Abnormal resident travel mode mining method based on taxi OD data

A travel mode and abnormal technology, applied in structured data retrieval, electronic digital data processing, geographic information database, etc.

Pending Publication Date: 2021-05-25
BEIJING UNIV OF TECH
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  • Description
  • Claims
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Problems solved by technology

However, this analysis method only considers the travel rules in space, and

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  • Abnormal resident travel mode mining method based on taxi OD data
  • Abnormal resident travel mode mining method based on taxi OD data
  • Abnormal resident travel mode mining method based on taxi OD data

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Embodiment Construction

[0093]Data pretreatment and functional zone division: the original data is the use of the taxi data, the original data record passengers start from the bus to the bus trajectory, and extract the start and end points (OD data) of each data at this time and latitude. information. The study area has a total of 8km * 8km square area, where each of this area is used to divide the functional zone attribute, and it is covered with different colors in different colors. The total classification is 11 functional area categories include: residential area, primary and secondary schools, factories, commercial areas, scenic spots, office, hospital, hotel, gymnasium, station, university.

[0094]After the ribbon division is completed, you need to add each O-D data to the area attribute information, ie what is the area where the data is last arrived. At this time, each data is mapped to the divided ribbon image, and the image pixel coordinates indicate latitude and longitude coordinates. There are two...

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Abstract

The invention discloses an abnormal resident travel mode mining method based on taxi OD data, and belongs to the field of intelligent traffic and data mining. In order to better mine taxi passenger travel rules and deeply mine abnormal modes existing in resident travel, the invention provides a method based on high-dimensional sparse tensor decomposition, namely, multi-dimensional information including time, longitude and latitude, functional area attributes and the like is organized as a tensor model, and low-rank sparse decomposition is performed on the tensor model. Therefore, the key technical problem to be solved comprises the following steps: dividing functional areas for a research area and classifying corresponding data into the corresponding functional areas; organizing corresponding data such as time, longitude and latitude, functional area attributes and the like to form a tensor model; performing low-rank sparse decomposition on the tensor model, respectively extracting a low-rank model and a sparse model, and performing Tucker decomposition; and visualizing a basis matrix obtained through decomposition, and displaying a passenger travel mode visually.

Description

Technical field[0001]The present invention belongs to the field of intelligent transportation and data mining, which involves a very stateful travel law mining method of urban residents.Background technique[0002]With the rapid development of information technology and ubiquitous data, the location and trajectory data of human individual activity in space and time scale have become possible. Under the driving of big data, these location information not only helps planners and researchers understand the city as complex systems, but also allow researchers to understand the law of human activities by data-centric technologies. The emergence of this mobile data does have the opportunity to integrate more information into decision. However, the complexity of the data also increases with its contents, which means that there is a complex dependency between space, time and social attributes, and high-order interactions. Taking into account the taxi as one of the important means of transporta...

Claims

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Application Information

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IPC IPC(8): G06F16/29G06F16/9537G06F16/909G06F17/16
CPCG06F17/16G06F16/29G06F16/909G06F16/9537
Inventor 王立春张彬王少帆孔德慧尹宝才
Owner BEIJING UNIV OF TECH
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