Document Type : Research Article
The performance of many traffic control strategies depends on how much the traffic flow models have been accurately calibrated. One of the most applicable traffic flow model in traffic control and management is LWR or METANET model. Practically, key parameters in LWR model, including free flow speed and critical density, are parameterized using flow and speed measurements gathered by inductive loop detectors and Closed-Circuit TV. The challenging problem here is continuous changes in these parameters due to traffic conditions (traffic composition, incidents) and environmental factors (dense fog, strong wind, snow) and missing data. Here, Maximum Likelihood approaches have been developed to LWR model identification while inaccurate observations are available at the traffic control center. Maximum Likelihood method has been accomplished via the employment of an Expectation Maximization algorithm. To approximate first and second derivative of optimal filter without sticking in analytical complexities, here EM algorithm has been implemented based on particle filters and smoothers. Two convincing simulation results for two set of field traffic data have been used to demonstrate the effectiveness of the proposed approaches.