Adaptive Traffic Control System
Performance measure against which our proposed policy to be tested is green time utilization. This performance measure is estimated by measuring the green time during which vehicles are served for an instance of a given phase and then dividing by the total length of the green time instance (i.e. minimum and maximum green time)
The supporting data for green time utilization are as follows:
* Allocated minimum and maximum green time
* vehicle departure times
* Saturation headway
These data can be retrieved from the Signal control data and stop line detector. Our objective is to maintain ideal state as long as possible if control system ...view middle of the document...
With this demand and departure rate (if known) one can find expected queued vehicles waiting for green signal.
But for heterogeneous traffic (mixed traffic) both 1 and 3 detection systems are not suitable. Using 2nd system of detection, number of departures and departure headway can be measured. In our problem also we are having inputs from stop line detectors only, so we are relying on departure rate only. To find expected number of queued vehicles at each link at end of fth phase of kth cycle, the computations are carried out by weighted average method where most recent cycle having more weight.
For model proposed here assumptions made are as follows,
1. Arrival rate does not exceeds capacity of intersection
2. One major assumption is that service rate = arrival rate
3. Even though data is available for all links, from each group of link only critical link data is considered for setting parameters.
4. Each vehicle is considered as one unit irrespective of its size and length
When control system monitors the flow at intersection phase by phase the resulting states of the phases are one of the / / . These states at end of each phase trigger the control action. In order to find better policy controller changes the signal parameters which would serve the estimated traffic demand.
The objective of the controller system is to maintain the phase as long as possible on control horizon.
Case 1. If last phase was in state
* Increase gmin and gmax or
* Reduce threshold headway value (hthr)
Case 2. If last phase was in state
* Reduce gmin and gmax or
* Increase threshold headway value (hthr)
So, to take these actions in terms of increase or decrease the control parameters requires approximate number of vehicles that may be queued-up at link in next phase.
One fundamental component of any traffic adaptive signal control system is the prediction of queue lengths at signalized intersection approaches. How to estimate queue length in real-time at signalized intersection is a long-standing problem. The problem gets even more difficult when signal links are congested. The because cumulative vehicle count for arrival traffic is not available here for our model instead of counting arrival traffic flow in the current signal cycle, we solve the problem of measuring intersection queue length by exploiting the queue discharge process in the immediate past cycle. Using traffic signal data, and applying simple forecasting model we are able to identify traffic state changes that distinguish queue discharge flow from upstream arrival traffic. Therefore, our approach can estimate time-dependent queue length even when the signal links are congested with long queues our model are evaluated by comparing the estimated maximum queue length with the ground truth data observed from the field. Evaluation results demonstrate that the proposed models can estimate long queues with...