Traffic volume estimation based on weather and foot traffic using LSTM for efficient asphalt pavement management: A case study of Jung-gu,  Seoul

Byeong-Hun Woo; Dong-Ho Yoo; Jae-Soon Choi

doi:10.22702/jkai.2024.14.2.17

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2024 Vol.14, Issue 2 Preview Page Next Page

Scientific Paper

Traffic volume estimation based on weather and foot traffic using LSTM for efficient asphalt pavement management: A case study of Jung-gu, Seoul 아스팔트포장관리 효율화를 위한 LSTM 활용 날씨 및 유동인구에 따른 교통량 추정: 서울시 중구를 기준으로

31 December 2024. pp. 183-191

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Abstract

This study utilized the Long Short-Term Memory (LSTM) model to estimate traffic volume in Jung-gu, Seoul, using weather and foot traffic data. The input variables included average temperature (°C), average relative humidity (%), sunlight (MJ/m²), maximum foot traffic, and minimum foot traffic, while the target variable was set as daily traffic volume. This study utilized a total of 1,400 days of time-series data collected from January 1, 2020, to October 31, 2024. During the training process, 500 epochs were optimized using the gradient descent method, with the initial loss value of 0.49 decreasing sharply to 0.15. The test results demonstrated that the model successfully captured the trends in traffic volume, producing predictions within a reasonable range. For example, on a specific day when the actual traffic volume was 640,000, the LSTM model predicted values ranging from 360,000 to 1,000,000, validating its reliability in range-based estimation. This study confirmed that traffic volume estimation is feasible even when using indirect variables, such as weather and foot traffic data.

Keywords

LSTM

Foot traffic

Machine learning

Time-series

Estimation

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Information

Publisher :KOREAN ASPHALT INSTITUTE
Publisher(Ko) :한국아스팔트학회
Journal Title :Journal of the Korean Asphalt Institute
Journal Title(Ko) :한국아스팔트학회지
Volume : 14
No :2
Pages :183-191
Received Date : 2024-12-11
Revised Date : 2024-12-15
Accepted Date : 2024-12-31
DOI :https://doi.org/10.22702/jkai.2024.14.2.17

Journal of the Korean Asphalt Institute ISSN:2234-0785(Print) 2635-9553(Online) 한국아스팔트학회지

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