Urban taxi demand prediction method and device and computer equipment

A demand forecasting and taxi technology, applied in forecasting, calculation, neural learning methods, etc., can solve the problems of insufficient consideration of influencing factors, large differences in regional demand, single division of forecasting units, etc., and achieve forecasting rationality and accuracy The effects of improvement, high prediction accuracy, and fast prediction time

CN113487219AInactive Publication Date: 2021-10-08CHONGQING JIAOTONG UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2021-10-08
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a city taxi demand prediction method. The method comprises the following steps: constructing an irregular prediction unit based on taxi historical passenger carrying data, constructing a multi-time slice data set, obtaining a taxi demand prediction data set, obtaining a space influence factor data set, and determining the influence intensity of different space influence factors; adopting a CNN-LSTM-ResNet fusion algorithm, and determining a parallel combination prediction model based on the CNN-LSTM-ResNet; and inputting the passenger carrying data and the space influence factors at the current moment into the parallel combination prediction model to determine the taxi demand of the irregular prediction unit. According to the method, the convolutional neural network is innovatively used for extracting the space influence factors except time, the multi-source space-time big data in the fields of population, nature and the like is selected for space influence factor quantification, and compared with single time sequence prediction, the prediction reasonability and precision are remarkably improved.
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Description

technical field

[0001] The invention relates to the field of taxi demand forecasting, in particular to a method, device and computer equipment for urban taxi demand forecasting based on multi-source data fusion. Background technique

[0002] With the continuous expansion of the city scale and the rapid growth of the urban population, in order to achieve a variety of travel purposes, residents need to use a variety of means of transportation to meet the travel needs of different places at different times; urban public transportation has become the daily life of residents. As an important part of travel activities, taxis, as a supplement to public transportation, are characterized by convenience, speed, and flexibility, and are an important part of modern cities to ensure the travel and quality of life of urban residents.

[0003] However, the travel needs of passengers are volatile and random, and the behavior of online car-hailing drivers to search for passengers on the road...

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Embodiment Construction

[0061] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0062] The invention aims to solve the problem of urban taxi demand forecasting. Taxi travel demand is highly dependent on time and space. Accurately predicting travel demand can not only trigger the forward-looking dispatching behavior of taxis, but also accurately provide passengers with boarding points; Accurate demand forecasting can not only improve the travel experience of passengers, but also reduce the invalid cruising of taxis, and has a positive effect on alleviating urban traffic congestion, air pollution, energy consumption and other problems.

[0063] Based on...