Ma, X., and Tapani, A., Analysis of a cooperative variable speed limit system using microscopic traffic simulation, Transp. Res. Part C, 2015, vol. 52, pp. 173–186.

    Article  Google Scholar 

  23. Hausknecht, M., Au, T.-C., and Stone, P., Autonomous intersection management: Multi-Intersection optimization, International Conference on Intelligent Robots and Systems (IROS), 2011.

  24. Luo, R., Van Den Boom, T.J.J., De Schutter, B., and Member, S., Multi-agent dynamic routing of a fleet of cybercars, IEEE Trans. Intell. Transp. Syst., 2018, vol. 19, no. 5, pp. 1340–1352.

    Article  Google Scholar 

  25. Çavusoğlu, A. and Kurnaz, İ., The development of a hardware- and software-based simulation platform for the training of driver candidates, Turk. J. Electr. Eng. Comput. Sci., 2013, vol. 21, pp. 131–143.

    Google Scholar 

  26. Liu, R., Automated transit applications real-world examples, TR News, 2018, no. 317.

  27. Gkartzonikas, C. and Gkritza, K., What have we learned? A review of stated preference and choice studies on autonomous vehicles, Transp. Res. Part C Emerg. Technol., 2019, vol. 98, pp. 323–337.

    Article  Google Scholar 

  28. Legacy, C., Ashmore, D., Scheurer, J., Stone, J., and Curtis, C., Planning the driverless city, Transp. Rev., 2018, vol. 39, no. 1, pp. 84–102.

    Article  Google Scholar 

  29. Mowll, J.U., US Patent 4791871, 1988.

  30. Ehlig-Economides, C. and Longbottom, J., Dual Mode Vehicle and Infrastructure Alternatives Analysis, Texas Transp. Inst., 2007.

    Google Scholar 

  31. Krevet, R. and Woronowicz, K., Autoshuttle electric express highway, 22 MAGLEV Conference, 2014.

  32. Jensen, P.R., RUF International Investment Case, RUF Int., 2015.

    Google Scholar 

  33. Davis, W.D., US Patent 7788000, 2010.

  34. CarTube, The Future of Urban Mass Transportation, 2019. http://cartube.global/index.html. Accessed March 17, 2019.

  35. Pierce, A., Elon Musk’s Boring Company, Technol. Today, 2017, Aug. 23.

  36. Gonzalez, D., Traffic Congestion in California: Implementation of Congestion Pricing in Los Angeles County as an Effective Traffic-Reducing Strategy, Northridge: Calif. State Univ., 2018.

    Google Scholar 

  37. Kaur, K. and Rampersad, G., Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars, J. Eng. Technol. Manage., 2018, vol. 48, pp. 87–96.

    Article  Google Scholar 

  38. Aramrattana, M., et al., Team Halmstad approach to cooperative driving in the grand cooperative driving challenge 2016, IEEE Trans. Intell. Transp. Syst., 2018, vol. 19, no. 4, pp. 1248–1261.

    Article  Google Scholar 

  39. Bozuyla, M., Tola, A.T., and Murat, Y.S., A novel safe merging algorithm for connected vehicles using NetLogo, Elektron. Elektrotech., 2018, vol. 24, no. 3, pp. 3–7.

    Article  Google Scholar 

  40. Bozuyla, M. and Tola, A.T., MetroCar—a novel transportation system for cities, Electron. World, 2018, vol. 124, no. 1987, pp. 32–33.

  41. Stone, P. and Veloso, M., Multiagent systems: A survey from a machine learning perspective, Auton. Rob., 2000, vol. 8, no. 3, pp. 345–383.

    Article  Google Scholar 

  42. Erdur, R.C., Applicaton of software agent technology to Internet based software reuse, PhD Thesis in Computer Engineering, Izmir: Ege Univ., 2001.

  43. Lamouik, I., Yahyaouy, A., and Sabri, M.A., Smart multi-agent traffic coordinator for autonomous vehicles at intersections, 3rd International Conference on Advanced Technologies for Signal and Image Processing—ATSIP’2017, 2017, pp. 1–6.

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Funding

This work was supported by Scientific Research Projects Coordination Unit of Pamukkale University, under the project no. 2015FBE028.

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Correspondence to A. T. Tola.

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The authors declare that they have no conflicts of interest.

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Bozuyla, M., Tola, A.T. Designing a Novel Transportation System Using Microscopic Models and Multi-Agent Approach. Aut. Control Comp. Sci. 55, 125–136 (2021). https://doi.org/10.3103/S0146411621020036

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  • DOI: https://doi.org/10.3103/S0146411621020036

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