Road obstacle risk assessment method and device based on city big data, and readable storage medium
A risk assessment and big data technology, applied in data processing applications, road vehicle traffic control systems, traffic control systems, etc., can solve the camera's limited weather and light, time cost, human and financial cost consumption, and the risk of not being able to do it Forecasting and other issues to achieve high accuracy
Active Publication Date: 2021-05-28
XIAMEN UNIV
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Problems solved by technology
The disadvantage of this traditional method is that the investigation process consumes a lot of time and human and financial resources, and the camera is limited by weather and light. More importantly, this method cannot predict risks
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[0046] like figure 1It is a frame diagram of the overall process of the technical solution of the embodiment of the present invention, which is divided into three stages, one is to extract the urban road network, the other is to extract the characteristics of the road network, and the third is to predict the risk of road obstacles. Urban road network extraction is based on urban grids, divided by grid time, extracting floating car data, clustering, and obtaining urban roads; road network feature extraction involves heterogeneous data fusion technology, from remote sensing satellite images, DEM data, urban weather, Extract characteristic information related to road obstacles from heterogeneous data such as urban POI; road obstacle risk assessment extracts relevant events based on crowd sensing technology, and uses Self-training self-training neural network model to realize road obstacle risk prediction;
[0047] The detailed implementation steps are as follows:
[0048] S1: Ur...
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The invention provides a road obstacle risk assessment method and device based on urban big data and a storable medium. The method comprises the steps of: urban road network extraction: counting the number of floating cars in each grid in each time period; clustering the number of floating cars corresponding to each hour of each grid, extracting a road network, and dividing the road network into corresponding road sections; road network feature extraction: extracting space-time and situation features corresponding to each road network grid; road obstacle risk prediction: crawling a specific field through social network data to obtain final road obstacle occurrence data; counting the number of the floating cars in each time period, obtaining data of no road obstacle event of each road network grid in each time period; carrying out model prediction on the basis of a Self-training self-training model; the method provided by the invention is based on multi-source data fusion, has the advantages of high efficiency and low consumption, and also achieves relatively high accuracy.
Description
technical field [0001] The present invention relates to the fields of artificial intelligence, big data, and urban computing, in particular to a road obstacle risk assessment method, device and readable storage medium based on urban big data. Background technique [0002] Road obstacles mainly refer to abnormal phenomena that hinder the normal passage of roads. Whether it is for urban management or urban emergency response, road obstacles have always plagued cities. Road obstacles bring many unnecessary troubles to the normal operation of the city, such as traffic problems and safety problems. At the same time, road obstacles also cause troubles to the masses and affect their travel. [0003] Traditional road obstacle detection mainly relies on the field survey of urban surveyors and the way of camera viewing. The disadvantage of this traditional method is that the investigation process consumes a lot of time and human and financial resources, and the camera is limited by...
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Login to View More IPC IPC(8): G06Q10/06G06Q10/04G06K9/62G06Q50/26G08G1/01G08G1/065
CPCG06Q10/0635G06Q10/04G06Q10/06313G06Q50/26G08G1/0112G08G1/0133G08G1/065G06F18/23G06F18/241G06F18/214Y02A30/60
Inventor 陈龙彪游建议王程范晓亮谢天琦黄靖淳
Owner XIAMEN UNIV