Research on Parking Scale Prediction of the First-Class District at Xining City Based on Regional Location Parking

摘要

To alleviate urban parking problems, considering demand scale analysis of influencing factors, we selected appropriate demand-predicting models according to different service objects and parking behavior mechanisms of construction, off-street, and on-street parking facilities. Road network capacity and location condition influence coefficients were introduced, and berth turnover rate was used for conversion and correction. A reasonable district parking scale prediction model based on location conditions was established. We predicted the scale of parking facilities in the first-class area and compared the prediction results with the results from traditional parking generation rate method and parking system planning results, which showed the error rate between the predicted result of the traditional parking generation rate and the planned parking berth was 12.76%, and the error rate of the district parking demand predicting model and the planned parking berth was 7.6%. This method had certain rationality and applicability.