Document Type
Dissertation
Degree
Doctor of Philosophy (PhD)
Major/Program
Civil Engineering
First Advisor's Name
Mohammed Hadi
First Advisor's Committee Title
Co-Committee Chair
Second Advisor's Name
Albert Gan
Second Advisor's Committee Title
Co-Committee Chair
Third Advisor's Name
Zhenmin Chen
Fourth Advisor's Name
Fang Zhao
Fifth Advisor's Name
David L. Shen
Date of Defense
4-3-2008
Abstract
Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit.
This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables.
The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.
Identifier
FI14051149
Recommended Citation
Bian, Jie, "Predicting delay reductions from freeway traffic diversion for incident management" (2008). FIU Electronic Theses and Dissertations. 1675.
https://digitalcommons.fiu.edu/etd/1675
Rights Statement
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
Comments
If you are the rightful copyright holder of this dissertation or thesis and wish to have it removed from the Open Access Collection, please submit a request to dcc@fiu.edu and include clear identification of the work, preferably with URL.