A Simple and Effective Method for Predicting Travel Times on Freeways

John R. Rice · E. vanZwet

331 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

We present a method to predict the time that will be needed to traverse a given section of a freeway when the departure is at a given time in the future. The prediction is done on the basis of the current traffic situation in combination with historical data. We argue that, for our purposes, the current traffic situation of a section of a freeway is well summarized by the current status travel time. This is the travel time that would result if one were to depart immediately and no significant changes in traffic would occur. This current status travel time can be estimated from single- or double-loop detectors, video data, probe vehicles, or any other means. Our prediction method arises from the empirical observation that there exists a linear relationship between any future travel time and the current status travel time. The slope and intercept of this relationship may change subject to the time of day and the time until departure, but linearity persists. This observation leads to a prediction scheme by means of linear regression with time-varying coefficients.

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References

1984Dynamic prediction of traffic volume through Kalman filtering theory 1996Combining kohonen maps with arima time series models to forecast traffic flow 2003Short-term travel time prediction 1995System Optimal Dynamic Assignment for Electronic Route Guidance in a Congested Traffic Network

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2014A review of travel time estimation and forecasting for Advanced Traveller Information Systems 2017Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps 2008Online Learning Solutions for Freeway Travel Time Prediction 2003Travel-Time Reliability as a Measure of Service 2015On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models 2018DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision

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