Urban traffic monitoring for predicting congestion and managing traffic is one of the concerns of municipal managers. In this study, using user’s smartphone’s Wi-Fi, are proposed a cost efficient approach to collect traffic data and to detect the traffic condition of the roads. Firstly, the number of users is collected by a Wi-Fi scanner during each one-hour intervals. After extracting the feature from thesedata, the traffic conditions are estimated by different supervised learning machine including Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Naïve Bayes (NB). Finally, the threshold is calculated using thebest cut-off point in Receiver Operating Characteristic (ROC) curve. The results show that KNN algorithm with the accuracy of 93.3% is the best estimator. The precision of traffic congestion detection in this algorithm is 92.1%. Also, the threshold was 38 for the case study. This threshold can be shown beginning of congestion in the road and can be used for applying traffic policies to management conditions
