In recent years, the prohibition of trucks which could cause environmental pollution on urban roads has become widespread in China. However, some truck restriction policies might lead to a reduction in logistics transportation efficiency. With the help of big data, technology companies have developed many truck applications, such as HCB, truck home, and truck help, to provide the drivers available traffic information. In this context, this paper put forward a truck path optimization model considering environmental impact (TPOM-EI), which is solved by a heuristic algorithm—ant colony optimization (ACO) algorithm. Most previous studies focused on unilateral benefits rather than overall benefits; this paper aims to propose a path optimization model based on real-time minimization of social and transportation costs. Finally, data of Xiqing Economic and Technological Development Zone in Tianjin city (XQ-EDZ) have been used to demonstrate the applicability of the proposed algorithm. The results show that logistics truck path has a huge impact on social costs, and real-time activities in various areas will also change the path of a truck. This research will also help logistics truck drivers to choose the best route in real time.
This paper describes a study that built a neural network prediction model based on feature extraction, focusing on text analysis and image analysis of WeChat official accounts reading quantity. Based on the embedding method of the deep learning model, we extracted the text features in the title and the image features in the cover picture, explored the relationship between these features and the reading quantity, and built a neural network model based on these features to predict the reading quantity. The results show that there is a phenomenon of sentiment fusion in the text, and a sentence vector model based on Doc2Vec and a neural network model both had a good performance. This paper proposes a tool that can predict the reading quantity in advance and help administrators adjust the titles and images according to the predicted results.
INDEX TERMSFeature extraction; neural network; WeChat official accounts; Doc2Vec; user engagement
I. INTRODUCTIONSocial media platforms, such as Twitter and Facebook, provide opportunities for people to create, communicate, and share ideas. In China, WeChat is a social media platform with strong communication and influencing characteristics. Administrators apply for WeChat official accounts to publish different kinds of articles or news on the platform, and readers can obtain and share information. By November 2017, WeChat had gathered more than 10 million official accounts, including 3.5 million monthly active official accounts and 797 million monthly active users [1]. Many authors have formed their own brands through original articles and become entrepreneurs on WeChat.Previous research on WeChat has focused on user behaviors and attitudes as well as the influence mechanism and communication power of WeChat as a social media platform. Specifically, it involves user satisfaction, user attitude, user intention [2-5], user engagement behavior [6][7], and the influence mechanism and effect of WeChat as an information communication platform on service provided by users [8][9][10][11]. However, there has been little research on deeper mining and exploration of the text through natural language processing (NLP). Moreover, as far as we know, there has been little research on the analysis of the cover image of WeChat official accounts. Our study focuses on text analysis and image analysis of WeChat official accounts reading quantity, which contributes to the research in this specific field and addresses
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