Urban taxi demand prediction method and device and computer equipment

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.

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...

AI Technical Summary

Problems solved by technology

[0005] On the one hand, the existing influencing factors for the forecast of taxi travel demand are not considered comprehensively. The traffic demand is a complex nonlinear system with human participation, changes, and these influencing factors are highly uncertain, not only affected by natural factors (such as seasons, , climate factors, etc.), as well as human factors (such as large-scale activities and emergencies, road conditions, population distribution, psychological state of passengers / drivers, etc.); existing research mainly mines time and space from historical data trajectory data characteristics, did not fully consider the impact of static and dynamic natural and human factors on taxi passenger demand

[0006] On the other hand, existing taxi demand forecasts have relatively simple divisions of forecasting units, most of which use regular grid divisions. In actual demand forecasts, there are spatial cold and hot spots, and the demand for online taxis is not distributed in various regions. Uniform, the traditional grid division method is likely to lead to large differences in demand in various regions and cause problems such as decreased prediction accuracy

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