Fatih Gündoğan Martin Fellendorf

1. Real-time signal control using artificial neural networks for developing megacities ITS World Congress Orlando, October 16th – 20th, 2011 Fatih Gündoğan Martin Fellendorf Graz University of Technology, AustriaInstitute for Highway Engineering and Transport Planning www.isv.tugraz.at

2. Contents • Introduction • - Problems in developing megacities • - Need • - Objective of this study • Proposed Methodology • - System Architecture • - Traffic Pattern Recognition • - Artificial Neural Network • Results of Simulation Study • Conclusion

3. Problems in developing megacities • Driving behavior/Lane Discipline • Engineering Experience • Cost and Budget balance • High and increasing population • Increasing in number of vehicles • Inadequate Infrastructure

4. Objective of the study Development of a simplified low-cost traffic dependent signal control system using pattern recognition and artificial neural networks Step 1: Conduct a questionnaire with megacities Step 2: Development of simplified control strategy Step 3: Reduction number of detectors and placement optimization Step 4: General Evaluation of the system

5. Real Time Signal Control (Rule-Based) Check Traffic Pattern Or Threshold Check Timing Plan Collect Data Send new timing plans

6. Proposed system architecture Neural Network Controller TrafficState Estimation Counts and Occupancy Data Pattern Recognition Method Data Collection (VISSIM 5.0) Set of SignalPlans (Planning Mode) Network SignalControlOptimization (TRANSYT 13) Outputs: Travel Time Delay Number of Stops

7. Pattern Selection / Pattern Recognition • What is pattern? Pattern: A regularly repeated arrangement (especially of lines, shapes, etc. on a surface or of sounds, words, etc.) (in Longman Dictionary) • What is pattern recognition? • pattern recognition is the assignment of some sort of output value to a given input value , according to some specific algorithm. • It contains classification, clustering, regression etc. • Where can be used? • Speech, Face, Finger Prints or Traffic sign recognition

8. Recognition of Fish Species using features… • Salmon • Sea bass • Measured Features: • Width • Lightness Classification of Fish Species Feature Extraction Pre-Processing Source: Duda et all. Pattern Classification 20.10.2011 8

9. Traffic pattern recognition • Morning Peak • Afternoon Peak • Off-Peak • Weekend • Measured Features: • Volume • Occupancy Classification of Traffic States Feature Extraction Pre-Processing 20.10.2011 9

10. What are (artificial) neural networks? • Natural neurons receive signals through synapses located on the dendritesor membrane of the neuron. When the signals are received strong enough (surpass a certainthreshold), the neuron is activated and emits a signal though the axon. This signal might besent to another synapse, and might activate other neurons. • The complexity of real neurons is highly abstracted when modelling artificial neurons. These basically consist of inputs (like synapses), which are multiplied by weights (strength of the respective signals), and then computed by a mathematical function which determines the activation of the neuron. Another function (which may be the identity) computes the output of the artificial neuron (sometimes in dependance of a certain threshold). ANNs combine artificial neurons in order to process information

11. Simple (single layer) Perceptron 1 0 Features: X1 X2 X3 X4 W1 1 W2 F W3 W4 (Sigmoidal Function) Learning using Gradient Descent Method

12. Backpropagation • Calculate newweights for Hidden - Output Layer Calculate new weights for Input - Hidden Layer(not in this simple example) Calculate Error: X1 X2 X3 X4 F 20.10.2011 12

13. Network architecture and system parameters input layer hidden layer output layer H1 I1 H2 O1 traffic state volume & occupancy I2 H3 O2 I3 H4 O3 I4 H5 O4 Hk Im On Number of Input Neurons Im wIH Number of Hidden Neurons Hk wHO Number of Output Neurons On • Learning Rate: 0.3 • Momentum 1.0 • Starting weights (random) between 0 and 1 • Number of Iteration: 20000 • 8 Input Neurons • 12 Output Neurons • 15 Hidden Neurons • 1 Hidden layer

14. RMSE during the training RMSE Number of Iterations

15. System application using microscopic traffic flow simulation 8 4 6 5 2 3 1 2 1 System detector 3 7 Number of Lanes and Direction 230m 270m • 12 Scenarios / 12 Timing plans (AM-peak, PM-peak, night hours, weekend etc…) • Optimization of Timing plans using TRANSYT 13 • Training and Test in neural network

16. VISSIM-Neural Network Interface using VBA Microscopic traffic flow simulation: VISSIM MLP Controller in VBA volume & occupancy data signal control data COM Interface (Update interval:5 min)

17. Simulation Test • Case 0: optimized pre-timed (signal plans are optimized using TRANSYT 13 • Case 1: optimized pre-timed (change the situation to am-peak for a short-time period • Case 2: Real-time signal plan selection using proposed controller) Volume (veh/h) Timing Plan 2 CASE 0 Timing Plan 1 Traffic Pattern 1 7:00 11:00 13:00 17:00 Time (h) Volume (veh/h) Timing Plan 2 Timing Plan 2 Traffic Pattern 2 CASE 1 Timing Plan 1 CASE 2 7:00 11:00 13:00 17:00 Time (h)

18. Results

19. Conclusions • Pattern Recognition with Neural Network can be an alternative for Rule-Based Systems and reduce delays about 15% on arterial streets • The system works with less number of detectors, can be reduced further • Based on Simulation study, but each scenario could be tested. • Comparison between optimized signal plans, but in reality it is unusual. Thank you for your attention!