Deep learning-based vehicle path optimization method and system

A vehicle routing and deep learning technology, applied in the direction of road vehicle traffic control systems, traffic control systems, instruments, etc., can solve problems such as reliability analysis of travel routes, and achieve the effect of reducing human errors, saving time, and reducing interference

Active Publication Date: 2017-03-29
JINAN BOTU INFORMATION TECH CO LTD
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AI Technical Summary

Problems solved by technology

However, these methods do not conduct an in-depth analysis of the reliability of travel routes, and the est

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  • Deep learning-based vehicle path optimization method and system
  • Deep learning-based vehicle path optimization method and system
  • Deep learning-based vehicle path optimization method and system

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Embodiment Construction

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. figure 1 It is a schematic flow chart of a vehicle route optimization method based on deep learning in the present invention. As shown in the figure, the vehicle route optimization method based on deep learning in this embodiment may include:

[0050] S101. Acquire real-time road data and historical road data, and preprocess the acquired data to form a labeled data set.

[0051] In the specific implementation, a large amount of real-time and historical road data can provide guarantee for the accuracy of the subsequent model prediction.

[0052] Real-time road data includes:

[0053] (1) Obtain the average vehicle speed and traffic flow data of the corresponding road section in real time by setting up sensing devices on each road section.

[0054] ⑵The video data of the lane is...

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Abstract

The invention discloses a deep learning-based vehicle path optimization method and system. The method includes the following steps that: real-time road data and historical road data are acquired, the acquired data are preprocessed, so that a tagged data set can be formed; a deep belief network model is constructed, and the deep belief network model is trained; the trained deep belief network model is utilized to predict all the paths from a vehicle to a destination, and the congestion coefficients of each path are output; and the paths are evaluated comprehensively based on two indexes, namely the congestion coefficients and distances, and an optimal path is outputted, wherein the optimal path is a path corresponding to the minimum linear accumulation result of the two indexes, namely a corresponding congestion coefficient and a distance. According to the method and system, through the powerful feature extraction function of the deep belief network model, required information can be obtained from multi-dimensional road traffic data, interference can be reduced, a congestion situation is predicted accurately and reasonably, path search efficiency can be improved, human-made mistakes can be reduced, and valuable time can be saved for disaster relief work.

Description

technical field [0001] The present invention relates to a vehicle route optimization method and system based on deep learning, especially for special vehicles weighing the balance between road distance and travel time in a complex urban traffic environment. Background technique [0002] In the past half century, the vehicle routing problem has been one of the research hotspots in the field of transportation. With the substantial increase of traffic flow, distance is no longer the focus of attention. Need to think more about the problem from the perspective of time. In real traffic conditions, due to the uncertainty of demand (different travel volumes of people at different time points) and uncertainty of supply (such as traffic accidents, weather reasons, road maintenance, etc.) decline), the travel time of the road section is changing within a certain range. The uncertainty of travel time greatly affects the reliability of the transportation system and causes most of the ...

Claims

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Application Information

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IPC IPC(8): G08G1/0968
CPCG08G1/096816
Inventor 刘治孔令爽
Owner JINAN BOTU INFORMATION TECH CO LTD
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