Citation

Wu Z, Rilett LR, Ren W. Int. J. Urban Sci. 2022; 26(3): 517-540.

Copyright

(Copyright © 2022, University of Seoul, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/12265934.2021.1899844

PMID

unavailable

Abstract

Accurate travel time prediction is very important for real-time traveller information systems. Many existing traveller information systems provide point estimates of forecast travel times. Often the forecast corridor travel time is estimated as a direct summation of the forecast link travel times on the route. This approach neglects the correlation between link travel times and may lead to inaccurate route travel time forecasts. This paper improves upon the simple addition method by accounting for the dependency of link travel times on the arrival time at that specific link which further correlates to its preceding links. In addition, this paper also explores the potential of using the nonlinear autoregressive with exogenous inputs (NARX) model and feedforward neural network model to forecast the corridor travel time mean and reliability metrics. To the authors knowledge this is the first time, short-term travel time reliability is measured by a reliability interval which is based on the forecasts of corridor travel time mean and standard deviation. The prediction methodologies developed in this paper are tested on an urban arterial that has been instrumented with Bluetooth readers so empirical travel times are available. It was found that the proposed NARX model outperforms the other models that were studied with respect to mean corridor travel time prediction. In terms of the reliability interval prediction, the performance of various models is presented as a Pareto Optimal Frontier trading off accuracy and usability. The proposed NARX model and three other tested models are all on the Pareto Optimal Frontier.HighlightsExplores the nonlinear autoregressive with exogenous inputs (NARX) model for travel time predictions;Develops models to forecast urban arterial corridor travel time means and reliability metrics;Develops forecasting models with link-based or corridor-based travel time inputs, respectively;Evaluates the performance of various models using a Pareto Optimal Frontier;Compared travel time reliability metrics: reliability interval, 95th percentile, and buffer index.

Language: en

Keywords

mean corridor travel time; nonlinear autoregressive with exogenous inputs neural network model; short-term arrival time reliability; Travel time prediction