A POI-Based Urban Traffic Demand Forecasting Method

Abstract

The invention discloses a method for predicting urban traffic demand based on Point of Interest (POI). The method classifies POI data in urban space according to a two-level classification system; The value of each factor is normalized; then the weight value of each factor in the ability factor list is calculated, including the calculation of the occurrence ability weight value and attractive ability weight value of each factor and the corresponding normalized weight value; finally According to the normalized value of each factor in the factor list and the corresponding normalized weight value, the travel generation capability index of POI is calculated. The invention adopts real-time updated network open-source data, and calculates the travel generation capability index of the POI data through the position, category and other attributes, which greatly improves the accuracy of urban traffic demand prediction and simplifies the prediction operation process.

Description

technical field [0001] The invention belongs to urban traffic transportation planning and management technology, in particular to a POI-based urban traffic demand forecasting method. Background technique [0002] The amount of travel generated, also known as travel demand, reflects the travel intensity of residents' social activities in a certain area in the field of urban transportation. The result of travel demand prediction is the key decision-making basis for determining the scale of urban transportation infrastructure construction, and it is an important content that needs to be measured and calculated in the practice of various urban transportation planning projects. [0003] In terms of traffic demand prediction, the existing technology adopts the "four-stage" prediction method, which is to predict the travel volume of local plots based on the urban land use status and the travel generation rate of different types of land, and then further integrate it Calculate the ...

AI Technical Summary

Problems solved by technology

[0004] 1. The accuracy of this aggregation method depends entirely on the accuracy of the land use data used, and the urban land use status is dynamically changing, so it is difficult to obtain data that accurately reflects the land use status in practice;

[0005] 2. In the actual operation process, it is necessary to measure the size of all types of plots in the target area, and the workload is cumbersome and arduous

[0006] In addition, the activity-based prediction method proposed on the basis of the land use data prediction method can greatly improve the prediction accuracy, but this method fails to reflect the individual travel activities from the essence of land use. At the same time, the individual travel The acquisition of activity chain data is also a complex and difficult process, so this method is also difficult to apply in project practice

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