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

A travel demand and prediction method technology, applied in the field of transportation, can solve problems such as inaccurate prediction and difficult to predict the dynamic change of taxi demand, and achieve the effect of improving prediction accuracy, alleviating urban traffic pressure, and improving operational efficiency.

Inactive Publication Date: 2019-12-20
GUANGDONG UNIV OF TECH
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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 ac

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

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Embodiment Construction

[0024] The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0025] The main task of the present invention is to predict the length and time of passengers' taxi-hailing demand through historical online car-hailing track data. The present invention hopes to capture complex nonlinear spatio-temporal dependencies by using spatial information (such as the similarity of getting on and off the car, the characteristics of site functions, etc.) and external background information (weather conditions, local events, etc.) Ultimately, it can accurately predict the long-term and short-term demand for online taxis, and provide technical support for optimizing vehicle scheduling, improving user travel efficiency, and alleviating urban road congestion. Example of online car-hailing data figure 2 As shown, it contains driver ID, order ID, times...

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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...

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Application Information

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IPC IPC(8): G08G1/01G06Q10/04G06Q50/30G06N3/04G06N3/08
CPCG08G1/0125G08G1/0137G06Q10/04G06Q50/30G06N3/08G06N3/044G06N3/045
Inventor 曾伟良吴淼森
Owner GUANGDONG UNIV OF TECH
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