Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments.

Yicao Ma,Gang Xue,Daqing Gong,Shifeng Liu

doi:10.3390/s20123348

Abstract

The rapid development of urbanization has increased traffic pressure and made the identification of urban functional regions a popular research topic. Some studies have used point of interest (POI) data and smart card data (SCD) to conduct subway station classifications; however, the unity of both the model and the dataset limits the prediction results. This paper not only uses SCD and POI data, but also adds Online to Offline (OTO) e-commerce platform data, an application that provides customers with information about different businesses, like the location, the score, the comments, and so on. In this paper, these data are combined to and used to analyze each subway station, considering the diversity of data, and obtain a passenger flow feature map of different stations, the number of different types of POIs within 800 m, and the situation of surrounding OTO stores. This paper proposes a two-stage framework, to identify the functional region of subway stations. In the passenger flow stage, the SCD feature is extracted and converted to a feature map, and a ResNet model is used to get the output of stage 1. In the built environment stage, the POI and OTO features are extracted, and a deep neural network with stacked autoencoders (SAE–DNN) model is used to get the output of stage 2. Finally, the outputs of the two stages are connected and a SoftMax function is used to make the final identification of functional region. We performed experimental testing, and our experimental results show that the framework exhibits good performance and has a certain reference value in the planning of subway stations and their surroundings, contributing to the construction of smart cities.

Highlights

The increasing population in urban areas has led residents to demand more for daily life and travel.To meet the needs of residents and build better smart cities, governments need to use the Internet of Things and communication technologies to obtain real-time data for further decision-making and planning [1,2]
Urban functional regions were first proposed in the Athens Charter, which claims that planners should address four types of city areas: the residential region, the work region, the recreation region, and the transportation region
The framework we proposed is compared with a variety of competing methods grouped into the following categories: The classical methods, liner regression (LR), random forest regression (RFR), and support vector regression (SVR)

Summary

Introduction

The increasing population in urban areas has led residents to demand more for daily life and travel.To meet the needs of residents and build better smart cities, governments need to use the Internet of Things and communication technologies to obtain real-time data for further decision-making and planning [1,2]. The increasing population in urban areas has led residents to demand more for daily life and travel. Urban functional regions were first proposed in the Athens Charter, which claims that planners should address four types of city areas: the residential region, the work region, the recreation region, and the transportation region. With the development of cities around the world, other functional regions have emerged that make the urban spatial structure more complicated, and these new functional regions vary with the specific features of each city [3,4]. Urban functional regions can be defined by some types of activities or spatial interactions that may occur in a region [3]. One of the fastest growing means of transportation are subways. The accuracy of the classification results for subway stations is closely

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Conclusion