Workforce forecasting for state transportation agencies: A machine learning approach
A decline in the number of construction engineers and inspectors at state transportation agencies (STAs) to manage the ever-increasing lane miles has emphasized the importance of workforce planning in these agencies. Forecasting workforce requirements is crucial for effective planning in any industr...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-03-01
|
| Series: | International Journal of Transportation Science and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2046043024000595 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849762203479048192 |
|---|---|
| author | Adedolapo Ogungbire Suman Kumar Mitra |
| author_facet | Adedolapo Ogungbire Suman Kumar Mitra |
| author_sort | Adedolapo Ogungbire |
| collection | DOAJ |
| description | A decline in the number of construction engineers and inspectors at state transportation agencies (STAs) to manage the ever-increasing lane miles has emphasized the importance of workforce planning in these agencies. Forecasting workforce requirements is crucial for effective planning in any industry or agency. This study developed machine learning (ML) models to estimate the person-hour requirements of STAs at the project level. The Arkansas Department of Transportation (ARDOT) was used as a case study, using its employee and project details data between 2012 and 2021. ML regression models ranging from linear, tree ensembles, kernel-based, and neural network-based models were developed. These models were compared based on the accuracy of their predictions, the time taken for training the models and their prediction time. Predictions were tested based on the K-fold cross validation technique. The results indicated a high performance from the random forest regression model, a tree ensemble with bagging, which recorded a mean R-squared value of 0.91. Other ML models such as an ensemble neural network model and the linear models also proved to be fit for the problem, attaining R squared value as high as 0.80 and 0.78, respectively. These findings underscore the capability of ML models to provide more accurate workforce demand forecasts for STAs and the construction industry. This enhanced accuracy in workforce planning will contribute to improved resource allocation and management. |
| format | Article |
| id | doaj-art-76fff8297afc4202bf2aafa0b86372d5 |
| institution | DOAJ |
| issn | 2046-0430 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Transportation Science and Technology |
| spelling | doaj-art-76fff8297afc4202bf2aafa0b86372d52025-08-20T03:05:49ZengKeAi Communications Co., Ltd.International Journal of Transportation Science and Technology2046-04302025-03-011734536010.1016/j.ijtst.2024.05.004Workforce forecasting for state transportation agencies: A machine learning approachAdedolapo Ogungbire0Suman Kumar Mitra1Department of Civil Engineering, University of Arkansas, Fayetteville, AR 72701, USACorresponding author.; Department of Civil Engineering, University of Arkansas, Fayetteville, AR 72701, USAA decline in the number of construction engineers and inspectors at state transportation agencies (STAs) to manage the ever-increasing lane miles has emphasized the importance of workforce planning in these agencies. Forecasting workforce requirements is crucial for effective planning in any industry or agency. This study developed machine learning (ML) models to estimate the person-hour requirements of STAs at the project level. The Arkansas Department of Transportation (ARDOT) was used as a case study, using its employee and project details data between 2012 and 2021. ML regression models ranging from linear, tree ensembles, kernel-based, and neural network-based models were developed. These models were compared based on the accuracy of their predictions, the time taken for training the models and their prediction time. Predictions were tested based on the K-fold cross validation technique. The results indicated a high performance from the random forest regression model, a tree ensemble with bagging, which recorded a mean R-squared value of 0.91. Other ML models such as an ensemble neural network model and the linear models also proved to be fit for the problem, attaining R squared value as high as 0.80 and 0.78, respectively. These findings underscore the capability of ML models to provide more accurate workforce demand forecasts for STAs and the construction industry. This enhanced accuracy in workforce planning will contribute to improved resource allocation and management.http://www.sciencedirect.com/science/article/pii/S2046043024000595Long term forecastingEngineering workforceData-driven forecastingWorkforce planningPredictive modeling |
| spellingShingle | Adedolapo Ogungbire Suman Kumar Mitra Workforce forecasting for state transportation agencies: A machine learning approach International Journal of Transportation Science and Technology Long term forecasting Engineering workforce Data-driven forecasting Workforce planning Predictive modeling |
| title | Workforce forecasting for state transportation agencies: A machine learning approach |
| title_full | Workforce forecasting for state transportation agencies: A machine learning approach |
| title_fullStr | Workforce forecasting for state transportation agencies: A machine learning approach |
| title_full_unstemmed | Workforce forecasting for state transportation agencies: A machine learning approach |
| title_short | Workforce forecasting for state transportation agencies: A machine learning approach |
| title_sort | workforce forecasting for state transportation agencies a machine learning approach |
| topic | Long term forecasting Engineering workforce Data-driven forecasting Workforce planning Predictive modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2046043024000595 |
| work_keys_str_mv | AT adedolapoogungbire workforceforecastingforstatetransportationagenciesamachinelearningapproach AT sumankumarmitra workforceforecastingforstatetransportationagenciesamachinelearningapproach |