Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting

Rose Yu · Yaguang Li · Cyrus Shahabi · Ugur Demiryurek · Yan Liu

427 citations0/0 files storedSociety for Industrial and Applied Mathematics eBooks

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Traffic Prediction and Management Techniques Traffic control and management Hydrological Forecasting Using AI

Abstract

Traffic forecasting is a vital part of intelligent transportation systems. It becomes particularly challenging due to short-term (e.g., accidents, constructions) and long-term (e.g., peak-hour, seasonal, weather) traffic patterns. While most of the previously proposed techniques focus on normal condition forecasting, a single framework for extreme condition traffic forecasting does not exist. To address this need, we propose to take a deep learning approach. We build a deep neural network based on long short term memory (LSTM) units. We apply Deep LSTM to forecast peak-hour traffic and manage to identify unique characteristics of the traffic data. We further improve the model for post-accident forecasting with Mixture Deep LSTM model. It jointly models the normal condition traffic and the pattern of accidents. We evaluate our model on a real-world large-scale traffic dataset in Los Angeles. When trained end-to-end with suitable regularization, our approach achieves 30%–50% improvement over baselines. We also demonstrate a novel technique to interpret the model with signal stimulation. We note interesting observations from the trained neural network.

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References

2015Long short-term memory neural network for traffic speed prediction using remote microwave sensor data 2014Short-term traffic forecasting: Where we are and where we’re going 2007The Economics of Urban Transportation 2011The Fundamental Law of Road Congestion: Evidence from US Cities 1997Traffic Flow Forecasting: Comparison of Modeling Approaches 2004Short‐term traffic forecasting: Overview of objectives and methods 2004Still Stuck in Traffic: Coping with Peak-Hour Traffic Congestion

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2020A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges 2019Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends 2019Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management 2018Survey on traffic prediction in smart cities 2019Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges 2020Spatio-Temporal Graph Structure Learning for Traffic Forecasting

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