Application of grey models to traffic flow prediction at non-detector intersections

Conventional prediction methods require large number of samples while traffic flow data at non-detector intersections is very limited. To solve this problem a new self-adapting GM (1,1) model to predict traffic flow at non-detector intersections using the grey theory is proposed. The practical implementation of the new model through calculating program compiled was accomplished by a case study in Changshu, Jiangsu province. Results show that self-adapting GM (1,1) can achieve much better prediction accuracy than that of total data GM (1,1).