A Method Based on Multiple Linear Regression Forecasting Taxi Capacity Demand in Railway Stations

Abstract

The invention discloses a method for predicting the taxi capacity demand of a railway station based on multiple linear regression. In order to improve the operation efficiency of taxi connection at the railway station as soon as possible, it is urgently necessary to effectively analyze and predict the taxi capacity of the railway station. This invention selects the railway station as the research object, obtains the necessary data, and designs a prediction model to provide the capacity of the railway station. Deployment provides a knowledge base. First of all, for the railway station to be studied, the taxi data and passenger order data with date patterns are extracted, and the missing values ​​are filled by linear interpolation method, and the travel time in the completed order is counted. By predicting the number of arrivals at the railway station who need to take a taxi, use the multiple linear regression method to predict the number of vehicle demand. The method for predicting the taxi capacity demand at the railway station proposed by the present invention can analyze and predict the taxi demand at the railway station, deploy capacity scheduling work, enhance operational efficiency, improve service reliability, and improve passenger satisfaction.

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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