Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey

Abstract

Detecting, predicting, and alleviating traffic congestion are targeted at improving the level of service of the transportation network. With increasing access to larger datasets of higher resolution, the relevance of deep learning for such tasks is increasing. Several comprehensive survey papers in recent years have summarised the deep learning applications in the transportation domain. However, the system dynamics of the transportation network vary greatly between the non-congested state and the congested state -- thereby necessitating the need for a clear understanding of the challenges specific to congestion prediction. In this survey, we present the current state of deep learning applications in the tasks related to detection, prediction, and alleviation of congestion. Recurring and non-recurring congestion are discussed separately. Our survey leads us to uncover inherent challenges and gaps in the current state of research. Finally, we present some suggestions for future research directions as answers to the identified challenges.

20 pages, 7 figures

References in corpus (8)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Learning in Neural Networks: An Overview
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• A Physics-Informed Deep Learning Paradigm for Car-Following Models
• A survey of advances in vision-based vehicle re-identification
• A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation
• Physics-Informed Deep Learning for Traffic State Estimation
• Learning-based Traffic State Reconstruction using Probe Vehicles

Cited by in corpus (3)

• Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap
• Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis
• Bidirectional yet asymmetric causality between urban systems and traffic dynamics in 30 cities worldwide