City aggregation event prediction and positioning method and device
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A positioning method and urban technology, applied in forecasting, neural learning methods, computer components, etc., can solve problems such as long-term prediction and positioning of key areas
Active Publication Date: 2019-08-20
SHENZHEN INST OF ADVANCED TECH
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[0005] Embodiments of the present invention provide a method and device for predicting and locating urban agglomeration event...
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Embodiment 1
[0056] The invention utilizes a new deep neural network structure to predict and locate city aggregation time in advance, and converts aggregation event prediction into a classification and regression problem. Specifically, given a series of observation time series Xt, output the prediction sequence Yt indicating whether the aggregation event occurs, the probability of the immediate occurrence of the aggregation event Pt, and the possible location Lt of the aggregation event.
[0057] In order to achieve this goal, the present invention is mainly divided into three parts: feature extraction, probability prediction, and class activation mapping. The relationship between the three is: first, feature extraction is performed on the input data set, and the extracted features are simultaneously input to the probability prediction and class activity mapping parts.
[0058] According to an embodiment of the present invention, see figure 1 , providing a method for predicting and locat...
Embodiment 2
[0099] According to another embodiment of the present invention, see Figure 4 , providing an urban aggregation event prediction and positioning device, including:
[0100] The picture generating unit 200 is used to convert the actual trajectory of the city vehicle into a multi-frame picture at preset time intervals for a period of time;
[0101] The feature extraction unit 202 is used to perform feature extraction using a deep neural network on multi-frame pictures, and extract the timing features and spatial features of multi-frame pictures;
[0102] The probability prediction unit 204 is used to perform probability prediction by using the temporal features and spatial features of the multi-frame pictures, and calculate the probability of urban aggregation events;
[0103] The class activation mapping unit 206 is configured to perform class activation mapping on the temporal features and spatial features of the multi-frame pictures, and determine the location where the urba...
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Abstract
The invention relates to the field of traffic monitoring, in particular to an urban aggregation event prediction and positioning method and device. According to the method and the device, feature extraction is carried out on multiple frames of pictures by utilizing a deep neural network; the time sequence features and the spatial features of multiple frames of pictures are extracted, probability prediction and class activation mapping are carried out by utilizing the time sequence features and the spatial features of the multiple frames of pictures, and the probability and the position of an urban aggregation event are judged, so that the aggregation event can be detected, the urban aggregation event can be predicted in advance, and an aggregation site is positioned. In the process of feature extraction and classification judgment, an ingenious network structure is selected according to actual conditions. According to the method, the urban aggregation event is predicted and positionedin advance based on the deep neural network under the complex Internet of Things mobile data, and long-time-span prediction and positioning can be carried out on a key area where the aggregation eventoccurs or is simulated.
Description
technical field [0001] The invention relates to the field of traffic monitoring, in particular to a method and device for predicting and locating urban aggregation events. Background technique [0002] The urban aggregation event is a phenomenon that a large number of moving objects (taxis, pedestrians, etc.) converge in a small area within a period of time. Typical urban gathering events include traffic jams and concert crowd gatherings. Large-scale urban agglomeration events have an important impact on urban traffic and urban security. Predicting the occurrence and location of gathering events in advance can help relevant departments plan and adjust resources such as police force, ensure the healthy operation of the city, and improve people's life satisfaction. With the rapid development of IoT sensor technology, we have been able to collect a large amount of mobile data such as traffic and mobile phones. However, human activities are very complex. At present, many stud...
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