Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments
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 t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/12/3348 |
| _version_ | 1797565464017108992 |
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| author | Yicao Ma Shifeng Liu Gang Xue Daqing Gong |
| author_facet | Yicao Ma Shifeng Liu Gang Xue Daqing Gong |
| author_sort | Yicao Ma |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:13:32Z |
| format | Article |
| id | doaj.art-e535c6972e4044a795e8828d8c015e69 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:13:32Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e535c6972e4044a795e8828d8c015e692023-11-20T03:40:19ZengMDPI AGSensors1424-82202020-06-012012334810.3390/s20123348Soft Sensor with Deep Learning for Functional Region Detection in Urban EnvironmentsYicao Ma0Shifeng Liu1Gang Xue2Daqing Gong3School of Economics and Management, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing 100044, ChinaThe 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.https://www.mdpi.com/1424-8220/20/12/3348functional regionPOI (point of interest)smart carddeep learningsoft sensors |
| spellingShingle | Yicao Ma Shifeng Liu Gang Xue Daqing Gong Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments Sensors functional region POI (point of interest) smart card deep learning soft sensors |
| title | Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments |
| title_full | Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments |
| title_fullStr | Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments |
| title_full_unstemmed | Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments |
| title_short | Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments |
| title_sort | soft sensor with deep learning for functional region detection in urban environments |
| topic | functional region POI (point of interest) smart card deep learning soft sensors |
| url | https://www.mdpi.com/1424-8220/20/12/3348 |
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