method integrates spatial neighbor information by aggregating temporal data in various areas and iteratively estimating the event location and the radius of event impact by examining the posterior probability base on the aggregated data application_number US 96447210 A description it is well established that network traffic varies with time of day and also varies with days of the week identifying events in such network traffic data is ideally accomplished by learning the normal traffic pattern and special event pattern however this problem is challenging due to unknown baseline for normal traffic as well as unknown event information such as event location and event duration another challenge is the fact that not only the temporal information but also the spatial information ought to be taken into account to accurately assess event influence to network it is necessary to consider an event 39 s impact on multiple neighboring cells where the cells are likely to have different traffic patterns either naturally e g proximity to a train station or driven by different events the large amount of data is another difficulty in the network traffic event detection the necessary computing might be unfeasible even though the computing increases only linearly with data markov modulated poisson process mmpp is a popular traffic model for capturing the characteristic of actual network load by describing varying rate parameters at irregular intervals according to markov process which basically asserts that the probability of a data point in a series depends.