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Uporaba metod spodbujevanega učenja za optimizacijo krmiljenja prometa na cestni arteriji : doktorska disertacija
Marsetič, Rok (Author), Žura, Marijan (Mentor) More about this mentor... This link opens in a new window, Mikoš, Matjaž (Thesis defence commission member), Lipar, Peter (Thesis defence commission member), Maher, Tomaž (Thesis defence commission member), Sever, Drago (Thesis defence commission member)

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Abstract
Današnja družba se zlasti v mestnih središčih pogosteje srečuje s prometnimi zastoji, višjimi stroški, nižjo prometno varnostjo in večjim onesnaženjem okolja. Prometne obremenitve vedno bolj presegajo kapaciteto cestne infrastrukture, posebno v koničnih urah. V mestih je velik delež križišč opremljenih s svetlobnosignalnimi napravami, ki imajo ključno vlogo pri varnem in učinkovitem vodenju prometa. Pri zagotavljanju čim krajših zamud je pomembna hitra prilagoditev krmilnih programov v realnem času. Z omejitvijo širjenja cestne infrastrukture in naraščanjem prometnih obremenitev čedalje težje ohranjamo zadovoljiv prometni nivo uslug s klasičnimi tehnikami krmiljenja. To še posebno velja ob nastanku nepredvidenih prometnih dogodkov. V takšnih situacijah so pomanjkljivosti zdajšnjih krmilnih sistemov še opaznejše. Promet je stohastične narave in spremembe v prometnem toku zahtevajo, da se krmilna strategija nenehno posodablja. Za reševanje tako zahtevnih in kompleksnih problemov optimizacije krmiljenja svetlobnosignalnih naprav potrebujemo sistem, ki se nenehno prilagaja in uči. S pomočjo umetne inteligence je mogoče razviti sisteme, ki so sposobni samostojnega učenja. V doktorski disertaciji smo predstavili sodoben pristop krmiljenja svetlobnosignalnih naprav na cestni arteriji, in sicer z uporabo algoritma spodbujevanega učenja. Predlagani algoritem omogoča hitro in samostojno učenje optimalne strategije krmiljenja v najrazličnejših prometnih razmerah. Uspešnost predlaganega algoritma smo preverili s pomočjo mikrosimulacijskega orodja, s katerim lahko z veliko natančnostjo simuliramo realne prometne razmere. Dokazali smo, da je učinkovitost predlaganega krmiljenja boljša v primerjavi s klasičnim prometno odvisnim krmiljenjem.

Language:Slovenian
Keywords:grajeno okolje, gradbeništvo, disertacije, spodbujevano učenje, cestna arterija, krmiljenje prometa, svetlobnosignalne naprave
Work type:Doctoral dissertation (mb31)
Tipology:2.08 - Doctoral Dissertation
Organization:FGG - Faculty of Civil and Geodetic Engineering
Year:2016
Publisher:[R. Marsetič]
Number of pages:XVIII, 109 str.
Place:Ljubljana
COBISS.SI-ID:7394401 This link opens in a new window
Views:1316
Downloads:472
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Secondary language

Language:English
Title:Application of reinforcement learning methods for optimization of traffic control on arterial roads : doctoral thesis
Abstract:
Nowadays, society faces several traffic related problems, such as traffic jams, time loss, lower traffic safety, increased pollution, etc., especially in urban areas. This is caused by high traffic volumes, which often exceed the capacity of the road infrastructure, particularly in peak hours. A common way of managing traffic in urban areas is traffic light control, which plays a key role in traffic safety and efficiency. To reduce delays the traffic light controllers should adjust to changing traffic volumes continuously and rapidly. Limited possibilities for road infrastructure extensions and growing traffic volumes represent a challenge for existent control techniques with increasing problem of maintaining suitable level of service. When unexpected events occur, the disadvantage of current traffic control system is even more evident. Stochastic nature of traffic and constant changes in traffic flow requires continuous adaption of traffic light controller. For solving complex problem of traffic lights optimization the system that continuously adapts and learns should be employed. Artificial intelligence approaches enable development of self-learning systems. The thesis presents a novel approach for solving problems of traffic light controller optimization with use of the reinforcement learning. The proposed algorithm enables fast and self-learning optimal strategy of traffic control in different traffic conditions. The efficiency of proposed algorithm was tested using a micro simulation tool, which simulates traffic conditions with great accuracy. The results of the performed experiments show that proposed algorithm outperforms the actuated signal controllers.

Keywords:building environment, civil engineering, thesis, reinforcement learning, road artery, traffic control, traffic lights

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