The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Federated Learning-based Route Choice Modeling for Preserving Driver's Privacy in Transportation Big Data Application

교통 빅데이터 활용 시 개인 정보 보호를 위한 연합학습 기반의 경로 선택 모델링

  • Jisup Shim (National Infrastructure & Geospatial Information Research Division, KRIHS)
  • 심지섭 (국토연구원 국토인프라.공간정보연구본부)
  • Received : 2023.10.31
  • Accepted : 2023.12.13
  • Published : 2023.12.31
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Abstract

The use of big data for transportation often involves using data that includes personal information, such as the driver's driving routes and coordinates. This study explores the creation of a route choice prediction model using a large dataset from mobile navigation apps using federated learning. This privacy-focused method used distributed computing and individual device usage. This study established preprocessing and analysis methods for driver data that can be used in route choice modeling and compared the performance and characteristics of widely used learning methods with federated learning methods. The performance of the model through federated learning did not show significantly superior results compared to previous models, but there was no substantial difference in the prediction accuracy. In conclusion, federated learning-based prediction models can be utilized appropriately in areas sensitive to privacy without requiring relatively high predictive accuracy, such as a driver's preferred route choice.

본 연구에서는 분산 컴퓨팅 및 개별 디바이스 활용을 통해 개인 정보 보호에 특화된 학습방법인 연합학습 방법론을 기반으로, 모바일 내비게이션 애플리케이션에서 수집된 대규모의 운전자 데이터를 이용하여 경로 선택 예측 모델을 수립하는 방법에 대해 고찰한다. 경로 선택 모델링에서 활용될 수 있는 운전자 데이터의 전처리 및 분석 방법을 수립하고, 서포트벡터머신(SVM) 및 다층 퍼셉트론(MLP)과 같이 기존에 널리 활용되는 학습 방법과 연합학습 방법의 성능과 특성을 비교한다. 분석 결과 연합학습을 통한 모델 성능은 중앙 서버 기반의 모델과의 비교에서 예측 정확도 측면의 차이가 거의 없는 것으로 나타났으나, 개별 데이터가 충분히 확보되는 경우 연합학습 모델과 같은 개인화 모델의 성능이 개선될 수 있다는 점을 확인하였다. 연합학습 모델은 본 연구의 경로 선택 모델링 사례와 같이 모빌리티 부문의 데이터 프라이버시 문제가 중요한 분야에서 대규모 데이터 처리를 필요로 하는 경우에 그 활용 가치가 매우 높을 것으로 기대된다.

Keywords

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