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Related Experiment Video

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

Yicao Ma1, Shifeng Liu1, Gang Xue1

  • 1School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.

Sensors (Basel, Switzerland)
|June 18, 2020
PubMed
Summary
This summary is machine-generated.

This study integrates smart card data (SCD), point of interest (POI), and Online to Offline (OTO) data to classify urban subway station functions. The novel two-stage framework accurately identifies urban functional regions, aiding smart city planning.

Keywords:
POI (point of interest)deep learningfunctional regionsmart cardsoft sensors

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Area of Science:

  • Urban planning and smart city development
  • Geographic Information Systems (GIS) and data analytics
  • Transportation and urban studies

Background:

  • Urbanization increases traffic, necessitating effective identification of urban functional regions.
  • Previous studies using point of interest (POI) and smart card data (SCD) for subway station classification faced limitations due to model and dataset constraints.
  • Integrating diverse data sources is crucial for a comprehensive understanding of urban dynamics.

Purpose of the Study:

  • To propose a novel two-stage framework for identifying urban functional regions based on subway station data.
  • To leverage a combination of smart card data (SCD), point of interest (POI), and Online to Offline (OTO) e-commerce platform data for enhanced analysis.
  • To improve the accuracy and scope of subway station classification for urban planning.

Main Methods:

  • A two-stage framework was developed, combining passenger flow analysis (SCD) with built environment analysis (POI and OTO data).
  • Stage 1 utilized a ResNet model on SCD feature maps to analyze passenger flow.
  • Stage 2 employed a stacked autoencoder deep neural network (SAE-DNN) model on POI and OTO features.
  • A SoftMax function integrated the outputs of both stages for final functional region identification.

Main Results:

  • The integrated framework successfully analyzed diverse datasets, generating passenger flow maps, POI counts, and OTO store information for subway stations.
  • Experimental results demonstrated the framework's good performance in identifying urban functional regions.
  • The approach provides valuable insights for subway station and surrounding area planning.

Conclusions:

  • The proposed two-stage framework effectively identifies urban functional regions by integrating SCD, POI, and OTO data.
  • This method offers a robust approach for analyzing subway station characteristics and their surrounding environments.
  • The findings contribute to the development of smart cities and informed urban planning decisions.