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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
25.04.2023
DOI
https://doi.org/10.7307/ptt.v35i2.40
ISSUE
2023 (Vol 35), Issue 2
LICENSE
Copyright (c) 2024 Ning Gao, Yuanbo Hong, Junfei Chen, Chonghao Pang
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Regional Expressway Freight Volume Prediction Algorithm Based on Meteorological Information

Authors:Ning Gao, Yuanbo Hong, Junfei Chen, Chonghao Pang

Abstract

In the post-epidemic era, dynamic monitoring of expressway road freight volume is an important task. To accurately predict the daily freight volume of urban expressway, meteorological and other information are considered. Four commonly used algorithms, a random forest (RF), extreme gradient boosting (XGBoost), long short-term memory (LSTM) and K-nearest neighbour (KNN), are employed to predict freight volume based on expressway toll data sets, and a ridge regression method is used to fuse each algorithm. Nanjing and Suzhou in China are taken as a case study, using the meteorological data and freight volume data of the past week to predict the freight volume of the next day, next two days and three days. The performance of each algorithm is compared in terms of prediction accuracy and training time. The results show that in the forecast of freight volume in Nanjing, the overall prediction accuracies of the RF and XGBoost models are better; in the forecast of freight volume in Suzhou, the LSTM model has higher accuracy. The fusion forecasting method combines the advantages of each forecasting algorithm and presents the best results of forecasting the freight volumes in two cities.

Keywords:road transportation, forecast of freight volume, machine learning, expressway, meteorological information

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