Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city

Abstract

With the rapid development of the information age, smart city has gradually become the mainstream of urban construction. Dynamic transportation assignment has attracted more interest in the smart city construction under the new era of the Internet of things (IoT) because the urban road traffic is the heart of many problems in many fields, such as in the case of city congestion and processing center planning system. In this paper, we analyzed the processing center’s economic indexes and optimized the dynamic transportation network assignment based on continuous big IoT input database, and a high performance computing model is proposed for the dynamic traffic planning. Specifically, while the previous methods exploited the geographical information system (GIS) or K-means separately, the proposed transportation planning is based on the real-time IoT and GIS data, which is processed by DBN and K-means to make the final solution close to the practice and meet the requirements of high performance computing and economic cost. which is regarded as the key target index. Moreover, considering the large data characteristic of real-time online stream, the deep belief network (DBN) model is built to preprocess the data to improve the clustering effect of the K-means. This study works on the example case of hotel service centers problem in Tianjin to evaluate the optimal dynamic traffic network planning result. The experiment test has proved that based on the performance of super high computing, the model is precisely helpful for the optimal planning of traffic network under real time mass data situation and low cost, and promoting the construction and development of the smart city.