Research On Preprocessing Methods Of Traffic Data For Traffic Control System

The study on the detection and forecast for traffic flow condition identification is an important prerequisite of traffic management and one of the important issues of Intelligent Transportation Systems (ITS). The objective of traffic flow condition identification is to recognize the condition of traffic flow timely and accurately, take effective traffic incident management, traffic control and traffic flow guidance measures to clean out traffic incidents timely. In this way, the loss of casualties will be lowered and the second incidents will be avoided. In addition, the control measures of transportation demand can be taken effectively and the route recommendations can be available to travelers so as to lessen traffic pressure, economize energy and reduce pollution.According to the demands of traffic control system in data, this paper has analyzed the principle of the loop detector which is used universally , and it's known that it can't get speed from single loop detector, so the maximum likelihood method (MLM) was established to get vehicle's speed. Furthermore, based on the relative relationships between the traffic volumes of intersections, the traffic data at non-detector intersections can be forecasted. Above finally, traffic flow condition can be identificated using the forecast result above, in which the author proposed the condition identification both on road and intersection. The results of tests proved that both algorithms perform better in traffic flow condition.