TY - JOUR
T1 - Predicting freeway work zone delays and costs with a hybrid machine-learning model
AU - Du, Bo
AU - Chien, Steven
AU - Lee, Joyoung
AU - Spasovic, Lazar
N1 - Funding Information:
T his paper is based on a research project sponsored by the New Jersey Department of Transportation (NJDOT). This support is gratefully acknowledged but implies no endorsement of the conclusions by NJDOT.
Publisher Copyright:
© 2017 Bo Du et al.
PY - 2017/8/7
Y1 - 2017/8/7
N2 - 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.
AB - 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.
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U2 - 10.1155/2017/6937385
DO - 10.1155/2017/6937385
M3 - Article
AN - SCOPUS:85028985932
SN - 0197-6729
VL - 2017
JO - Journal of Advanced Transportation
JF - Journal of Advanced Transportation
M1 - 6937385
ER -