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Hourly Origin-Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning.

Shahriar Afandizadeh Zargari1, Amirmasoud Memarnejad1, Hamid Mirzahossein2

  • 1School of Civil Engineering, Iran University of Science and Technology (IUST), Tehran 16846-13114, Iran.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary

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Predicting urban travel demand using intelligent transportation systems (ITS) data is crucial. A convolutional neural network (CNN) model accurately predicted the origin-destination (OD) matrix for Tehran, outperforming other machine learning methods.

Area of Science:

  • Transportation Science
  • Urban Planning
  • Data Science

Background:

  • Traditional origin-destination (OD) demand surveys are costly and time-consuming.
  • Intelligent Transportation Systems (ITS) generate diverse data suitable for demand prediction.
  • Leveraging big data from ITS offers significant benefits for transportation planning.

Purpose of the Study:

  • To predict the OD matrix for Tehran metropolis using various ITS data sources.
  • To evaluate the performance of different machine learning (ML) models for OD matrix prediction.
  • To identify the most accurate ML model for OD matrix estimation.

Main Methods:

  • Utilized data from automatic number plate recognition (ANPR) cameras, smart fare cards, loop detectors, and GPS navigation software.
Keywords:
big datahourly OD demand matrixmachine learningneural network

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  • Incorporated socio-economic, demographic, and land-use characteristics of zones.
  • Developed and compared five ML models, including convolutional neural networks (CNN).

    • Main Results:

    • The CNN model demonstrated the highest accuracy in predicting the OD matrix.
    • CNN achieved the lowest Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).
    • The CNN-predicted OD matrix exhibited the highest structural similarity to the ground truth matrix.

    Conclusions:

    • Machine learning, particularly CNN, is effective for predicting urban OD matrices using ITS data.
    • CNN offers a superior approach for accurate and reliable travel demand forecasting.
    • The findings support the integration of diverse ITS data for enhanced transportation planning.