Predicting freeway work zone delays and costs with a hybrid machine-learning model

Document Type

Article

Publication Date

8-7-2017

Abstract

A hybrid machine-learning model, integrating an artificial neural network (ANN) and a support vector machine (SVM) model, is developed to predict spatiotemporal delays, subject to road geometry, number of lane closures, and work zone duration in different periods of a day and in the days of a week. The model is very user friendly, allowing the least inputs from the users. With that the delays caused by a work zone on any location of a New Jersey freeway can be predicted. To this end, tremendous amounts of data from different sources were collected to establish the relationship between the model inputs and outputs. A comparative analysis was conducted, and results indicate that the proposed model outperforms others in terms of the least root mean square error (RMSE).The proposed hybridmodel can be used to calculate contractor penalty in terms of cost overruns as well as incentive reward schedule in case of early work competition. Additionally, it can assist work zone planners in determining the best start and end times of a work zone for developing and evaluating traffic mitigation and management plans.

Identifier

85028985932 (Scopus)

Publication Title

Journal of Advanced Transportation

External Full Text Location

https://doi.org/10.1155/2017/6937385

e-ISSN

20423195

ISSN

01976729

Volume

2017

Fund Ref

New Jersey Department of Transportation

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