A bus transit time prediction method based on big data and artificial intelligence
A technology of passing time and artificial intelligence, applied in traffic flow detection, road vehicle traffic control system, forecasting, etc., can solve the problem of poor accuracy of bus travel time, achieve the effect of eliminating cumulative errors and improving accuracy
Active Publication Date: 2021-11-16
南通安广美术图案设计有限公司
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This method does not predict the transit time based on the factors that affect the transit time of the bus, but directly obtains the average historical transit time, which will lead to poor accuracy of the predicted transit time
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[0032] In order to further explain the technical means and effects that the present invention takes to achieve the intended invention purpose, below in conjunction with the accompanying drawings and preferred embodiments, a method for predicting bus transit time based on big data and artificial intelligence proposed in the present invention , its specific implementation, structure, characteristics and effects thereof are described in detail as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of the invention.
[0034] The embodiment of the present invention provides a specific imple...
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The invention relates to the technical field of artificial intelligence, in particular to a bus transit time prediction method based on big data and artificial intelligence. In this method, the bus section is first divided into multiple subsections, and the driving data of the bus and the optical flow information of the vehicles in the subsections are obtained. According to the road condition characteristics, road section feature vector, complexity index and driving habit index of each sub-section according to the driving data and vehicle optical flow information; input the road condition feature, road section feature vector and driving habit index into the transit time prediction network and output the predicted passage of the corresponding sub-section Time; the loss function of the transit time prediction network is constructed by the complexity index, the predicted transit time and the real transit time; the traffic of the bus section is obtained from the sum of the predicted transit time of multiple sub-sections, the waiting time of traffic lights and the stop time of the bus station time. The present invention reduces the cumulative error and improves the accuracy of the passing time by predicting the passing time of each subroad section.
Description
technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a bus transit time prediction method based on big data and artificial intelligence. Background technique [0002] With the continuous development of our country's social economy, problems such as urban traffic congestion and travel inconvenience have become increasingly prominent, seriously affecting people's normal life and urban development. The development of urban public transport is not only an effective measure to alleviate urban traffic congestion, but also an inevitable requirement for improving urban living environment and promoting sustainable urban development. Bus is the most common mode of transportation in urban public transportation. The judgment of bus transit time directly affects the results of bus dispatching, and the prediction of bus transit time has a positive effect on people's travel. [0003] At present, bus transit time prediction is usu...
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IPC IPC(8): G08G1/01G06Q10/04G06Q50/26
CPCG08G1/0125G08G1/0137G06Q10/04G06Q50/26
Inventor 谢宗艺唐银丹
Owner 南通安广美术图案设计有限公司