Short-long-term prediction method based on online taxi-hailing travel requirements

Abstract

The invention discloses a short-long-term prediction method based on online taxi-hailing travel requirements. The method comprises the following steps of firstly, preprocessing data and segmenting thedata into a training set and a test set; then, dividing an urban road network into grids according to longitude and latitude and finding space-time correlation between areas; next, establishing a mixed model based on CNN+LSTM+XGBoost; and finally, predicting a taxi demand quantity within a short time period (such as 10 minutes) and a taxi demand quantity in a long time period (such as 1 hour, festivals and holidays and rush hours). The method can be used for short-term prediction such as prediction of traffic flow trends with intervals of 10 minutes, and can also be used for long-term prediction; and different periodic changes caused by weekends and festivals and holidays can be considered and different passenger flow rules in the rush hours every day are found out, so that the predictionaccuracy is improved.

Description

technical field [0001] The invention relates to the technical field of transportation, in particular to a long-term and short-term prediction method based on online car-hailing travel demand. Background technique [0002] In the era of advocating energy conservation and emission reduction, taxis, especially online car-hailing vehicles, have gradually become the preferred means of transportation for people to travel. Accurate demand forecasting is an important means to improve the efficiency of taxi operations. It is of great help in allocating unloaded taxis, resisting black car scams, and improving passenger travel efficiency. Demand forecasting can also be used to infer road section speed and flow, guiding vehicles to travel safely and efficiently in complex urban traffic environments. With the mature development of online taxi-hailing, it is more convenient for citizens to take a taxi. However, due to the uneven distribution of taxis between regions, the imbalance betwee...

AI Technical Summary

Problems solved by technology

Most of the above methods are used to predict short-term traffic demand (the next 10 minutes), and cannot accurately predict the evolution of long-term traffic demand (the next 30 minutes, future peak periods, holidays, and severe weather periods).

Although the above methods have achieved satisfactory results in short-term forecasting, they seldom excavate the evolution law of taxi demand in different spatial regions and different time periods, and it is difficult to predict the dynamic changes of taxi demand in complex nonlinear spatio-temporal relationships

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