By Pushkin Kachroo; Kaan Ozbay
Feedback Ramp Metering in clever Transportation Systems is the 1st booklet concerning utilizing suggestions keep watch over (also known as real-time site visitors keep an eye on or adaptive keep watch over by way of a few site visitors engineers) in ramp metering. It presents site visitors idea basics after which the layout of suggestions controllers for remoted and coordinated ramp metering difficulties. software program simulation code in Matlab and Paramics is equipped within the e-book in order that the reader can get a hands-on think for a few of the algorithms. With lots of examples, illustrations, and unique difficulties, this publication is superb as a textbook or reference ebook for a senior or graduate point direction at the topic, in addition to a reference for researchers in similar fields.
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Extra resources for Feedback Ramp Metering in Intelligent Transportation Systems
Example text
For a closed-loop ramp metering control, define state variables, input and output variables . Show the system in simple block diagram form. p 11. PROBLEMS 1. m and run the program. Compare the results (plots) with Figure 1-11 and explain the differences . And give the advantages of ramp metering control from the results. 2. Using the parameters given in the diagram write down the projection dynamic constraints for a ramp metering problem. (Hint: 1) Use 1 min time steps, and take the length of the freeway section as 1 unit) 2) Study the simulation code in section 6.
What is ramp metering and why is it important ? 2. Give two objectives that a ramp metering control law should satisfy 3. What is the difference between open-loop and closed-loop traffic control? 4. What is feedback control, and how can it be applied to ramp metering? 5. What kinds of sensors are used for traffic surveillance , and what traffic variables do they sense? 6. What are some mechanisms to control traffic behavior? 7. Explain why one of the objectives of ramp metering control is to keep the freeway density below critical level.
Carn+l X I I :.. ' I I I I Figure2-6: Car-Following Model For example, if we make h a linear function of the speed of the follower, we obtain Xn - X n+1 = k i: n+1 +L (32) Here k and L also are constants. By differentiating (32) we get .. 1 [. DistributedRamp Model 45 Figure 2-6: Car-Following Model For example, if we make h a linear function of the speed of the follower, we obtain (32) Here k and L also are constants. By differentiating (32) we get (33) This model is enhanced by introducing a driver delay T to the stimulus provided by the leader car.