Large-scale competition activity traffic distribution prediction method based on interestingness

Abstract

The invention discloses a large-scale competition activity traffic distribution prediction method based on interestingness. A traditional gravity model is relatively weak in pertinence for traffic distribution prediction of large-scale competition activities, so that the model prediction error is relatively large, and even misguidance is caused for making related traffic organizations and management and control schemes. According to the prediction method, on the basis of a traditional gravity model, travel distribution between cells is changed into travel distribution between cells and competition areas; setting an attraction amount parameter of the activity according to the scale of the large-scale activity, the number of projects and the like; a travel volume parameter is set according to the interestingness of the community residents on the competition activity; when intention intensities of campaign of populations between cells differ significantly, the interestingness may be used to adjust the distance value and set an impedance function to modify the model. According to the method, the accuracy and pertinence of traffic distribution prediction of the large-scale competition activities are effectively improved, and the method is provided for traffic distribution prediction of the large-scale competition activities in the future.

Description

technical field [0001] The invention relates to a traffic distribution prediction method for large-scale events based on the degree of interest. Background technique [0002] In recent years, with the rapid development of social economy and the vigorous development of national sports and cultural undertakings, the frequency of large-scale events held in major cities has become higher and higher, and the country and all sectors of society have paid more and more attention to various sports events. During the large-scale events and activities, the number of people traveling is huge, and the traffic service requirements are high, which makes the local and surrounding road networks bear huge traffic pressure, and the traffic security work faces great challenges. In order to reasonably respond to the challenges brought by large-scale events and ensure the smooth progress of the events, predicting the traffic volume of large-scale events in advance is one of the foundations and ke...

AI Technical Summary

Problems solved by technology

[0004] Because the traditional gravity model is relatively weak in traffic prediction for large-scale events, it may cause large prediction errors in the model, and even mislead the formulation of relevant traffic organization and control plans. The patents of the two improved gravity models are suitable for large-scale events Therefore, the present invention proposes a traffic distribution prediction method for large-scale events based on the degree of interest

Images