The Real Time Traffic Control Strategy of Oversaturated Intersections on Urban Traffic Arterial

Author(s):  
Xia Luo ◽  
Yang Wu ◽  
Lei Lei
Author(s):  
Rob van Kooten ◽  
Pieter Imhof ◽  
Karst Brummelhuis ◽  
Maurits van Pampus ◽  
Anahita Jamshidnejad ◽  
...  

Author(s):  
Ghulam Q. Memon ◽  
A.G. R. Bullen

The application of modern heuristic techniques, neural networks, simulated annealing, tabu search, and genetic algorithms for multivariate optimization is receiving increased attention compared with traditional techniques like hill climbing and gradient search. In the present research the efficiency and effectiveness of genetic algorithms are investigated for their application to the real-time optimization of traffic control signal timings and are compared with the efficiency and effectiveness of the Quasi-Newton gradient search method. The development, testing, comparison, and evaluation of these two multivariate optimization techniques for inclusion in the real-time traffic adaptive control system LOCAL model developed at the University of Pittsburgh as a part of an FHWA contract to a consortium led by the University of Maryland are described. The measures of effectiveness used in the comparisons include optimum total stopped delay, percentage of improvement in total stopped delay, optimal phase timings, execution time, and code size. Testing and evaluation results indicate that genetic algorithms are more efficient and effective than the Quasi-Newton method for this real-time application.


2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  


2011 ◽  
Vol 308-310 ◽  
pp. 1582-1585
Author(s):  
Yi Sheng Huang ◽  
Tso Hsien Liao

Statechart has been utilized as a visual formalism for the modeling of complex systems. It illuminates the features on describing properties of causality, concurrency and synchronization. The reachability structure is used to represented dynamic model by a Boolean function. In this paper, we try to describe State invariant method and equation function for hierarchical tree diagram. Finally, we used them to analyze the urban traffic control systems which are modeled by using Statecharts. Their formalism provides a concept of propositional logic for presenting control strategy.


Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>


2006 ◽  
Vol 16 (1) ◽  
pp. 3-30
Author(s):  
Dusan Teodorovic ◽  
Jovan Popovic ◽  
Panta Lucic

This paper describes an artificial immune system approach (AIS) to modeling time-dependent (dynamic, real time) transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions) that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies) for different antigens (different traffic "scenarios"). This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.


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