Cross-correlating traffic flow data at different intersections in an urban transportation network is important for understanding the collective behavior of constituents in a complex system and for predicting the risk of network-wide congestion. In this work, a Random Matrix Theory (RMT) based method is used to describe the collective behavior from massive traffic data sets. Nonrandom correlations between traffic flow series recorded in the Beijing road network occur both with and without detrending. The effect of the traffic load on the correlation patterns of network-wide traffic flows is analyzed using the RMT analysis of a simulated data set collected from Paramics. The RMT analysis is also used to evaluate the impact of incidents on the network-wide traffic status. Cluster analysis is used to find the largest cluster in the network which indicates the critical congestion caused by the incident. All the results show that RMT analyses are an effective method for investigating systematic interactions in urban transportation systems. Cross-correlating traffic flow data at different intersections in an urban transportation network is important for understanding the collective behavior of constituents in a complex system and for predicting the risk of network-wide congestion. In this work, a Random Matrix Theory (RMT) based method is used to describe the collective behavior from massive traffic data sets. Nonrandom correlations between traffic flow series recorded in the Beijing road network occur both with and without dending.. The effect of the traffic load on the correlation patterns of network-wide traffic flows is The RMT analysis is also used to evaluate the impact of incidents on the network-wide traffic status. Cluster analysis is used to find the largest cluster in the network which indicates the critical all the results show that RMT analyzes are an effective method for investigating systema tic interactions in urban transportation systems.