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

TL;DR

This survey reviews how deep learning techniques are applied to detect, predict, and alleviate traffic congestion, emphasizing the unique challenges of congestion dynamics and highlighting future research directions.

Contribution

It provides a comprehensive overview of deep learning applications in traffic congestion management, focusing on the distinct challenges of congestion prediction and identifying research gaps.

Findings

Deep learning effectively detects and predicts traffic congestion.

Challenges include system dynamics variability between congestion states.

Future research should address data quality and model robustness.

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.