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City region traffic accident risk prediction method

A traffic accident and risk prediction technology, applied in traffic flow detection, traffic control system, road vehicle traffic control system, etc., to achieve the effect of improving interpretability and accuracy

Active Publication Date: 2019-07-09
SOUTHWEST JIAOTONG UNIV
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AI Technical Summary

Problems solved by technology

This method can solve the actual traffic accident risk prediction problem and can be extended to other spatiotemporal related fields

Method used

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  • City region traffic accident risk prediction method
  • City region traffic accident risk prediction method
  • City region traffic accident risk prediction method

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Embodiment Construction

[0039] The present invention will be described in further detail below in conjunction with real traffic data in the New York area.

[0040] 1. Data source

[0041] (1) Vehicle collision data. Vehicle crash data we obtained from the NYPD. This data contains every recorded traffic record from 2012 to 2018. In addition to basic information such as time, location, and street, this dataset also includes the type of vehicle involved and the main cause of the collision.

[0042] (2) Motor vehicle travel data. We obtained car trip data from the New York Taxi and Limousine Commission. These data contain motor vehicle trip records from 2009 to the present. The specific motor vehicles include three types of vehicles, yellow taxis, green taxis and online car-hailing vehicles. The main service scope of yellow taxis and green taxis is different. Yellow taxis can pick up passengers anywhere in the five major districts of New York. Green taxis are only allowed to pick up passengers in U...

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Abstract

The invention discloses a city region traffic accident risk prediction method. A future traffic accident risk is predicted through adoption of a coder-decoder deep learning frame with a space-time attention mechanism. On the basis of a great deal of multi-source heterogeneous data related to traffic accidents, the future traffic accident risk is predicted relatively effectively and accurately through fusion of the multi-source heterogeneous data. A main data set comprises traffic accident quantity and traffic flows of different vehicles. External data is external environment data such as weather and street design. According to the space-time attention mechanism provided by the scheme of the invention, space-time characteristics of local and global regions can be grasped at the same time, precision of models is improved, explainability of the models is also improved, and important degree of different influence factors on the predicted value is explained according to the attention value.The frame provided by the invention can be expanded to the other similar fields of space-time data, and the frame has universality value.

Description

technical field [0001] The invention belongs to the technical field of data mining. Background technique [0002] With the rapid development of urbanization and the realization of road motorization process, people's life is more convenient. At the same time, the explosive growth of motor vehicles has brought enormous pressure to the government's traffic control, causing a series of social security problems such as traffic congestion, air pollution and traffic accidents. According to the "Global Road Safety Status Report" published by the World Health Organization in 2015, road traffic accidents cause approximately 1.3 million deaths and 20 to 50 million non-fatal injuries worldwide each year. Road traffic accidents are the leading cause of death for all age groups. Therefore, it is very important to accurately and effectively predict the number of traffic accidents in various regions of the city in the future to reduce the number of traffic accidents. The government and c...

Claims

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Application Information

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IPC IPC(8): G08G1/01G06N3/04
CPCG08G1/0129G08G1/0137G06N3/045
Inventor 李天瑞朱磊杜圣东
Owner SOUTHWEST JIAOTONG UNIV
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