Deep learning methods in transportation domain: a review

Hoàng Nguyên · Minh Kieu · Tao Wen · Chen Cai

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Traffic Prediction and Management Techniques Traffic control and management Transportation Systems and Logistics

Abstract

Recent years have seen a significant amount of transportation data collected from multiple sources including road sensors, probe, GPS, CCTV and incident reports. Similar to many other industries, transportation has entered the generation of big data. With a rich volume of traffic data, it is challenging to build reliable prediction models based on traditional shallow machine learning methods. Deep learning is a new state‐of‐the‐art machine learning approach which has been of great interest in both academic research and industrial applications. This study reviews recent studies of deep learning for popular topics in processing traffic data including transportation network representation, traffic flow forecasting, traffic signal control, automatic vehicle detection, traffic incident processing, travel demand prediction, autonomous driving and driver behaviours. In general, the use of deep learning systems in transportation is still limited and there are potential limitations for utilising this advanced approach to improve prediction models.

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References

2014Traffic Flow Prediction With Big Data: A Deep Learning Approach 2017LSTM network: a deep learning approach for short‐term traffic forecast 2014Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning 2017Deep learning for short-term traffic flow prediction 2002Comparison of parametric and nonparametric models for traffic flow forecasting 2013Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning 2017Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach 2015Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

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2019Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges 2022Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities 2020How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey

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