Application of big data optimized clustering algorithm in cloud computing environment in traffic accident forecast
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Application of big data optimized clustering algorithm in cloud computing environment in traffic accident forecast Zhun Tian 1,2 & Shengrui Zhang 1,3 Received: 3 July 2020 / Accepted: 26 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract As the usage rate of cars is getting higher and higher, the injuries and losses caused by traffic accidents are also getting bigger and bigger. If some traffic accidents can be predicted, then such losses can be greatly solved. Although there are abundant research results on intelligent transportation, there are not many research results on how to predict traffic accidents. For this issue, the main aim of this paper is to propose a continuous non-convex optimization of the K-means algorithm in order to solve the model problem in the traffic prediction process. First, this paper uses clustering algorithm for feature analysis and big data for the establishment of simulation model in cloud environment. Through this paper an equivalent model, using matrix optimization theory to analyze and process K-means problem, and design efficient and theoretically guaranteed algorithms for big data. By simulating the traffic situation in Shanghai city within three years, the outcomes display that the model endorsed in the given paper can predict traffic accidents at a rate of 93.88% and the accuracy rate of traffic accident processing time is 78%, which fully illustrates the effectiveness of the model established in this paper. Keywords Cloud computing . Big data . Clustering algorithm . Traffic accident prediction . Algorithm optimization
Abbreviations NAP EEG R-CNN ANN MEC MLP SVM
Normalized Traffic Accident Propensity Electroencephalogram Region Based Convolutional Neural Network Artificial Neural Network Mobile Edge Computing Multiple layer Perceptron Neural Networks Support Vector Machine
This article is part of the Topical Collection: Special Issue on Network In Box, Architecture, Networking and Applications Guest Editor: Ching-Hsien Hsu * Zhun Tian [email protected] 1
College of Transportation Engineering, Chang’an University, Xi’an 710064, Shaanxi, China
2
School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, Shaanxi, China
3
Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area (Chang’an University), Xi’an 710064, Shaanxi, China
GDP HDFS EGS YOLO Fuzzy ARTMAP RF KM-MBFO
Gross Domestic Product Hadoop Distributed File System Expanded Graphites You only look once Fuzzy Adaptive Resonance Theory Random Forest K-means Modified Bacterial Foraging
1 Introduction The rate and severity of traffic accidents in China have remained high for a long time.By analyzing the recorded causes of traffic accidents, it can be found that traffic accidents in China are mainly caused by two reasons: people (93%) and cars (4%). The reasons for road, traffic, weather, and management account for only 3%, which is far from the
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