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Signal Timings (signal + timing)
Selected AbstractsBi-level Programming Formulation and Heuristic Solution Approach for Dynamic Traffic Signal OptimizationCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2006Dazhi Sun Conventional methods of signal timing optimization assume given traffic flow pattern, whereas traffic assignment is performed with the assumption of fixed signal timing. This study develops a bi-level programming formulation and heuristic solution approach (HSA) for dynamic traffic signal optimization in networks with time-dependent demand and stochastic route choice. In the bi-level programming model, the upper level problem represents the decision-making behavior (signal control) of the system manager, while the user travel behavior is represented at the lower level. The HSA consists of a Genetic Algorithm (GA) and a Cell Transmission Simulation (CTS) based Incremental Logit Assignment (ILA) procedure. GA is used to seek the upper level signal control variables. ILA is developed to find user optimal flow pattern at the lower level, and CTS is implemented to propagate traffic and collect real-time traffic information. The performance of the HSA is investigated in numerical applications in a sample network. These applications compare the efficiency and quality of the global optima achieved by Elitist GA and Micro GA. Furthermore, the impact of different frequencies of updating information and different population sizes of GA on system performance is analyzed. [source] Application of LQ modelling and optimization in urban traffic controlOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 6 2003Majura F. Selekwa Abstract The increasing congestion on urban streets demands traffic control signal timing to be well co-ordinated and optimized even during the transition between timing patterns used in different periods of time-of-day (TOD). The TOD timing plans, defined by fixed-time co-ordination parameters, need to change from one TOD period to another. The current methods used in transitioning are aimed at achieving quick transition rather than optimizing traffic flow during the transition period. As a result, they generally cause increased vehicle delays during the transition period particularly for vehicles on the minor street, which face lengthened red times. This paper proposes a quadratic optimization method that can be used to reduce disutility measures to motorists during the transition period. The transition is modeled as a linear dynamic process, and the disutility measures are modeled as the sum of squares of the deviations of the co-ordination parameters,that is, cycle length, phase split, and offset,from the optimal values during the transition. A linear quadratic (LQ) optimization technique of optimal control is used to determine the step size and the number of steps necessary to complete the transition with minimum disutility. The proposed transition period optimization method has the advantage that the user need not specify minimum and maximum cycle length to achieve optimization, as is the case with current methods. Simulation results for three co-ordinated intersections showed that the proposed method reduces total vehicle delay when compared to the ,immediate' transition method embedded in CORSIM traffic simulation software. This is due to the fact that vehicles on the minor street approaches get proportional green time without significantly affecting green times on the major street approach green phase. However, the method showed a slight increase in total delay for vehicles on the major street. Copyright © 2003 John Wiley & Sons, Ltd. [source] Dynamic Optimal Traffic Assignment and Signal Time Optimization Using Genetic AlgorithmsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2004H. R. Varia A simulation-based approach is employed for the case of multiple-origin-multiple-destination traffic flows. The artificial intelligence technique of genetic algorithms (GAs) is used to minimize the overall travel cost in the network with fixed signal timings and optimization of signal timings. The proposed method is applied to the example network and results are discussed. It is concluded that GAs allow the relaxation of many of the assumptions that may be needed to solve the problem analytically by traditional methods. [source] Sensitivity analysis on stochastic equilibrium transportation networks using genetic algorithmJOURNAL OF ADVANCED TRANSPORTATION, Issue 3 2004Halim Ceylan Abstract This study deals with the sensitivity analysis of an equilibrium transportation networks using genetic algorithm approach and uses the bi-level iterative sensitivity algorithm. Therefore, integrated Genetic Algorithm-TRANSYT and Path Flow Estimator (GATPFE) is developed for signalized road networks for various level of perceived travel time in order to test the sensitivity of perceived travel time error in an urban stochastic road networks. Level of information provided to drivers correspondingly affects the signal timing parameters and hence the Stochastic User Equilibrium (SUE) link flows. When the information on road system is increased, the road users try to avoid conflicting links. Therefore, the stochastic equilibrium assignment concept tends to be user equilibrium. The GATPFE is used to solve the bi-level problem, where the Area Traffic Control (ATC) is the upper-level and the SUE assignment is the lower-level. The GATPFE is tested for six-junction network taken from literature. The results show that the integrated GATPFE can be applied to carry out sensitivity analysis at the equilibrium network design problems for various level of information and it simultaneously optimize the signal timings (i.e. network common cycle time, signal stage and offsets between junctions). [source] | ||||||||||