Online Learning Solutions for Freeway Travel Time Prediction

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199 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

Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time savings and-if nothing else-on the reliability of travel times. As such, there is an increasing need for fast and reliable online travel time prediction models. Previous research showed that data-driven approaches such as the state-space neural network (SSNN) are reliable and accurate travel time predictors for freeway routes, which can be used to provide predictive travel time information on, for example, variable message sign panels. In an operational context, the adaptivity of such models is a crucial property. Since travel times are available (and, hence, can be measured) forrealizedtrips only, adapting the parameters (weights) of a data-driven travel time prediction model such as the SSNN is particularly challenging. This paper proposes a new extended Kalman filter (EKF) based online-learning approach, i.e., the online-censored EKF method, whichcanbe applied online and offers improvements over a delayed approach in which learning takes place only as realized travel times are available.

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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 2017Deep learning for short-term traffic flow prediction 2017Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks 2020Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values 2017An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic 2011Prediction Intervals to Account for Uncertainties in Travel Time Prediction 2013Dynamic data-driven local traffic state estimation and prediction

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