Method for identifying and predicting bus passenger flow influence factor based on geographically and temporally weighted regression

Abstract

The invention discloses a method for identifying and predicting a bus passenger flow influence factor based on geographically and temporally weighted regression (GTWR). The method comprises steps of: extracting a traffic zone hour bus passenger flow and calculating built environment density; 2, constructing a space-time three-dimensional coordinate system according to the time of a passenger flow observation point and latitude and longitude to calculate space-time distance, and reckoning a spatial regression weight matrix according to a Gaussian function and the distance; 3, calculating a relation between a passenger flow volume and land utilization under different space-time conditions based on the GTWR; and 4, obtaining a change of a relevant parameter to a coefficient according to the calculation to perform visualization processing in time and space, and analyzing an inherent law. The method takes account of the influence of a time factor on the bus passenger flow and the built environment relation, can deeply excavate an internal relation between the passenger flow and the land utilization, accurately predicts the bus passenger flow, and provides the scientific theoretical guidance for the bus line planning and operation management.

Description

technical field [0001] The invention belongs to the technical field of intelligent traffic information processing, in particular to a method for identifying and predicting influencing factors of bus passenger flow based on spatiotemporal geographic weighted regression (GTWR). Background technique [0002] With the continuous acceleration of the urbanization process and the popularization of automobiles, the road traffic volume is gradually increasing. A large number of private cars lead to a series of problems such as traffic congestion and environmental pollution. Vigorously developing public transportation is one of the most effective ways to solve urban congestion. The analysis of public transportation demand is the foundation of developing public transportation, and the core content of public transportation demand analysis is to explore the causes of public transportation passenger flow and predict the passenger flow. Fully understanding the influencing factors of pass...

AI Technical Summary

Problems solved by technology

Passenger flow forecasting that considers the temporal non-stationarity and spatial non-stationarity of passenger flow at the same time is a new research idea, but most of the current methods are based on spatial changes and cannot incorporate time changes into the method, so the research results will lose their relevance. Time change information cannot completely characterize the changing characteristics of the relationship between land use and passenger flow, and the prediction accuracy is poor

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