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A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

Jin, Junchen (författare)
KTH,Transportplanering, ekonomi och teknik,System Simulation & Control (S2CLab)
Ma, Xiaoliang (författare)
KTH,Transportplanering, ekonomi och teknik,System Simulation & Control (S2CLab)
Kosonen, Iisakki (författare)
 (creator_code:org_t)
Elsevier, 2017
2017
Engelska.
Ingår i: Advances in Engineering Software. - : Elsevier. - 0965-9978 .- 1873-5339. ; 114, s. 348-360
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Traffic flow is considered as a stochastic process in road traffic modeling. Computer simulation is a widely used tool to represent traffic system in engineering applications. The increased traffic congestion in urban areas and their impacts require more efficient controls and management. While the effectiveness of control schemes highly depends on accurate traffic model and appropriate control settings, optimization techniques play a central role for determining the control parameters in traffic planning and management applications. However, there is still a lack of research effort on the scientific computing framework for optimizing traffic control and operations and facilitating real planning and management applications. To this end, the present study proposes a model-based optimization framework to integrate essential components for solving road traffic control problems in general. In particular, the framework is based on traffic simulation models, while the solution needs extensive computation during the engineering optimization process. In this work, an advanced genetic algorithm, extended by an external archive for storing globally elite genes, governs the computing framework, and in application it is further enhanced by a sampling approach for initial population and utilizations of adaptive crossover and mutation probabilities. The final algorithm shows superior performance than the ordinary genetic algorithm because of the reduced number of fitness function evaluations in engineering applications. To evaluate the optimization algorithm and validate the whole software framework, this paper illustrates a detailed application for optimization of traffic light controls. The study optimizes a simple road network of two intersections in Stockholm to demonstrate the model-based optimization processes as well as to evaluate the presented algorithm and software performance.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Software Engineering (hsv//eng)

Nyckelord

Simulation-based optimization
Archived genetic algorithm
Road traffic controls
Traffic light control
Object-oriented software framework
Transportvetenskap
Transport Science
Datalogi
Computer Science

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