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A method for recommending passenger-seeking areas based on driver experience

A recommendation method and technology for finding customers. It is applied in the fields of instruments, network data indexing, and computing. It can solve problems such as insufficient data volume, inability to obtain effective information, and inability to solve the problem of cold start.

Active Publication Date: 2021-12-24
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

However, if we simply use statistical techniques to analyze the original data, on the one hand, we cannot solve the cold start problem (the amount of data in the initial state is too small to obtain effective information), and on the other hand, it is difficult to use the underlying data such as driver experience and other influencing factors

Method used

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  • A method for recommending passenger-seeking areas based on driver experience
  • A method for recommending passenger-seeking areas based on driver experience
  • A method for recommending passenger-seeking areas based on driver experience

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Embodiment Construction

[0056] The present invention will be further described below in conjunction with accompanying drawing.

[0057] Such as figure 1 Shown is a flow chart of a driver-seeking area recommendation method based on driver experience in an embodiment of the present invention. The flow chart shows the four steps involved in a driver-seeking area recommendation method based on driver experience: trajectory data preprocessing, passenger-loading point clustering, driver-seeking area visit frequency statistics, and customer-seeking value of the customer-seeking area Calculate the Top-k search area recommendations for a certain driver.

[0058] figure 2 It is a schematic diagram of the PR-tree index structure.

[0059] figure 2 The 10 passenger areas in (c1,c2,...c10) are recursively divided into four groups according to the similarity of spatial positions, N3, N4, N5, N6, N3 and N4 are further reduced to N1, and N5 and N6 are further reduced to N2, N1 and N2 form the root node. The ...

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Abstract

The invention discloses a driver-seeking area recommendation method based on driver experience. The specific implementation steps of the present invention are as follows: step 1, preprocessing of vehicle trajectory data; step 2, clustering the data of the passenger location points to obtain the distribution map of the passenger-seeking area, and establishing a regional network for the passenger-seeking areas in different locations Hierarchical index structure. Step 3. Statistics of the driver's visit frequency in the seeker area: use the driver's personal historical seeker trajectory data set and the seeker area distribution map to calculate the driver's seeker frequency matrix M. Step 4. Calculate the customer-seeking value of the customer-seeking area. Step 5. Recommend customer-seeking areas: Recommend the location information of the Top-k most valuable customer-seeking areas within the current location for a driver. The present invention makes full use of the correlation between the value of the customer-seeking area and the driver's experience, and excavates the customer-seeking value score of the customer-seeking area.

Description

technical field [0001] The invention belongs to the field of intelligent passenger-seeking by taxis, and in particular relates to a driver-experience-based method for recommending a passenger-seeking area. Background technique [0002] In recent years, with the rapid development of location positioning technology, GPS devices have been widely used by taxis, resulting in a large amount of taxi trajectory data information. This information has mature applications in many fields, such as urban computing and route planning. [0003] In the large-scale history of taxis, there is a large amount of search strategy information of taxi passengers hidden, and the collective wisdom of these taxi drivers needs to be discovered and utilized urgently. How to improve drivers' revenue by mining efficient passenger search strategies is a very meaningful problem. However, if we simply use data statistics technology to analyze the original data, on the one hand, it cannot solve the cold star...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9537G06F16/9535G06F16/951
CPCG06F16/9537G06F16/9535G06F16/951
Inventor 徐建
Owner HANGZHOU DIANZI UNIV
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