| Summary: | In order to better predict the traffic states on freeways and make management decisions, a hybrid model of support vector machine (SVM) and K-nearest neighbor (KNN) is proposed for short- term freeway exiting volume prediction.
First, a historical data set is built by using the freeway toll data. The abnormal toll records, such as records that have same entry and exit station, illogical time record and abnormal travel speed, are excluded by data quality control. Based on the historical dataset, it is found that the exiting volume has periodical variation over time which provides the basis of the short-term prediction. Then, the historical data set is cross-classified into twelves groups based on the day of week and time of day. The prediction has been done for each group. Finally, the prediction is accomplished by the hybrid-model of SVM and KNN. The exiting volumes of previous time periods are used as the feature vector for KNN and SVM. Besides, a dynamic weight is adopted for the prediction of current time period based on the latest prediction accuracy of KNN and SVM.
The model results indicate that the proposed algorithm is feasible and accurate. The Mean Absolute Percentage Error is under 10%. When comparing with the results of single KNN or SVM method, the results show that the combination of KNN and SVM can improve the reliability of the prediction significantly. The proposed method can be implemented in the on-line application of exiting volume prediction, which is able to consider different vehicle types.
|
|---|