Applying quality control to traffic condition monitoring

The problem of traffic congestion on urban freeways has led to the development of traffic management systems responsible for monitoring and responding to traffic conditions. Most systems use the traffic data they collect to monitor traffic conditions through incident detection algorithms. However, these algorithms generally do not take into account the relationships among the traffic variables measured and have not performed very well in practice. Many systems now archive the traffic data collected at many locations throughout their coverage areas. The archived data, along with advances in computing power; make more complex condition monitoring methods feasible. Multivariate statistical quality control (MSQC) has the potential to evaluate current conditions with respect to normal conditions based on historical data. The state of the highway system can be assessed using Hotelling's T-2 to measure the distance of current data from om the center of the region defined by historical data. This measurement and additional calculations can provide an assessment of normality across a range of conditions, rather than the binary output typical of incident detection algorithms. Extensions on basic MSQC can provide additional insight into causes of abnormal traffic conditions.