State Transportation Agencies (STAs) are faced with many questions as to how they should deliver projects without causing major delays and hazards to the traveling public while at the same time keeping project cost and quality within the acceptable and sustainable parameters. These conflicting constraints highlight the need for STAs to have guidance on how to streamline the entire process of planning, procurement, construction, and operation and maintenance.
i2dEAS LAB has sought to address these challenges by developing, experimenting, validating, and implementing groundbreaking decision-support models and analytical tools through an interdisciplinary view that integrates advanced planning concepts and streamlined construction management strategies into transportation informatics to promote intellectual cross-pollination with fields outside lab's areas of expertise.
i2dEAS LAB strategically aims at addressing unique spectrum of challenges and issues facing state transportation agencies, daily commuters, and business enterprises in heavily congested metropolitan areas. The following are the brief descriptions of each of the four proposed Strategic Research Areas (SRAs).
SRA1: Integrated Informatics and Automation with Sensing Technologies
SRA1-1: Integrated spatiotemporal modeling for construction work zones
The main objective of this research is to create, test, exploit, and validate a spatiotemporal analytical modeling framework that fully automates and optimizes impact assessments of construction work zones (CWZ). Changes to the Work Zone Safety and Mobility Rule, enacted by the Federal Highway Administration in October 2007, mandate that CWZ impact assessments be completed for all federally-funded highway infrastructure improvement projects. These impact assessments are critical to the selection of the most effective construction alternatives for highway rehabilitation projects in urban corridors, but they are also difficult and expensive to produce. State Transportation Agencies (STAs) immediately began to struggle with increased overhead costs and construction scheduling delays. As a solution to these issues, we propose a decision-support model called SWAT. SWAT utilizes large volumes of real-world traffic sensor data to integrate traffic prediction technique with CWZ impact assessment. The SWAT’s modeling frameworks are unique as they study the impact of CWZ on traffic from a quantitative perspective using real-world transportation data, providing numeric measurements of the impact to the surrounding areas.
SRA1-2: Stochastic optimization of incentives/disincentives (I/D)
The main objectives of this study are to create a new decision-support analytical framework of optimal I/D and test whether it can reasonably and realistically determine and justify the most economical I/D dollar amounts. This study blends existing schedule and traffic simulation techniques with a stochastic analysis. Determining I/D rates is extremely difficult due largely to the lack of systematic methods for helping state transportation agencies (STAs) determine effective I/D rates. This proposed research provides research communities and industry practitioners with the first holistic view to determine the most economical and realistic I/D dollar amounts for a given project—an optimal value that allows the agency to stay within budget while at the same time effectively motivating contractors to use their ingenuity to complete the projects earlier. Once successfully completed, the model can help agency engineers and decision makers make better-informed decisions and allocate more realistic incentives, which will result in more favorable cost-benefit ratios and better use of public funds. Critically, it will also significantly reduce the agency’s expenses in the time and effort required for determining I/D dollar amounts.
SRA1-3: Life-cycle cost analysis (LCCA) decision-support model based on PMIS
For major highway pavement rehabilitation projects, a federal rule enforces the agencies to perform a Life-Cycle Cost Analysis (LCCA) that accounts for both the agency cost and the future maintenance cost. For instance, TxDOT mandates the implementation of a LCCA for significant highway pavement construction projects with greater than 30 percent trucks or 100,000 average daily traffic volumes. The following three critical problems in the current LCCA practice are observed: 1) existing methods like RealCost require too many intuitive assumptions; 2) existing methods are time consuming; and 3) accuracy of the analysis performed under the numerous unrealistic assumptions is highly questionable. This project aims at developing a groundbreaking decision-support computer model for quantifying the most realistic, reliable life-cycle costs that account for the total project cost, the future maintenance cost and the road user cost within a viable integration analysis framework using a series of K-means cluster-driven regression analyses based on the big data gathered through the Pavement Management Information System (PMIS). This research effort is the first of its kind, as the existing tools including RealCost require many intuitive assumptions and judgments. The proposed decision-support model has the great potential to greatly assist state transportation agencies to carry out a LCCA with the reliable estimation of the agency cost and road user cost. A more accurate life-cycle cost can help STAs make better-informed decisions when selecting the most feasible project scenarios among numerous alternatives, which can translate to better use of public funds. Estimating long-term maintenance cost and road user cost is the cornerstone of the LCCA. Therefore, methods developed from this project have the great potential to improve the accuracy of LCCA.
SRA2: Sustainable Infrastructure Planning & Development
- GIS-based urban sustainability model to assess the impact of LEED sustainable site credits.
- Economic impact analysis of LEED public transportation accessibility.
- Modeling future highway maintenance costs to support a life-cycle cost analysis for major infrastructure improvement projects.
- Economic Input-Output Life-Cycle Assessment (EIO-LCA) of highway pavement rehabilitation alternatives to assess sustainable use of energy, external costs, and toxic emissions (see figure below exemplifying a LCA analysis procedure).
SRA3: Improved Project Delivery and Transportation Project Management: Holistic Analysis Approach
- How to balance access, mobility, and livability in planning, constructing and executing multimodal transportation systems in urban corridors.
- Stochastic modeling, discrete-event simulations, and advanced statistical analyses aiming at (1) investigating the marginal impact of various project delivery systems and contracting methods on key performance measures while controlling for different project and market characteristics (see figure below (right)) and (2) developing decision-support computer applications of the improved contracting projects. The root problem this study addresses is how to determine when and what type of delivery method to use in order to realize the maximum benefits with regards to project type, size, and complexity.
SRA4: Cost-Schedule-Change-Labor Productivity-Risk-Quality-Profitability Interdependence Analysis
- Interdependence analysis of schedule-cost-change-productivity-risk-quality associated with critical highway rehabilitation projects.
- Advanced statistical analyses including a non-linear regression analysis built in a set of clusters with the goal of (1) analyzing the macroeconomic performance of construction industry as a whole and at fourteen of its sub-sectors in terms of labor productivity, gross margin, and worker’s wages and (2) developing a quantitative model that predicts a firm’s profitability by analyzing various levels of labor productivity and taking crucial external factors into account.