ارائه یک ساختار هوشمند برای مدیریت ترافیک در شرایط اضطرار

نویسنده

استادیار، دانشکده مهندسی صنایع، دانشگاه خواجه نصیرالدین طوسی، تهران، ایران

چکیده

مدیریت ترافیک مسیرها، شامل بهبود روانی ترافیک در شبکه مسیرها و کاهش اثرات منفی ترافیک مانند مسدود شدن مسیر وسایل نقلیه امدادی، تاخیر، زمان انتظار و استرس رانندگان وسایل امدادی است. بهبود جریان عبور و مرور ‌در شبکه مسیرها می‌تواند تعداد خودروهای بیشتری را بدون نیاز به کاهش سرعت متوسط پشتیبانی کند. از طرف دیگر در کاهش موقعیت‌های پر ترافیک، با جلوگیری از افزایش استفاده از یک مسیر در یک زمان خاص، با فراهم کردن مسیرهای پیشنهادی با زمان کوتاه‌تر، تاثیر گذار است. رویکرد ارائه شده در این مقاله، امکان اصلاح هوشمندانه و بلادرنگ عبور و مرور مسیرها در شبکه را بر اساس تغییرات بلادرنگ در شرایط اضطرار فراهم می‌کند. عامل‌ها هم برای نمایش خودروها و هم برای نمایش سیستم‌های کنترلی گوناگون ترافیک استفاده می شوند. روش‌های چند عامله، برای مدل کردن سیستم‌های پیچیده‌ای که در آن‌ها تعداد بیشماری واحدهای مستقل بر هم اثر گذاشته و یا  همکاری دارند، استفاده می‌شوند. افزایش پیچیدگی و اندازه کل شبکه‌ حمل و نقل، نیاز به سیستم‌های چندعاملی را افزایش می‌دهد. این مقاله یک سیستم چند عامله جدید برای مدیریت عبور و مرور را که از هوش تجمعی استفاده می‌کند، ارائه کرده است. به این منظور از  هوش تجمعی برای کاهش زمان محاسبات استفاده شده است. وسایل نقلیه ورودی، در مجموعه ای از مسیرها توزیع شده، زمان  طی شده مسیر، کاهش یافته و اطلاعات عبور و مرور به صورت بلادرنگ برای وسایل نقلیه امدادی افزایش می‌یابد.

کلیدواژه‌ها


-Abraham, A., Jarvis, D., Jarvis, J.and Jain, L. (2008) "Innovations in intelligent agent technology. J. Multi agent Grid Syst. Vol.4. No.4, pp. 347–349.
-Adler, J. L., Satapathy, G., Manikonda, V., Bowles, B. and Blue, V. J. (2005). "A multi-agent approach to cooperative traffic management and route guidance".Transport. Res. Part B: Methodology. Vol.39 No.4, pp. 297–318.
-Arslan, T. and Khisty, C.J. (2005) "A rational reasoning method from fuzzy perceptions in route choice. Fuzzy Sets Syst. Vol.150.No.3, pp. 419–435.
-Balaji, P.G. and Srinivasan, D. (2011) “Type-2 fuzzy logic based urban traffic management. Eng. Appl. Artif. Intell. Vol.24, No.1, pp.12–22.
-Bazzan, A. L. C., Klügl, F. and Ossowski, S. (2005) "Agents in traffic and transportation: exploring autonomy in logistics, management, simulation, and cooperative driving”, Transport. Res. Part C: Emer. Technol. 13 (4), 251–254.
-Bertelle, C., Dutot, A., Lerebourg, S. and Olivier, D. (2003) "Road traffic management based on ant system and regulation model". In: Proc. of the Int. Workshop on Modeling and Applied Simulation, pp. 35–43.
-Bierlaire, M. and Frejinger, E. (2008) "Route choice modeling with network-free data. Transport", Res. Part C: Emer. Technol. Vol.16 .No.2.pp. 187–198.
- Boushehri, S. N. S., Hosseininasab, .S. N.  and Kazemi, A. (2015) "Selection of Transportation Investment Projects in regard to Spatial Equity (Case Study: Isfahan Transportation Network) , Journal of Transportation Engineering, Vol.6, No. 3, pp. 445-462.
-Chen, B., Cheng, H.H., (2010) “A review of the applications of agent technology in traffic and transportation systems". IEEE Trans. Intell. Transport. Syst.vol. 11. No.2 pp. 485–497.
-Chen, Y., Yang, B., Abraham, A., Peng, L. (2007) "Automatic design of hierarchical Takagi–Sugeno type fuzzy systems using evolutionary algorithms". IEEE Trans. Fuzzy Syst. Vol.15. No. 3. pp 385–397.
-Chen, B., Cheng, H. H. and Palen, J. (2009)" Integrating mobile agent technology with multi-agent systems for distributed traffic detection and management systems". Transport. Res. Part C: Emer. Technol. Vol.17. No.1.pp. 1–10.
-Claes, R., Holvoet, T. and Weyns, D. (2011) "A decentralized approach for anticipatory vehicle routing using delegate multiagent systems, IEEE Trans. Intell. Transport. Syst. Vol.12, No. 2, pp. 364–373.
-Daganzo, C. F. (2013) "System optimum and pricing for the day-long commute with distributed demand, autos and transit, Transp. Res. Part B. No.55.pp. 98–117.
-D’Acierno, L., Montella, B. and De Lucia, F. (2006) “A stochastic traffic assignment algorithm based on ant colony optimisation, Proc. of the Ant Colony Optimization and Swarm Intelligence, LNCS, vol. 4150. pp. 25–36.
-D’Acierno, L., Gallo, M. and Montella, B. (2012) "An ant colony optimisation algorithm for solving the asymmetric traffic assignment problem", Eur. J. Oper. Res. vol. 217. No.2, pp. 459–469.
-Deng, Y., Tong, H. and Zhang, X. (2010) " Dynamic shortest path in stochastic traffic networks based on fluid neural network and particle swarm optimization", In:Proc. of the 6th Int. Conf. on Natural Computation ICNC, IEEE, pp. 2325–2329.
-Dia, H., (2002). "An agent-based approach to modeling driver route choice behavior under the influence of real-time information". Transport. Res. Part C: Emer.Technol. vol.10. No. (5–6).pp. 331–349.
-Dijkstra, E.W. (1959) "A note on two problems in connexion with graphs, Numer. Math. Vol.1.pp. 269–271.
-Dorigo, M., Maniezzo, V. and Colorni, A. (1996) "Ant system: optimization by a colony of cooperating agents". IEEE Trans. Syst. Man Cybernet. Part B. vol. 26 .No. 1, pp. 29–41.
-Drogoul, A., Vanbergue, D. and Meurisse, T. (2003) "Multi-agent based simulation: where are the agents? " Proc. of the Multi-Agent-Based Simulation, LNCS, vol. 2581.pp. 43–49.
-Ferber, J., Michel, F. and Baez, J. (2005) "AGRE: integrating environments with organizations. In: Environments for Multi-Agent Systems", LNCS, vol. 3374. pp. 48–56.
-García-Nietoa, J., Albaa, E. and Olivera, A.C. (2012) "Swarm intelligence for traffic lightscheduling: application to real urban areas", Eng. Appl. Artif. Intell. Vol.25. No.2.pp. 274–283.
-Ghatee, M., Hashemi, S. M. (2009) "Traffic assignment model with fuzzy level of travel demand: an efficient algorithm based on quasi-Logit formulas, Eur. J. Oper. Res. vol. 194.pp.432–451.
-Gong, J., Yu, Z. and Chen, N. (2007)" An analysis of drivers’ route choice behavior in urban road networks based on GPS data", In: Proc. of the Int. Conf. on Transportation Engineering ICTE, American Society of Civil Engineers, pp. 515–520.
-Gonzales, E. J. and Daganzo, C. F. (2013) "The evening commute with cars and transit: duality results and user equilibrium for the combined morning and evening peaks", Transp. Res. Part B., vol 57.pp. 286–299.
-Hawas, Y. E. (2004) "Development and calibration of route choice utility models: neuro-fuzzy approach, J. Transport. Eng. Vol. 130. No.2, pp.171–182.
-Kallel, I., Mezghani, S. and Alimi, A.M. (2008b)" Towards a fuzzy evaluation of the adaptivity degree of an evolving agent". In: Proc. of the 3rd Int. Workshop on Genetic and Evolving Fuzzy Systems GEFS, IEEE, pp. 29–34.
-Katwijk, R.V., Koningsbruggen, P.V., (2002). "Coordination of traffic management instruments using agent technology". Transport. Res. Part C: Emer. Technol. Vol.10. No.5–6.pp. 455–471.
-Kefi, S., Kammoun, M. H., Kallel, I. and Alimi, A. M. (2010) “Hybrid fuzzy-Muti-Agent planning for robust mobile robot motion, In: Proc. of the IEEE World Congress on Computational Intelligence WCCI, IEEE, pp. 1886–1893.
-Lee, M. L., Chung, H. Y. and Yu, F. M. (2003) ” Modeling of hierarchical fuzzy systems", Fuzzy Sets Syst. Vol. 138. No.2.pp. 343–361.
-Kammoun, M. H., Kallel, I., Casillas, J. and Alimi, A.M. (2010) "An adaptive vehicle guidance system instigated from ant colony behavior", In: Proc. of the IEEE Int. Conf. on Systems, Man, and Cybernetics SMC, IEEE, pp. 2948–2955.
-Liu, W., Yang, H. and Yin, Y. (2014a) “Expirable parking reservations for managing morning commute with parking spaces constraints", Transp. Res. Part C .vol.44.pp. 185–201.
-Liu, W., Yang, H., Yin, Y. and Zhang, F. (2014b) " A novel permit scheme for managing parking competition and bottleneck congestion". Transp. Res. Part C. vol.44.pp. 265–281.
-Liu, W., Yang, H., Yin, Y., (2014c). "Traffic rationing and pricing in a linear monocentric city”,  J. Adv. Transp. vol. 48. No.6.pp.655–672.
-Liu, W., Yin, Y. and Yang, H. (2015) "Effectiveness of variable speed limits considering commuters’ long-term response", Transp. Res. Part B (in press). doi:http://dx.doi.org/10.1016/j.trb.2014.12.001.
-Meignan, D., Simonin, O. and Koukam, A. (2007) “Simulation and evaluation of urban bus-networks using a multi agent approach", Simul. Model. Pract. Theory. vol. 15. No. 6.pp. 659–671.
-Nie, Y. and Yin, Y. (2013) "Managing rush hour travel choices with tradable credit scheme”, Transp. Res. Part B. No.50, pp.1–19.
-Panwai, S. and Dia, H. (2006) "A fuzzy neural approach to modelling behavioural rules in agent-based route choice models, In: Proc. of the 4th Int. Workshop on Autonomous Agents in Traffic and Transportation ATT@AAMAS, Future University, pp. 70–79.
-Peeta, S. and Yu, J.W. (2004) "Adaptability of a hybrid route choice model to incorporating driver behavior dynamics under information provision". IEEE Trans. Syst. Man Cybernet. Part A: Syst. Hum. Vol.34. No.2. pp. 243–256.
-Ridwan, M. (2004) "Fuzzy preference based traffic assignment problem. Transport", Res. Part C: Emer. Technol. Vol.12. No. (3–4).pp. 209–233.
-Shirmohammadi, N., Zangui, M., Yin, Y. and Nie, Y. (2013) “Analysis and design of tradable credit schemes under uncertainty". Transp. Res. Rec., No.2333, pp.27–36.
-Srinivasan, D. and Choy, M.C. (2006) " Cooperative multi-agent system for coordinated traffic signal control. IEE Proc. Intell". Transport. Syst. Conf. vol.153. No.1.pp. 41–50.
- Tari,F.,  Kamalabadi,E.N. and  Moghaddam, S. K. (2015)  " Pricing of arterial links of urban transportation networks using bilevel programming problem," Journal of Transportation Engineering, Vol.6.No.3.pp 397-412
-Tian, L.J., Yang, H. and Huang, H. J. (2013), " Tradable credit schemes for managing bottleneck congestion and modal split with heterogeneous users", Transp. Res.Part E. No.54. , pp. 1–13.
-Van den Berg, V. (2014) “Coarse tolling with heterogeneous preferences", Transp. Res. Part B .No. 64.pp. 1–23.
-Wada, K. and Akamatsu, T. (2013) "A hybrid implementation mechanism of tradable network permits system which obviates path enumeration: an auction mechanism with day-to-day capacity control", Transp. Res. Part E. No.60. pp. 94–112.
-Xiao, F. and Zhang, H. M. (2013) "Pareto-improving and self-sustainable pricing for the morning commute with nonidentical commuters". Transp. Sci. vol.48. No. 2. , pp.159–169.
-Xiao, L. L., Huang, H.J. and Liu, R. (2013a) "Congestion behavior and tolls in a bottleneck model with stochastic capacity", Transp. Sci. http://dx.doi.org/10.1287/trsc.2013.0483.
-Xiao, F., Qian, Z. and Zhang, H.M. (2013b) “Managing bottleneck congestion with tradable credits, Transp. Res. Part B. No. 56.pp. 1–14.
-Xiao, L. L., Liu, R. and Huang, H. J. (2014) "Stochastic bottleneck capacity, merging traffic and morning commute", Transp. Res. Part E. No. 64.pp. 48–70.
-Yang, Z., Yu, B. and Cheng, C. (2007) "A parallel ant colony algorithm for bus network optimization, Comput.-Aided Civil Infrastruct. Eng. vol. 22. No. 1.pp 44–55.
-Yang, H., Liu, W., Wang, X. and Zhang, X.N. (2013) "On the morning commute problem with bottleneck congestion and parking space constraints", Transp. Res. Part B. No. 58. pp. 106–118.
-Zhang, F., Yang, H. and Liu, W. (2014) "The downs–thomson paradox with responsive transit service". Transp. Res. Part A. No.70, pp244–263.