Calibration of microsimulation traffic model using neural network approach

Outline

• Abstract
• Keywords
• 1. Introduction
• 2. Data Gathering
• 3. Calibration of Microsimulation Model Using Neural Network Approach
• 3.1. Input Parameters of the Model
• 3.2. Application of Neural Networks to Traveling Time Prediction
• 3.3. Traffic Microsimulation Model Calibration Program
• 3.4. Analysis of Program Calibration Results
• 3.5. Queue Parameters
• 4. Validation of the Calibrated Model
• 4.1. First Validation
• 4.2. Second Validation
• 5. Discussion
• 6. Conclusion
• References

Conclusions

Within this paper, a new calibration method encompassing the application of a neural network for the prediction of the results of simulations of microsimulation traffic model within the computer calibration program is analyzed.

Traveling time and queue parameters are the traffic indicators which are analyzed in the process of calibration and validation of the model, because they are easily measurable in real traffic conditions. The microsimulation model selected for the study of the applicability of neural networks in the calibration procedure is the VISSIM, and two urban roundabouts were used as the basis of the experiment.

Results have shown that a neural network is applicable in the process of calibration of the examined microsimulation model. Basic steps of the calibration method by means of neural network application include:

– A minimum of two sets of measured data (the first for the model calibration and the second for the validation), which are: vehicle and pedestrian traffic load, traffic distribution, mean traveling time between two chosen measuring points easy to enter into the model layout, queue parameters at one of the entrances into the roundabout;

– Creating the VISSIM simulation database (variations of values of input parameters) for neural network learning;

– Selection of neural network (General Regression Net, Iterative type, with a logistic activation function gave the best response in this study) and obtaining the function of prediction for the program calibration;

– Program calibration (program designed in MATLAB);

– Checking the results of the program calibration output file in the VISSIM model for the mean traveling time and queue parameters as well as checking the best result in the validation procedure;

– Validation procedure involves comparison of simulated and measured data for the second set of data measured in the field;

– Combination of input parameters, that gives the simulation results which best approximate the measured data, is optimal = calibrated VISSIM.

– The described basic steps of calibration are applicable, not only in the VISSIM, but in other microsimulation models.

The question about the optimal size of database for microsimulation modeling of neural network learning and the question about a possibility of finding a configuration of neural network which would provide a better response than the chosen General Regression Network both remain unanswered, since these answers require further research. Regarding the optimisation issues, the question is whether the obtained optimum is local or absolute minimum and how good should possible local minimum approximate the absolute one. In practical terms, the comparison of the measured field results with simulation results obtained with calibrated and default values of input parameters, gives a basic insight into the performance of the calibration procedure.