To capture the complex nature of intersection queue dynamics, this research proposed a recursive nonparametric regression model and implemented it to forecast traffic flows and queue evolution in a congested actuated intersection. The major contribution of this study is that the proposed model can be used to substitute traditional simulation software in the lower level of a real time traffic control system to search the optimal control variables, and then utilize the found solutions as the inputs in the simulation software in the upper level of that control system to attain the system performances. In this way, the advantages of traditional mathematical modeling approach and simulation software could be both utilized, while their disadvantages could be effectively avoided. The proposed model also takes the external un-quantifiable or non-easily quantifiable factors influence on the traffic pattern into consideration. Moreover, its multi-step prediction ability and the usage of an advanced data structure makes the proposed approach has the potential to be applied in real-time control. As an application, a case study based on Baltimore-Washington network is presented at the end of this paper. The numerical results are reasonable.
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