Meta-learning-based vehicle trajectory clustering method and system

A technology of vehicle trajectory and clustering method, which is applied in the field of vehicle trajectory clustering method and system based on meta-learning, and can solve problems such as inability to obtain clustering results

Active Publication Date: 2020-08-14
WUHAN UNIV OF TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Due to the characteristics of the trajectory, the trajectory is composed of several trajectory sequences. If the clustering is based on the trajectory points or the entire trajectory segment, the historical driving route and comprehensive information cannot be observed from the clustering results. Therefore, we need to cluster GPS trajectories in the form of trajectory segments, and the existi

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  • Meta-learning-based vehicle trajectory clustering method and system
  • Meta-learning-based vehicle trajectory clustering method and system

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Embodiment 1

[0033] The embodiment of the present invention provides a vehicle track clustering method based on meta-learning, comprising the following steps:

[0034]Collect different types of GPS vehicle trajectory data, use different DBSCAN clustering algorithms to cluster different types of GPS vehicle trajectory data, and obtain clustering evaluation indicators corresponding to different types of GPS vehicle trajectory data, according to different types of GPS vehicle trajectory Data clustering evaluation index to obtain the best DBSCAN clustering algorithm corresponding to different types of GPS vehicle trajectory data;

[0035] Gathering GPS vehicle track data, dividing the GPS vehicle track data collected into a training data set and a test data set, utilizing the training data set and the test data set to train a meta-learner for vehicle track type division;

[0036] Gathering the GPS vehicle trajectory data again, utilizing the meta-learner to obtain the corresponding vehicle tra...

Embodiment 2

[0045] The embodiment of the present invention provides a vehicle trajectory clustering method based on meta-learning, which collects different types of GPS vehicle trajectory data, and the collection of GPS trajectory data comes from 726 vehicles; the daily vehicle trajectory data volume includes more than 60,000 trajectory data , after preprocessing, the average amount of effective track data per vehicle per day is 231, and each vehicle contains at least 1617 track numbers in a week. The GPS track data of the vehicle is shown in Table 1, including the longitude of positioning , latitude, speed and positioning time and other information;

[0046] Table 1

[0047]

[0048] For vehicle trajectory data, its types are diverse, such as different types of vehicles, the data of taxis or trucks will be very different; or the GPS trajectory data of long-distance high-speed sections and short-distance transportation in the city The data is also very different; the types of GPS traj...

Embodiment 3

[0097] The present invention also provides a vehicle trajectory clustering system based on meta-learning, including a data acquisition module, a clustering algorithm matching module, a meta-learner construction module, and a trajectory data clustering result acquisition module;

[0098] The data collection module is used to collect different types of GPS vehicle trajectory data;

[0099] The clustering algorithm matching module is used to use different DBSCAN clustering algorithms to cluster different types of GPS vehicle track data respectively, to obtain clustering evaluation indicators corresponding to different types of GPS vehicle track data, according to different types of GPS vehicles Clustering evaluation index of trajectory data, to obtain the best DBSCAN clustering algorithm corresponding to different types of GPS vehicle trajectory data;

[0100] The meta-learner construction module is used to divide the GPS vehicle track data collected by the data acquisition module ...

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Abstract

The invention discloses a meta-learning-based vehicle trajectory clustering method and system, belongs to the technical field of vehicle trajectory clustering, and solves the problem that an optimal clustering result cannot be obtained due to the fact that multiple different types of trajectory data adopt a single trajectory clustering algorithm in the prior art. A meta-learning-based vehicle trajectory clustering method comprises the following steps: collecting different types of GPS vehicle trajectory data, and obtaining an optimal DBSCAN clustering algorithm corresponding to the different types of GPS vehicle trajectory data; collecting GPS vehicle trajectory data, and obtaining a meta-learning device used for vehicle trajectory type division through training; and acquiring a vehicle trajectory type corresponding to the GPS vehicle trajectory data by using the meta-learning device, and clustering the GPS vehicle trajectory data by using an optimal DBSCAN clustering algorithm corresponding to the vehicle trajectory type to obtain a clustering result of the GPS vehicle trajectory data. The optimal clustering result can be obtained for various different types of trajectory data.

Description

technical field [0001] The invention relates to the technical field of vehicle trajectory clustering, in particular to a meta-learning-based vehicle trajectory clustering method and system. Background technique [0002] In recent years, with people's travel needs and extensive transportation of goods, more and more vehicles including cars and different types of trucks have appeared in people's lives, and a large amount of GPS trajectory data is generated every day during the driving of vehicles. Trajectory data is the spatiotemporal data sequence left by moving objects in space over time. It contains a large amount of information, which enables us to understand the behavior of moving objects more intuitively. Based on the GPS trajectory data of massive vehicles, trajectory clustering can be performed. Carry out data mining to discover potential utilization value. [0003] Due to the characteristics of the trajectory, the trajectory is composed of several trajectory sequence...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N20/00G01S19/42
CPCG06N20/00G01S19/42G06F18/23G06F18/241G06F18/214Y02T10/40
Inventor 曹菁菁赖馨夏飞余达旭
Owner WUHAN UNIV OF TECH
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