Work

Are Neural Networks Better than Machine Learning? A Comparative Study for Travel Mode Predictions

Year: 2025

Type: article

Abstract: Predicting how people choose their travel modes accurately is important in the transportation field. Machine learning (ML) and neural networks (NNs) have gradually become popular in recent years. However, which is better is seldom discussed in previous studies. Therefore, we collect several real-world travel datasets from different countries, and pick five typical ML models, six classic NN models, and ten new NN models for comparison. Some methods for improvement are also considered, including SMOTE, Near-Miss, and using focal loss. The results show that, when looking at the F1-score, the NN models do not perform as well as ML models. While the performances of different classic NN models are similar, making the neural network more complex does not improve the prediction results. Some new NN models can reach the level of ML models on small datasets, but they still perform poorly on large datasets. Due to such a result, we further discuss two important topics: why NN models are not as good as compared to the ones in some other fields, and why this phenomenon is not revealed in many previous papers. In summary, we think this study gives a good reference for future research on predicting travel modes and choosing the right models.

Source: Systems

DOI: 10.3390/systems13121099

Authors: Tongkai Zhang, Cheng-Jie Jin, Yuchen Song, Dawei Li

Institutions: Southeast University, National University of Singapore

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China