Article2019

Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends

416 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Traffic Prediction and Management Techniques Advanced Neural Network Applications Autonomous Vehicle Technology and Safety

Abstract

Transportation systems operate in a domain that is anything but simple. Many exhibit both spatial and temporal characteristics, at varying scales, under varying conditions brought on by external sources such as social events, holidays, and the weather. Yet, modeling the interplay of factors, devising generalized representations, and subsequently using them to solve a particular problem can be a challenging task. These situations represent only a fraction of the difficulties faced by modern intelligent transportation systems (ITS). In this paper, we present a survey that highlights the role modeling techniques within the realm of deep learning have played within ITS. We focus on how practitioners have formulated problems to address these various challenges, and outline both architectural and problem-specific considerations used to develop solutions. We hope this survey can help to serve as a bridge between the machine learning and transportation communities, shedding light on new domains and considerations in the future.

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References

2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2014Traffic Flow Prediction With Big Data: A Deep Learning Approach 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction 2014Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning 2018Big Data Analytics in Intelligent Transportation Systems: A Survey 2019Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

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2020Applications of Artificial Intelligence and Machine learning in smart cities 2023Graph Neural Networks for Intelligent Transportation Systems: A Survey 2021Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges 2020How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey 2021

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