Title: Deep learning-based hybrid traffic flow prediction model for freight transport

Authors: Anıl Utku; Sema Kayapinar Kaya; Umit Can

Addresses: Computer Engineering Department, Munzur University, 62000 Tunceli, Turkey ' Industry Engineering Department, İzmir Demokrasi University, İzmir, Turkey ' Computer Engineering Department, Munzur University, 62000 Tunceli, Turkey

Abstract: Urban freight transportation is crucial to sustaining domestic and international trade and fulfilling the regular requirements of community companies and customers. Therefore, this study developed an efficient convolutional neural network (CNN) and long short-term memory (LSTM) based hybrid deep learning model for predicting traffic flow in freight transportation. The number of freight trucks that would travel along 663 Street in Anderlecht, Belgium, and their average speed was predicted. The created model was compared with support vector machine (SVM), linear regression (LR), multilayer perceptron (MLP), random forest (RF), LSTM, and CNN using the success measures. The critical finding of this study is that combining the feature extraction ability of the 1D CNN model with the learning ability of the LSTM model from multivariate traffic data increases the traffic density prediction success compared to other traditional methods. Based on the results of the experiments, the CNN-LSTM model outperformed all the other models employed in this study. With this model, freight density can be successfully predicted, assisting in planning freight transport activities.

Keywords: deep learning; freight transportation; machine learning; traffic flow; traffic prediction.

DOI: 10.1504/IJMDM.2025.147193

International Journal of Management and Decision Making, 2025 Vol.24 No.4, pp.371 - 393

Received: 23 Mar 2024
Accepted: 19 May 2024
Published online: 11 Jul 2025 *

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