Travel-Time Prediction for Freeway Corridors

Abstract

The application of a nonlinear time series model to the prediction of traffic parameters on a freeway network is investigated. The nonlinear time series approach is a statistical technique that has strong potential for on-line implementation. A new approach for predicting corridor travel times is developed and tested with travel-time data. The travel-time data are derived from observed speed data, which are collected from an 18-km (11.2-mi) freeway section in Orlando, Florida. The westbound Interstate-4 morning peak period (6:00 to 10:00 a.m.) for 20 incident-free days is tested with the goal of predicting recurrent congestion. The problem is addressed from the perspectives of single-variable and multiple-variable prediction of corridor travel times. In single-variable prediction, speed time-series data are used to forecast travel times along the freeway corridor. A calibrated single-variable prediction model is developed through the application of decay factors to smooth out the input data and the establishment of a threshold on the minimum speed prediction permitted. Multivariable prediction schemes are developed using speed, occupancy, and volume data provided by inductive loop detectors on the study section. The prediction performance of the calibrated single-variable model is shown to be superior to the multivariable prediction schemes. This new approach produces reasonable errors for short-term (5-min) travel-time predictions. The developed model can be implemented on-line with minimal effort.

Short-Term Prediction of Travel Time using Neural Networks on an Interurban Highway

“…Automatic travel time monitoring systems are not common, and although the whole road network cannot be covered completely with loop detectors, the traffic information collected by inductive loops is used as input for many models that predict travel time (Saito & Watanabe 1995;Lee & Choi 1998;Al-Deek 2003;D'Angelo et al 1999; van Grol et al 1999a, b;Kwon et al 2000;Lindveld et al 2000;van Lint 2003;Figure 1. The vehicle whose driver sees the travel time information on the variable message sign, and the vehicles on whose travel time the information is based.…”

Effect of Monitoring System Structure on Short-term Prediction of Highway Travel Time

“…In addition, the input information is usually either loop detector-based (Matsui and Fujita, 1998;D'Angelo et al, 1999;McFadden et al, 2001;van Lint, 2003;Zhang & Rice, 2003) or travel time measurement-based (Rilett & Park, 2001;Chien & Kuchipudi, 2002), according to the system available at the study site.…”

GPS Data-based Non-parametric Regression for Predicting Travel Times in Urban Traffic Networks

“…In addition, there have been many attempts to estimate future conditions using data mining. Some are parametric linear and non-linear regression models [7][8][9][10], nonparametric regression models [11], ARIMA models [12], space-time ARIMA models [13][14][15], ATHENA models [16], Kalman filters [17], artificial neural networks [18][19][20][21][22], and support vector machines [23]. Emerging traffic data collection techniques make these extrapolation-based models easier to use.…”