Mapping of Practices of State Transportation Agencies for Consultant Oversight of Construction Engineering and Inspection Services

Author(s):  
Valerie Carrasco Torres ◽  
Mohammad Moin Uddin ◽  
Paul M. Goodrum ◽  
Keith R. Molenaar
Author(s):  
Sara Al-Haddad ◽  
Ying Li ◽  
Paul M. Goodrum ◽  
Timothy R.B. Taylor ◽  
Ray L. Littlejohn

State transportation agencies (STA) are relying on needs-based construction, engineering, and inspection (CEI) consultants as a primary solution to their staffing deficits. While other studies have examined the reasons STAs hire CEI consultants at an agency level, prior research has not identified potential patterns between project characteristics and STA staffing choices. A national survey was administered to examine how the use of CEI consultants differs by project type, work type, complexity level, and the authority level of inspectors. A rigorous model-building variety of Chi-squared analyses, Cochran’s Q tests, McNemar tests, and binomial logistic regression models were used to analyze the data. This research found that STAs are more likely to use consultants on projects with utilities, drainage, roadway, and/or grading because they either do not have enough staff in-house or do not have the experience in-house to complete these projects. Additionally, most STAs do not grant senior inspector consultants the same authority level as their in-house counterparts. Generally, this research indicates that socio-economic and political factors have both short- and long-term effects on staffing choices in public transportation projects and studying project characteristics might help shed more light on the effects of these decisions. Specifically, the results indicate a need for both the private and public sectors to collaborate and share knowledge to preserve institutional knowledge within agencies. These results suggest that further research into staffing trends and project characteristics is warranted.


Author(s):  
Karla Diaz Corro ◽  
Taslima Akter ◽  
Sarah Hernandez

Increased demand for truck parking resulting from hours-of-service regulations and growing truck volumes, coupled with limited supply of parking facilities, is concerning for transportation agencies and industry stakeholders. To monitor truck parking congestion, the Arkansas Department of Transportation (ARDOT) conducts an annual observational survey of truck parking facilities. As a result of survey methodology, it cannot capture patterns of diurnal and seasonal use, arrival times, and duration. Truck Global Positioning System (GPS) data provide an apt alternative for monitoring parking facility utilization. The issue is that most truck GPS datasets represent a sample of the truck population and the representativeness of that sample may differ by application. Currently no method exists to accurately expand a GPS sample to reflect population-level truck parking facility utilization. This paper leverages the ARDOT study to estimate GPS “expansion factors” by parking facility type and defines two expansion factors: (1) the ratio of trucks parked derived from the GPS sample to those observed during the Overnight Study, and (2) the ratio of truck volume derived from the GPS sample to total truck volume measured on the nearest roadway. Varied expansion factors are found for public, private commercial (e.g., restaurant, retail store, etc.), and private truck stop facilities. Comparatively, the expansion factor based on roadway truck volumes was at least twice as high as that derived from the Overnight Study. Considering this, the method to determine expansion factors has significant implications on the estimated magnitudes of parking facility congestion, and thus will have consequences for investment prioritization.


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