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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Incorporating Cost-Based Estimating Techniques into Bid-Based Estimating</title>
      <link>https://trid.trb.org/View/2709242</link>
      <description><![CDATA[Accurate and defensible cost estimates are critical for state departments of transportation (DOTs) to program funds, award contracts, and deliver projects on time. Most agencies rely on historical bid-based estimating, which is efficient but often fails to meet federal accuracy benchmarks. Contractors, by contrast, use cost-based estimating (CBE), building unit prices from labor, equipment, materials, and productivity. CBE is generally more accurate and defensible, especially during volatile markets or for unique items. Yet only a few state DOTs rely primarily on CBE, as compared with bid-based and hybrid approaches.

State DOTs require practical, low-burden methods to integrate CBE into bid-based workflows. Research is needed to (1) identify transferable CBE techniques that improve estimate reliability; (2) develop guidelines, decision trees, and templates for hybrid estimating; (3) define a minimum viable data specification for labor, equipment, and productivity inputs; and (4) validate guidelines developed and deliver an implementation playbook with training modules. 

The objective of this research is to provide guidance, resources, and tools based on CBE techniques that can be readily incorporated into a state DOT’s existing bid-based estimating process to produce more reliable and defensible engineer’s estimates. The desired outcome is a practical hybrid estimating framework that strengthens accuracy without requiring the full resource commitment of traditional CBE. ]]></description>
      <pubDate>Wed, 03 Jun 2026 11:18:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709242</guid>
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    <item>
      <title>Toledo-Detroit Ridership Feasibility &amp; Cost Estimate Study</title>
      <link>https://trid.trb.org/View/2696963</link>
      <description><![CDATA[The Toledo-Detroit Ridership Feasibility & Cost Estimate Study presents a pre-feasibility assessment of developing a passenger rail service between Toledo, Detroit, and Ann Arbor, with Detroit Metropolitan Wayne County Airport (DTW) serving as a central hub. Using Transportation Economics & Management Systems, Inc.'s (TEMS’s) business-planning methodology, the report evaluates route options, train technologies, ridership and revenue potential, capital and operating costs, and broader financial and economic effects. The study favors the CSX airport route because it directly serves DTW and can connect both Detroit and Ann Arbor on a single corridor, unlike the Wyandotte alternative, which the report finds more constrained by freight activity and less effective for passenger service. From TMACOG’s regional-development perspective, the report presents the CSX corridor not only as a transportation improvement but also as a strategic investment that could better integrate the economies of Toledo, Detroit, Ann Arbor, and DTW, expand access to jobs and markets, support income growth and property development, and strengthen the region’s connection to national and international travel through DTW’s role as a major gateway airport.]]></description>
      <pubDate>Sat, 23 May 2026 18:35:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696963</guid>
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    <item>
      <title>Border Crossing Delay Cost Analysis: Integration of Various Data Sources in the Direct Cost Estimation Tool</title>
      <link>https://trid.trb.org/View/2697866</link>
      <description><![CDATA[While previous studies have estimated border delay costs at specific locations or for limited vehicle types, there has been a lack of comprehensive, U.S.-wide tools that integrate multiple data sources to quantify direct economic impacts for both commercial and passenger vehicles. This study presents the findings of the Direct Cost Estimation Tool (DCET), a comprehensive framework for quantifying the direct economic impact of delays at U.S. land ports of entry. The research integrates multiple data sources to calculate delay costs for both commercial and passenger vehicles at 49 major border crossings. The methodology employs an approach that considers commodity-specific costs for commercial vehicles and value of time calculations for passenger drivers and passengers. Using 2024 data, the analysis reveals that, for U.S.-bound traffic at the selected crossings, border delays cost more than $1.5 billion annually, with $337 million attributed to commercial vehicles and $1.25 billion to privately owned vehicles. California experienced the highest passenger delay costs (58% of the national total), while Texas accounted for the largest share of commercial vehicle delay costs (61%). DCET serves as a valuable decision support tool for transportation planners, policymakers, carriers, and shippers to evaluate infrastructure investments and operational improvements at international border crossings. By quantifying these costs, stakeholders can better understand the economic implications of border inefficiencies and make data-driven decisions to enhance cross-border transportation.]]></description>
      <pubDate>Tue, 05 May 2026 10:16:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697866</guid>
    </item>
    <item>
      <title>Personal Vehicle Ownership and Operating Cost Calculator (Version 2.0) for Quantifying On-road Vehicle Operating Costs</title>
      <link>https://trid.trb.org/View/2691663</link>
      <description><![CDATA[In 2018, the Georgia Tech National Center for Sustainable Transportation (NCST) research team developed the Vehicle Ownership and Operating Cost Calculator (VCC) Version 1.0, allowing users to calculate and understand total vehicle ownership costs over the lifespan of the vehicle. Traditional resources typically found on automotive websites offer five-year cost projections, but often overlook or simplify long-term expenses such as financing, maintenance, energy use, and depreciation, which vary widely based on region, vehicle type, and individual driving habits. By allowing users to input personalized data, the calculator provides a tailored, detailed analysis of ownership costs, helping users make more informed decisions about vehicle purchases. The VCC is designed to serve as an educational resource (highlighting the cost categories associated with vehicle ownership) and as an instructional aid in courses that examine transportation planning and economic assessments. The VCC allows users to input data specific to their circumstances, including vehicle purchase price, loan details, annual mileage, insurance, energy costs, maintenance, and other costs like parking and tolls. Using data from sources such as the Georgia Department of Revenue’s vehicle pricing database and the U.S. Department of Energy’s Fuel Economy Database, the calculator provides customized cost estimates. The tool provides users (students and the public) with a thorough understanding of the full costs associated with lifetime vehicle ownership, by offering a comprehensive breakdown of ownership costs, including hidden expenses often overlooked in purchase decisions. The original model became dated, because the tool did not have the ability to automatically ingest and update vehicle ownership cost data. This project will update the tool with new data, develop data ingestion procedures, and modify output formats to support economic assessments of roadway design alternatives. To make the VCC accessible and support technology transfer, this project will update the calculator to accommodate the latest vehicle technologies (2018-2025) and to generate an online model presence. The research team will update fuel prices, maintenance, insurance costs, and depreciation rates to capture recent market changes. The team will also assess and implement enhanced reporting features to provide users with more detailed breakdowns and visualizations of ownership costs. Finally, the team will modify the structure of the model so that the tool can compile operating costs per vehicle-mile for observed and modeled on-road fleet compositions and operating conditions. The deliverables will include an updated version of the calculator accessible as both an Excel tool and a web interface.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:22:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691663</guid>
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    <item>
      <title>Evaluation and Development of Cost Prediction Models for Resurfacing Projects to Improve M&amp;R Analysis and Project Development</title>
      <link>https://trid.trb.org/View/2688790</link>
      <description><![CDATA[Accurate preliminary cost estimates for resurfacing projects are essential to conduct a reliable Maintenance & Rehabilitation (M&R) analysis, prioritize projects, and optimize the use of available budget. However, the Tennessee Department of Transportation (TDOT) is currently using an outdated cost per lane mile data for such analysis, and hence the results of such analysis can be less reliable. To address the issue, this study develops a framework and a tangible tool entitled “Resurfacing Cost Prediction (RCP).” This framework and tool require limited project characteristics, such as, project length and location, that are available at the early phase of project development. The validation of the tool achieved 100% compliance for accuracy based on AASHTO Practical Guide for Cost Estimation for three treatment types for planning phase. The study also addresses another issue related to project bundling. TDOT creates bundles of resurfacing projects to attract more contractors, achieve lower cost per lane mile, and reduce administrative burden. However, TDOT lacks a systematic methodology to create project bundles. As such, it relies on manual identification of projects suitable for bundling. This manual approach can be very time-consuming and cumbersome, and it can create inconsistent bundles. To address this issue, an Automated Maintenance Project Bundling (AMPB) tool is developed. The tool was able to achieve up to 92% accuracy in correctly identifying if a project should be bundled or not. These frameworks and tools are expected to aid TDOT in improving the planning and execution of resurfacing projects while optimizing the use of available budget.]]></description>
      <pubDate>Thu, 09 Apr 2026 10:35:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688790</guid>
    </item>
    <item>
      <title>Detection of Pavement Distresses Using 3D Laser Scanning Technology</title>
      <link>https://trid.trb.org/View/2164384</link>
      <description><![CDATA[The 3D laser scanning is one of the exceptionally versatile and efficient technologies for accurately capturing large sets of 3D coordinates. 3D laser scanner uses a technique that employs reflected laser pulses to create accurate digital models of existing objects. For 3D survey, detection of pavement distresses, such as potholes, large-area utility cuts or patches, is possible application where laser scanner technology excels. The traditional surveying and evaluation of distresses on pavement are extremely rough and restrictive as it implies lane or even entire road closures. In the study, the accurate 3D point-cloud points with their elevations were captured during scanning and extracted focusing on specific distress features by means of a grid-based processing approach. The experimental results indicate that the severity and coverage of distresses can be accurately and automatically quantified to calculate the needed amounts of filled materials. This application is the first attempt and can assist pavement engineers in monitoring pavement performance and estimating repair funding.]]></description>
      <pubDate>Sun, 29 Mar 2026 17:20:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2164384</guid>
    </item>
    <item>
      <title>Benders Decomposition for Multimodal Facility Location Allocation Problem Considering Capacity Levels and Uncertainty</title>
      <link>https://trid.trb.org/View/2591223</link>
      <description><![CDATA[Freight movement from various cities of origin in China, through consolidation centers and frontier ports, to different destinations in Europe within the China Railway Express logistics network is studied in this paper. The problem is formulated as a multi-capacity and multi-mode facility location-allocation problem with stochastic demand and delivery time which is modeled as a distributionally robust optimization problem. The objective is to minimize the total cost, which includes facility construction, transportation, and time delay costs. Given the stochastic nature of destination demand and transportation time, historical data is utilized to construct the ambiguity set of these stochastic parameters. Then, the proposed distributionally robust optimization problem is transformed into a two-stage deterministic optimization problem using probability and duality theory. An enhanced Benders decomposition algorithm is developed to solve the transformed problem that integrates several valid inequalities, multi-cut subproblem reformulation, and Pareto-optimal cuts to improve the performance of the algorithm. The computational experiments demonstrate that this improved Benders decomposition algorithm significantly outperforms the widely-used Gurobi solver in terms of solving speed. Finally, consolidation centers with different capacity levels are established in Xi’an, Urumqi, Chongqing, Shenyang, and Hohhot, and the corresponding transportation routes are given.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591223</guid>
    </item>
    <item>
      <title>Engineering-Adaptive Pavement Maintenance Decision-Making Model: A Reinforcement Learning Approach From Expert Feedback</title>
      <link>https://trid.trb.org/View/2591218</link>
      <description><![CDATA[The increase in highway mileage and lifespan is driving up the demand for road maintenance. With most research focusing on corrective maintenance, remedial maintenance(such as sealing and patching) optimization is understudied. Oriented toward remedial maintenance, data-driven models often fall short due to difficulty in establishing and implementing the model under complex road conditions, while the experts’ decision lacks consistency amidst multifaceted factors. To address this gap, this paper proposes a fine-grained maintenance decision model that combines data-driven methods with expert knowledge through Reinforcement Learning from Expert Feedback (RLEF). The experts’ experience introduced in decision-making model could improve the engineering application ability of decisions. The research uses a pavement performance prediction model as the environment and applies reinforcement learning to optimize strategies in the decision model. Additionally, the model integrates multidimensional expert feedback into reward functions to better understand ambiguous decision rules. Real-world data validation demonstrates that the RLEF model can adapt to engineering scenarios and applications better as well as achieve superior cost-effectiveness.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591218</guid>
    </item>
    <item>
      <title>Enhancement of AASHTOWare Bridge Management for Florida's Bridge Inspection and Asset Management</title>
      <link>https://trid.trb.org/View/2679072</link>
      <description><![CDATA[Following its adoption of the new American Association of State Highway and Transportation Officals (AASHTO) Bridge Element Inspection Manual, the Florida Department of Transportation (FDOT) has been collecting bridge element condition data in order to use the AASHTO’s Bridge Management (BrM) software. Now, to aid the Department in satisfying the various federal requirements and prepare for anticipated changes at the national level in the bridge inspection and management standards, the FDOT needs to re-calibrate the core BrM models and tools, including the element deterioration models, risk models, the translator model, and cost models. This study has developed deterioration models for forecasting bridge element condition within the analytical framework of the BrM and explored the revision of the FDOT’s environmental classification scheme for its bridge inventory. Considering the natural and man-made hazards that are unique to Florida, risk models were developed by estimating at each bridge location, the likelihood of occurrence of the hazard, the likelihood of service disruption, and costs of the associated consequences. To enable conversion of bridge element condition data to the FHWA’s National Bridge Inventory (NBI) ratings, a new NBI Translator was developed, with the focus on five bridge components: deck, superstructure, substructure, culvert, and channel. Finally, in response to the decreasing availability of cost data for bridge maintenance, repair and rehabilitation (MR&R) activities, this study developed unit costs based on the available historical costs (bridge work orders and the bid unit prices) and also formulated a methodology for developing crew-based cost estimates.]]></description>
      <pubDate>Tue, 17 Mar 2026 09:47:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679072</guid>
    </item>
    <item>
      <title>A cost model for road public transport sensitive to the operating program</title>
      <link>https://trid.trb.org/View/2630683</link>
      <description><![CDATA[Most models for estimating the cost per kilometer of local public transport (LPT) do not include variables attributable to the service operating program (SOP), that is the document showing lines, routes, stops, rides and related timetables. Therefore, by these tools it is not possible to take into account the impact, on the unit cost, of the different use of drivers and vehicles resulting from the number and time of the rides performed. This work, to overcome this important limitation proposes a modification, applicable to most existing models already including the other variables that most affect the cost. In particular, a statistical relationship has been built as a function of the descriptive variables of the SOP known to all, capable of providing a factor 𝘒 that, multiplied by the value of the cost derived from any model, allows it to be corrected as a result of the specific characteristics of the SOP. The methodology developed is of general validity. The model built for the calculation of 𝘒 has been calibrated on a regional LPT service offered in an area with weak demand. A specific recalibration would allow to use it in areas different in size, type and characteristics of transport demand and supply.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:56:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630683</guid>
    </item>
    <item>
      <title>Leveraging Radial Basis Function Regression and Bootstrapping for Price Adjustments in Road Contracts</title>
      <link>https://trid.trb.org/View/2562127</link>
      <description><![CDATA[Price escalation, the upward movement of costs, is managed in long-term road construction contracts through Price Adjustments. These adjustments mitigate fluctuations in input costs such as materials, labor, and equipment, preventing financial strain on contractors and ensuring project continuity. Typically, price adjustments rely on cost indices with assigned weightings. However, road agencies often incur higher-than-anticipated costs, necessitating optimized weighting determination. This paper explores Radial Basis Function (RBF) regression combined with bootstrapping to refine weightings in price adjustment formulas using historical price index data. RBF regression models the non-linear relationships between price index ratios and weightings, while bootstrapping enhances robustness by reducing the influence of outliers and quantifying variability. By averaging results over multiple iterations, this method improves reliability and provides contracting agencies with confidence in the adjustment process. Integrating bootstrapping with RBF regression offers a data-driven, flexible approach to setting price adjustment weightings. This enhances financial planning for contractors and road agencies, reducing risks and ensuring fair compensation in long-term construction projects.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562127</guid>
    </item>
    <item>
      <title>Hazard-Based Model for Setting Financial Milestones in Road Construction Contracts</title>
      <link>https://trid.trb.org/View/2562091</link>
      <description><![CDATA[Effective project management in road construction requires aligning financial milestones with progress. Setting these milestones at various stages—such as 6, 12, 18, 24, or 30 months—poses a challenge. The objective of this paper is developing a hazard-based methodology for setting such financial milestones. Prior studies have used probabilistic methods, including survival analysis and regression models, to predict completion times. Building on these approaches, this paper explores a semi-parametric Cox proportional hazards model, leveraging hazard rates from parametric models (log-logistic), to establish progress thresholds at key intervals. Using historical road project data—total cost, length, and completion time—this study demonstrates how parametric hazard rates can enhance milestone predictions. The proposed approach enables clients and project managers to set data-driven financial thresholds, improving predictability and risk management. By applying hazard analysis techniques, this methodology offers a systematic framework for timely, cost-effective infrastructure project delivery.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562091</guid>
    </item>
    <item>
      <title>Wind Power-Hybrid Clean Energy Microgrid Feasibility Study for Roadside Assets for VDOT</title>
      <link>https://trid.trb.org/View/2562090</link>
      <description><![CDATA[This study explores the potential of hybrid wind-solar energy systems with battery storage to enhance the resilience and sustainability of Virginia Department of Transportation (VDOT) roadside assets. For semi-permanent assets, such as digital message signs, road weather systems, and cameras, geospatial analysis using ArcGIS identified optimal locations for small wind turbines, considering wind speeds, setbacks, and environmental constraints. Solar PV systems were integrated to complement wind energy, providing a more stable energy supply. Energy outputs were modeled using the National Renewable Energy Laboratory’s System Advisory Model, revealing the potential of these systems to strengthen infrastructure and advance clean energy goals, despite longer payback periods. This research highlights the role of hybrid systems in improving the performance, reliability, and sustainability of VDOT infrastructure.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562090</guid>
    </item>
    <item>
      <title>Cost Overrun Prediction in Road Construction: A Fuzzy Logic and Clustering Approach</title>
      <link>https://trid.trb.org/View/2562041</link>
      <description><![CDATA[Cost overruns, percentage variations between the cost at bidding and the cost after construction, in road construction projects are influenced by multiple stakeholders, including contractors, clients, and consultants. Predicting these overruns remains a challenge, requiring models that rely on quantifiable project attributes. This study presents a fuzzy logic-based predictive model, integrated with K-means clustering and optimization, to estimate cost variations between bidding and final construction costs. The model utilizes historical road construction data, incorporating unit cost (cost per kilometer) and project length as key predictors. K-means clustering segments projects into three categories based on unit cost and length, improving predictive accuracy by grouping similar projects. Fuzzy logic is then applied to capture uncertainties, with membership functions for unit cost, length, and percentage variation dynamically optimized using Mean Squared Error (MSE) minimization. Results indicate that iterative optimization enhances prediction accuracy, enabling dynamic model adjustments. The proposed approach offers a practical tool for road agencies to forecast cost overruns more effectively, minimizing reliance on generalized assumptions. By clustering projects with similar characteristics and refining predictions through fuzzy logic, the model provides a data-driven method for improving cost management in road construction.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562041</guid>
    </item>
    <item>
      <title>Development of Equipment Rental Schedule for Illinois</title>
      <link>https://trid.trb.org/View/2657015</link>
      <description><![CDATA[During highway construction, the Illinois Department of Transportation (IDOT) resident engineer commonly adds “extra work” to the contract as needed for satisfactory completion of the project. One of the formats for contractor reimbursement requires establishing an hourly compensation rate for contractor-owned equipment used to perform the extra work and similar equipment owned by local agencies eligible for Motor Fuel Tax funding. Construction equipment rental rates vary widely according to factors, including equipment age, type, overhaul labor and parts, field labor and parts, capacity, estimated operating costs, availability, the geographic and climatic conditions at the job site, etc. It is critical that each highway agency, including IDOT, establish specific policies and standard guidelines to deal with construction equipment reimbursement in force account work in a fair manner to contractors. This project develops a comprehensive equipment rate schedule model to establish hourly compensation rates for contractor-owned equipment used in performing extra work. The model incorporates ownership costs—such as depreciation, overhead, and overhaul costs—and operating costs, including fuel, tire, and lubrication expenses. A methodology for annual rate updates is also developed. Additionally, the project delivers a user-friendly, web-based tool that can be operated and maintained by IDOT.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657015</guid>
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