Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction

Muhammad Tayyab Asif · Justin Dauwels · Chong Yang Goh · Ali Oran · Esmail Fathi · Muye Xu +2 more

223 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Traffic Prediction and Management Techniques Transportation Planning and Optimization Traffic control and management

Abstract

The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, data-driven methods, such as support vector regression (SVR), can predict traffic with high accuracy because traffic tends to exhibit regular patterns over time. However, in practice, the prediction performance can significantly vary across the network and during different time periods. Insight into those spatiotemporal trends can improve the performance of intelligent transportation systems. Traditional prediction error measures, such as the mean absolute percentage error, provide information about the individual links in the network but do not capture global trends. We propose unsupervised learning methods, such as k-means clustering, principal component analysis, and self-organizing maps, to mine spatiotemporal performance trends at the network level and for individual links. We perform prediction for a large interconnected road network and for multiple prediction horizons with an SVR-based algorithm. We show the effectiveness of the proposed performance analysis methods by applying them to the prediction data of the SVR.

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References

2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 2004Travel-Time Prediction With Support Vector Regression 2002Comparison of parametric and nonparametric models for traffic flow forecasting 1996Combining kohonen maps with arima time series models to forecast traffic flow 2013Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning 2010Real-time road traffic prediction with spatio-temporal correlations 2005Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach 1998Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models

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2018Deep Sequence Learning with Auxiliary Information for Traffic Prediction 2018A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction 2019Traffic flow prediction based on combination of support vector machine and data denoising schemes 2019Traffic speed prediction for urban transportation network: A path based deep learning approach

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