국토계획 (Journal of Korea Planning Association)

  • 제54권3호
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  • Pages.106-118
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  • 2019
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  • 1226-7147(pISSN)
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  • 2383-9171(eISSN)
대한국토도시계획학회 (Korea Planning Association)
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기계학습 알고리즘을 이용한 보행만족도 예측모형 개발

Developing a Pedestrian Satisfaction Prediction Model Based on Machine Learning Algorithms

  • 투고 : 2019.01.28
  • 심사 : 2019.06.04
  • 발행 : 2019.06.30
⟨ 이전 논문 다음 논문 ⟩

초록

In order to develop pedestrian navigation service that provides optimal pedestrian routes based on pedestrian satisfaction levels, it is required to develop a prediction model that can estimate a pedestrian's satisfaction level given a certain condition. Thus, the aim of the present study is to develop a pedestrian satisfaction prediction model based on three machine learning algorithms: Logistic Regression, Random Forest, and Artificial Neural Network models. The 2009, 2012, 2013, 2014, and 2015 Pedestrian Satisfaction Survey Data in Seoul, Korea are used to train and test the machine learning models. As a result, the Random Forest model shows the best prediction performance among the three (Accuracy: 0.798, Recall: 0.906, Precision: 0.842, F1 Score: 0.873, AUC: 0.795). The performance of Artificial Neural Network is the second (Accuracy: 0.773, Recall: 0.917, Precision: 0.811, F1 Score: 0.868, AUC: 0.738) and Logistic Regression model's performance follows the second (Accuracy: 0.764, Recall: 1.000, Precision: 0.764, F1 Score: 0.868, AUC: 0.575). The precision score of the Random Forest model implies that approximately 84.2% of pedestrians may be satisfied if they walk the areas, suggested by the Random Forest model.

키워드

참고문헌

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피인용 문헌

  1. Determining the Tiers of a Supply Chain Using Machine Learning Algorithms vol.13, pp.10, 2021, https://doi.org/10.3390/sym13101934