Please use this identifier to cite or link to this item:
Title: Ensemble tree approach to estimating work zone capacity
Authors: Weng, J.
Meng, Q. 
Issue Date: 12-Jan-2012
Citation: Weng, J., Meng, Q. (2012-01-12). Ensemble tree approach to estimating work zone capacity. Transportation Research Record (2286) : 56-67. ScholarBank@NUS Repository.
Abstract: Accurate estimation of work zone capacity is necessary for successful traffic control management at work zones. This study uses an ensemble tree approach to estimate work zone capacity more accurately than would a model that was based on a single decision tree. A bootstrap aggregation method is used to build an ensemble tree comprising a set of individual decision trees. In this method, a set of bootstrap samples is generated by sampling with replacement from a training sample. A set of individual trees is then constructed with a tree learning algorithm and combined through averaging of the output. Data on work zone capacity from 14 states and cities are used in a case study to build and evaluate the ensemble tree. The results of the statistical comparison demonstrate that the ensemble tree outperforms the existing models of work zone capacity in estimation accuracy. The ensemble tree also performs better than any single decision tree for stability. A comparison with the 2010 Highway Capacity Manual indicates that the ensemble tree can provide a more accurate estimate of work zone capacity. Unlike the manual's model, the ensemble tree avoids estimated errors caused by subjective judgments of users because it does not require manual setting of various adjustment factors. Because of its high estimation accuracy and stability, the ensemble tree is a good alternative for estimating work zone capacity, especially for inexperienced users.
Source Title: Transportation Research Record
ISSN: 03611981
DOI: 10.3141/2286-07
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Mar 23, 2019


checked on Mar 13, 2019

Page view(s)

checked on Mar 24, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.