Method for predicting transport capacity demand of taxi in railway station based on multiple linear regression

Abstract

The invention discloses a method for predicting the transport capacity demand of taxies in a railway station based on multiple linear regression. In order to improve the taxi connection operation efficiency of the railway station as soon as possible and to effectively analyze and predict the taxi transport capacity of the railway station, the method selects the railway station as a research object, calls necessary data, designs a prediction model, and provides knowledge reserve for the transport capacity allocation of the railway station. The method comprises the following steps: firstly, extracting taxi data and passenger order data with a date mode for a railway station to be researched, filling missing values by adopting a linear interpolation method, and counting travel time in a finished order; predicting the number of passengers who arrive at the port and need to take taxis in the railway station, and predicting the number of the passengers who need to take the taxis by using a multiple linear regression method. According to the method for predicting the taxi transport capacity demand of the railway station, the taxi demand of the railway station can be analyzed and predicted, the transport capacity scheduling work can be deployed, the operation efficiency can be enhanced, the service reliability can be improved, and the passenger satisfaction can be improved.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a method for predicting the capacity demand of taxis at railway stations based on multiple linear regression. Background technique [0002] In important transportation hubs such as airports and railway stations, taxis are the main force to ensure the safety and convenience of passengers arriving in Hong Kong, and they are also a concern of leaders at all levels. The gathering and retention of passenger flow, especially at night, will not only bring inconvenience to the people, but also pose a major safety hazard. At present, the competent departments of the taxi industry mainly rely on their experience to deploy capacity scheduling, and there are problems such as inaccurate and untimely guarantees. For example, there are large scheduling deviations during periods of heavy passenger flow such as major holidays and event days, and capacity scheduling when extreme...

AI Technical Summary

Problems solved by technology

The gathering and retention of passenger flow, especially at night, will not only bring inconvenience to the people, but also pose a major safety hazard

At present, the competent departments of the taxi industry mainly rely on their experience to deploy capacity scheduling work, and there are problems such as inaccurate guarantees and untimely guarantees. For example, there are large scheduling deviations during periods of heavy passenger flow such as major holidays and event days, and capacity scheduling when extreme weather and other emergencies occur. Relatively lagging behind, etc., it is urgent to carry out the analysis and related forecasting of taxi transport capacity at the station

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