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A comparison of algorithms used in traffic control systems
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
En jämförelse av algoritmer i trafiksystem (Swedish)
Abstract [en]

A challenge in today's society is to handle a large amount of vehicles traversing an intersection. Traffic lights are often used to control the traffic flow in these intersections. However, there are inefficiencies since the algorithms used to control the traffic lights do not perfectly adapt to the traffic situation. The purpose of this paper is to compare three different types of algorithms used in traffic control systems to find out how to minimize vehicle waiting times. A pretimed, a deterministic and a reinforcement learning algorithm were compared with each other. Test were conducted on a four-way intersection with various traffic demands using the program Simulation of Urban MObility (SUMO). The results showed that the deterministic algorithm performed best for all demands tested. The reinforcement learning algorithm performed better than the pretimed for low demands, but worse for varied and higher demands. The reasons behind these results are the deterministic algorithm's knowledge about vehicular movement and the negative effects the curse of dimensionality has on the training of the reinforcement learning algorithm. However, more research must be conducted to ensure that the results obtained are trustworthy in similar and different traffic situations.

Abstract [sv]

En utmaning i dagens samhälle är att hantera en stor mängd fordon som kör igenom en korsning. Trafikljus används ofta för att kontrollera trafikflödena genom dessa korsningar. Det finns däremot ineffektiviteter eftersom algoritmerna som används för att kontrollera trafikljusen inte är perfekt anpassade till trafiksituationen. Syftet med denna rapport är att jämföra tre typer av algoritmer som används i trafiksystem för att undersöka hur väntetid för fordon kan minimeras. En tidsbaserad, en deterministisk och en förstärkande inlärning-algoritm jämfördes med varandra. Testerna utfördes på en fyrvägskorsning med olika trafikintensiteter med hjälp av programmet Simulation of Urban MObility (SUMO). Resultaten visade att den deterministiska algoritmen presterade bäst för alla olika trafikintensiteter. Inlärningsalgoritmen presterade bättre än den tidsbaserade på låga intensiteter, men sämre på varierande och högre intensiteter. Anledningarna bakom resultaten är att den deterministiska algoritmen har kunskap om hur fordon rör sig samt att dimensionalitetsproblem påverkar träningen av inlärningsalgoritmen negativt. Det krävs däremot mer forskning för att säkerställa att resultaten är pålitliga i liknande och annorlunda trafiksituationer.

Place, publisher, year, edition, pages
2018. , p. 30
Series
TRITA-EECS-EX ; 2018:209
Keywords [en]
traffic lights, reinforcement learning, traffic control system, comparison, intersection, waiting time, longest queue first
Keywords [sv]
trafikljus, förstärkande inlärning, trafikkontrollsystem, jämförelse, korsning, längst kö först
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-229709OAI: oai:DiVA.org:kth-229709DiVA, id: diva2:1214166
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Presentation
2018-05-29, B22, Brinellvägen 23, Stockholm, 15:00 (English)
Supervisors
Examiners
Available from: 2018-07-10 Created: 2018-06-05 Last updated: 2018-07-10Bibliographically approved

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