In this thesis, we study and work on traffic data analysis of highways and urban areas. Using physics science besides traortation engineering enable us to extract information from traffic data-set, which can be speed, density or flow. Using clustering methods such as K-mean clustering algorithm, and also statistical analysis methods such as Fokker-Planck equation and Markov chain enable us to categorize and define traffic phases. We showed that for highways under study, the three phase After that, to have a better vision of traffic, traffic modeling named "TASEP dynamic" borrowed from physics is used. First we showed that this simple model can show and explain the traffic features. With the help of TASEP dynamic we show that wide-moving-jam state has a multiple stable states and this is the reason of seeing scatter data at this stage. We also showed that TASEP dynamic can explain hysteresis effect. We showed hysteresis like effect can happen in the system, not only because of the structure of the highway (network), but also the different ranges of in/out rates. This modeling showed that the hysteresis effect happens because of multiple stable states in wide-moving-jam state. Any small changes in in/out-rate value can change the state of the system, which has a multiple state state, and cause hysteresis effect. As a final work, we worked on speed data of 40 urban streets of Philadelphia, PA. We chose a downtown area. Using the definitions of information flow we found the connectivity between streets, i.e., we found how streets can affect each other. We show that we can cluster streets in a way that only streets with high affection on each other will be in the same group. At the final step, we showed that the information of the clustering method can help us to have a better prediction by using the right features for prediction method.
