Real-Time Prediction of Freeway Occupancy for Congestion Control

Loading...

View/Download File

CTS97-12.pdf (5.31 MB)

Persistent link to this item

https://hdl.handle.net/11299/155110

Statistics

View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Real-Time Prediction of Freeway Occupancy for Congestion Control

Authors

Cherkassky, Vladimir

Yi, Sangkug

Published Date

1997-09

Publisher

Center for Transportation Studies, University of Minnesota

Type

Report

Abstract

Accurate traffic prediction is critical for effective control of on-ramp traffic (ramp metering). During congestion, traffic shock waves propagate back and forth between the detectors, and traffic becomes inherently non-stationary and difficult to predict. Recently, several adaptive non-linear time series prediction methods have been developed in statistics and in artificial neural networks. We applied these methods to develop real-time prediction of freeway occupancy during congestion periods, from current and time-lagged observations of occupancy at several (neighboring) detector stations. This study used the following function estimation methodologies for real-time occupancy prediction: two statistical techniques, multivariate adaptive regression splines (MARS) and projection pursuit regression; two neural network methods, multi-layer perceptrons (MLP) and constrained topological mapping (CTM). All these methods were applied to freeway occupancy data collected on I-35W during morning rush hours. Data collected on one day was used for training (model estimation), whereas the data collected on a different day was used for testing, i.e., estimating the quality of prediction (generalization). Results for this study indicate that the proposed methodology provides 10-15% more accurate prediction of traffic during congestion periods than the approach currently used by Minnesota DOT.

Keywords

Neural networks

Ramp metering

Function estimation

Occupancy prediction

Traffic congestion

Traffic forecasting

Description

Related to

Replaces

License

Collections

Research Reports

Series/Report Number

CTS
97-12

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Cherkassky, Vladimir; Yi, Sangkug. (1997). Real-Time Prediction of Freeway Occupancy for Congestion Control. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/155110.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.