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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>A Decision Support System for Road Maintenance Budget Allocation</title>
      <link>https://trid.trb.org/View/2159403</link>
      <description><![CDATA[The road transport system is a large and complex system and the allocation of resources to different maintenance activities requires an effective maintenance strategy based on a long-term perspective. This paper presents a simulation model based on System Dynamics methodology that can be used as a decision support system to effectively allocate road maintenance funds. The System Dynamics model is constructed in the Powersim modeling environment and it consists of four main module or sub-systems: 1) Financial sub-system, 2) Physical sub-system, 3) Functional sub-system, and 4) Evaluation sub-system. The model has been tested for several different maintenance policies. The behavior of the system was studied for several combinations of budget allocation that were used in the simulation experiments.]]></description>
      <pubDate>Sat, 07 Mar 2026 16:05:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2159403</guid>
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    <item>
      <title>Visualization in Collaborative Construction Management Decision Support System on Bridge Construction Project</title>
      <link>https://trid.trb.org/View/2159429</link>
      <description><![CDATA[Share precise information in the Construction Management tends to be difficult in the AEC Industry. For this reason Construction work is hardly carried out smoothly. This document was an attempt to propose the Collaborative Supported FAA application and CG-CICC system, and its application in the Sashiki Bridge construction process. Through the case studies, the evaluation method for them is found to be necessary not only by the cost and the period of production but by some other performance evaluators.]]></description>
      <pubDate>Sat, 07 Mar 2026 16:05:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2159429</guid>
    </item>
    <item>
      <title>A Decision Support System for Life-Cycle Management of Thruway Bridges</title>
      <link>https://trid.trb.org/View/2160389</link>
      <description><![CDATA[This paper presents a data warehousing approach to develop a decision support system for the engineers to enhance their management operation on the Thruway bridges. The system consists of a data warehouse and analytical tools aimed at improving the life-cycle performance of bridge infrastructure. The data warehouse equips with the capabilities to perform decision queries, design ad hoc reports, perform online analytical processing, and discover information and business solutions through data mining. Various tools available for analytical processing and data mining are based on a multidimensional data model, which aims at improving the condition, capacity, and safety of bridges with a multi-faceted solution for Thruway engineers to manage routine maintenance as well as rehabilitation and replacement. An incremental data warehousing methodology is applied in the development process to address the business needs of infrastructure management. Results are to enhance serviceability and maintenance economy of the highway infrastructure facilities throughout their service life.]]></description>
      <pubDate>Sat, 07 Mar 2026 16:05:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2160389</guid>
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    <item>
      <title>Assessing the Impacts of Safety-Focused Design Interventions on Arterial Roadways</title>
      <link>https://trid.trb.org/View/2677552</link>
      <description><![CDATA[Arterial roadways serve as critical connectors in urban transportation networks, yet their design often prioritizes vehicular mobility over safety. Despite the widespread application of safety-focused infrastructure interventions on local and collector streets, similar strategies are rarely implemented on arterials due to concerns over congestion, emergency response, and operational efficiency. However, these design choices have proven to result in unsafe conditions.

This project investigates how infrastructure design interventions can improve safety on arterial roadways while addressing operational and institutional constraints. The research follows a phased approach. First, it examines the historical, regulatory, and policy factors that have limited the adoption of safety-focused interventions on arterials, including the influence of fire codes and emergency response standards. Second, it assesses the real-world impacts of infrastructure changes on speeds, crashes, and emergency response metrics. Finally, it synthesizes findings to develop actionable recommendations and a decision-making framework for arterial design.

By providing an evidence-based understanding of how design choices affect safety, mobility, and community outcomes on arterial corridors, this study aims to inform infrastructure design practices.]]></description>
      <pubDate>Tue, 03 Mar 2026 20:07:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2677552</guid>
    </item>
    <item>
      <title>Field Test and Evaluation of a Mobile Automated Winter Road Condition Reporting System</title>
      <link>https://trid.trb.org/View/2635955</link>
      <description><![CDATA[Timely and accurate monitoring of road surface conditions (RSC) in a winter season is essential to the winter road maintenance (WRM) managers, commercial vehicle operators, as well as the travelling public. Existing RSC monitoring methods rely heavily on manual reporting by patrollers, lacking objectivity, reliability, and timeliness required to optimize decision-making of maintenance and commercial vehicle drivers. This project evaluates the performance of a smartphone based road surface condition monitoring system called AVL-Genius. The system was installed on eight patrol and maintenance vehicles, which were operated on a section of Highway 6 to collect field data in the Winter 2013/14 season. The performance of the AVL-Genius system was evaluated in three aspects, namely, spot-wise monitoring, route-level monitoring and reliability, by comparing its monitoring results to those of manual classifications, patrol reports and the Ontario Ministry of Transportation's Travellers Road Information Portal (TRIP). This report details the main methodology and analysis results along with an overview of various RSC monitoring technologies. The main findings and recommendations for further research and development are highlighted.]]></description>
      <pubDate>Mon, 02 Mar 2026 16:12:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635955</guid>
    </item>
    <item>
      <title>Effect of Video Camera-Based Remote Roadway Condition Monitoring on Snow Removal-Related Maintenance Operations</title>
      <link>https://trid.trb.org/View/2636020</link>
      <description><![CDATA[Remote monitoring through the use of cameras is widely utilized for traffic operation but has not been utilized widely for roadway maintenance operations. The Utah Department of Transportation (UDOT) has implemented a new remote monitoring system, referred to as a Cloud-enabled Remote Video Streaming (CRVS) camera system for snow removal-related maintenance operations in the winter. The purpose of this study was to evaluate the effectiveness of the use of the CRVS camera system in snow removal-related maintenance operations. This study was conducted in two parts: opinion surveys of maintenance station supervisors and an analysis on snow removal-related maintenance costs. The responses to the opinion surveys mostly displayed positive reviews of the use of the CRVS cameras. On a scale of 1 (least effective) to 5 (most effective), the average overall effectiveness given by the station supervisors was 4.3. An expedition trip for this study was defined as a trip that was made to just check the roadways if snow-removal was necessary. The average of the responses received from surveys was calculated to be a 33 percent reduction in expedition trips. For the second part of this study, an analysis was performed on the snow removal-related maintenance cost data provided by UDOT to see if the installation of a CRVS camera had an effect in reducing expedition trips. This expedition cost comparison was performed for 10 sets of maintenance stations within Utah. It was difficult to make any definitive inferences from the comparison of expedition costs over the years for which precipitation and expedition cost data were available; hence a statistical analysis was performed using the Mixed Model ANOVA. This analysis resulted in an average of 14 percent higher ratio of expedition costs at maintenance stations with a CRVS camera before the installation of the camera compared to the ratio of expedition costs after the installation of the camera. This difference was not proven to be statistically significant at the 95 percent confident level but indicated that the installation of CRVS cameras was on the average helpful in reducing expedition costs and may be considered practically significant. It is recommended that more detailed and consistent maintenance cost records be prepared for accurate analysis of cost records for this type of study in the future.]]></description>
      <pubDate>Mon, 02 Mar 2026 16:12:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636020</guid>
    </item>
    <item>
      <title>A New Method of Determining Payment for In-Place Concrete with Double-Bounded Compressive Strength Pay Factors</title>
      <link>https://trid.trb.org/View/2669643</link>
      <description><![CDATA[The Vermont Agency of Transportation currently uses a lower acceptance limit on 28-day concrete compressive strength (CCS) of 4,000 psi for acceptance of in-place concrete in its construction projects, particularly for placement of bridge decks. Over time, to reduce risk, the concrete industry’s response has led to increasingly higher average 28-day CCS, which is believed to be associated with increased brittleness and excessive early cracking. These findings have led to a recommendation to establish a target mean CCS of around 5,000 psi with pay factors and they support the argument for including an upper acceptance limit when CCS is used as a performance characteristic. Under this type of performance specification, pay factors are typically enforced for payment using the percent-within-limits (PWL) quality measure. A drawback of the PWL is its implicit assumption that the distribution of 28-day CCS is Gaussian so that z-scores can be used for assessment of payment. Our research team’s review of the literature and historical data suggests that the distribution of resulting industry-wide CCS is not likely to be Gaussian, especially once the double-bounded acceptance range is implemented. The goal of this project was to develop a new quality measure for payment of in-place CCS that does not rely on the Gaussian distribution and allows a variety of pay factors around the target mean. A new approach was developed, called the percent-within-distribution (PWD), which calculates a quality measure from a 28-day CCS sample by comparing the sample to any type of design distribution using a Bayes process. Random variables were used to guide the new approach and the simulated responses that the industry might take. We showed how the new quality measure can be used for acceptance and payment under a double-bounded pay factor schedule, but also how it could be used to design a pay factor schedule in the absence of complete lifecycle cost data. The research team also created a decision-support tool to manage the implementation of the new approach. The tool allows the user to specify and visualize their design distribution, then calculate the PWD from a sample. The tool is based in MS Excel so that it will be useful to a variety of DOT quality assurance/quality control (QA/QC) personnel.]]></description>
      <pubDate>Mon, 02 Mar 2026 13:24:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669643</guid>
    </item>
    <item>
      <title>Configuring Taiwan’s highway bridge performance indicator: a semi-quantitative risk assessment approach</title>
      <link>https://trid.trb.org/View/2663636</link>
      <description><![CDATA[The Taiwan Bridge Management System (TBMS) calculates the condition index (CI) of every highway bridge in its inventory using the latest findings from biennial routine inspections. However, the computational approach for CI can yield skewed results and make a bridge appear healthier than it actually is. To address this issue, a highway bridge performance indicator called the bridge risk index (RI) is presented. The RI is determined by combining the risks associated with the failure of each bridge component, which are evaluated using a semi-quantitative risk assessment approach. This evaluation considers the probability and consequence of a bridge component’s failure as a function of a component's deterioration state and criticality to the bridge’s overall structural safety and serviceability, respectively. To validate the effectiveness of the RI, inspection data from a sample of seven highway bridges were extracted from the TBMS and used to compare the RI with the CI. The results showed that the RI and its associated parameters can mitigate the inherent issues observed with the CI. Hence, the RI has the potential to be a valuable decision-support tool for transport agencies that rely on similar routine inspection processes to enhance their bridge management systems.]]></description>
      <pubDate>Fri, 27 Feb 2026 11:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663636</guid>
    </item>
    <item>
      <title>Adaptive electric vehicle routing and charging with deep reinforcement learning</title>
      <link>https://trid.trb.org/View/2656336</link>
      <description><![CDATA[As electric vehicles (EVs) gain popularity, efficient routing and charging solutions remain challenging due to time-dependent travel variability, sparse charging infrastructure, and heterogeneous user preferences. To address these challenges, this paper introduces a decision-support system that integrates three complementary methods: Temporal Multimodal Multivariate Learning (TMML) for real-time characterization of travel time uncertainty, Time-Dependent Shortest Path (TDSP) for reliability-aware route choice, and Deep Q-Network (DQN) reinforcement learning for adaptive charging decisions in sparse infrastructure environments. TMML updates link-level travel time distributions in real-time through Bayesian inference with cluster-based propagation, reducing uncertainties across the network. TDSP leverages these updated distributions to estimate remaining travel time and reliability scores for route planning. DQN learns optimal charging policies by determining when to charge, how much to charge (partial charging at 25%, 50%, 75%, or 100% levels), and which route to take based on battery state, traffic patterns, and available stationary charging stations (SCSs) and mobile charging infrastructure—including Mobile Energy Distributors (MEDs) and Dynamic Inductive Charging (DIC). DQN training uses simulation-based learning from actual traffic patterns of the Washington, DC metropolitan region, allowing the agent to explore charging-route pairs and discover efficient solutions through trial and error. To accommodate heterogeneous user preferences, the system calculates multiple Pareto-optimal solutions that trade off travel time, charging cost, battery safety, and route reliability, enabling users to select alternatives that match their current priorities without specifying preference weights in advance.]]></description>
      <pubDate>Wed, 25 Feb 2026 13:59:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656336</guid>
    </item>
    <item>
      <title>Sometimes right, sometimes wrong: Drivers’ responses to inconsistently accurate automated vehicle system confidence information</title>
      <link>https://trid.trb.org/View/2659631</link>
      <description><![CDATA[Automated vehicles (AVs) are becoming increasingly equipped with intelligent functions that support drivers’ decision-making. Human-machine interfaces (HMIs) that communicate an AV’s confidence in its ability to navigate challenges in the driving environment are expected to become a pervasive feature. While this type of confidence display can enhance drivers’ situation awareness, information presented to drivers may not always reflect accurate, real-world conditions, which can misguide perceptions and contribute to poor decision-making. Also, repeated exposure to inconsistently accurate information can reinforce negative biases. This study investigates how initial exposure to a series of both accurate and inaccurate information affects AV drivers’ perceptions, behavior, and physiological responses in later interactions. Using a visual HMI displaying an AV’s self-assessed confidence in avoiding a roadway obstacle, in a first phase, thirty participants were (unknowingly) assigned to two groups: one initially exposed to accurate confidence information, and the other to inaccurate confidence information. In the second phase, participants experienced the reversed information accuracy condition. The vehicle was highly reliable, but the AV confidence information was manipulated to either be aligned or misaligned with the system reliability. Across 12 takeover scenarios, drivers decided whether to take control of the vehicle, and their takeover decisions, trust levels, and physiological responses were collected. Overall, participants who were initially exposed to accurate information demonstrated heightened attention, faster voluntary takeovers, higher trust, and increased reliance on system information. In contrast, those initially exposed to inaccurate information spent more time monitoring the driving environment. Also, participants initially exposed to accurate information displayed higher cognitive workload (measured physiologically) and unchanged trust levels. This observation was also true when inaccurate information was presented later. The number of voluntary takeovers did not differ between the two groups. These findings highlight the role of initial information presentation in shaping drivers’ perception and behavior, offering insights for designing AV systems that support effective human-AV interactions.]]></description>
      <pubDate>Wed, 25 Feb 2026 13:58:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659631</guid>
    </item>
    <item>
      <title>Effects of false alarms and miscues of decision support systems on human–machine system performance: a study with airport security screeners</title>
      <link>https://trid.trb.org/View/2625881</link>
      <description><![CDATA[We tested 115 professional airport security screeners using realistic X-ray images of cabin baggage.Screeners were supported by an explosives detection system for cabin baggage (EDSCB) to help them detect bombs.Besides correct explosives alarms, the EDSCB made false alarms on target-absent images or miscues on images containing guns or knives that were localised elsewhere on the image.Screeners missed more knives when the EDSCB provided miscues than when the EDSCB provided no miscues.Instead of on-screen alarm resolution of EDSCB alarms in primary screening, automated decision or clear instructions to screeners should be considered.]]></description>
      <pubDate>Tue, 24 Feb 2026 09:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625881</guid>
    </item>
    <item>
      <title>Joint bunker fuel and freight revenue management in liner shipping: A decision-focused learning approach</title>
      <link>https://trid.trb.org/View/2626024</link>
      <description><![CDATA[The volatility of bunker fuel prices significantly impacts the maritime industry due to increasing regulatory complexities and market uncertainties. This study focuses on addressing these challenges by identifying bunker price prediction as a critical area of impact for improving operational efficiency and decision robustness. A comprehensive optimization model has been proposed to determine optimal ship speed and bunkering strategies both within and outside Emission Control Areas (ECAs). The traditional two-stage method involves training predictive models for bunker price, with the predictions being used as input parameters to solve the optimization problem. However, the loss function in this two-stage method does not consider the effect of predictions on the downstream decision-making problem. Therefore, this study adopts an integrated framework, Decision-Focused Learning (DFL) that unifies prediction and optimization processes into a cohesive approach. Unlike traditional sequential approaches, this method embeds the predictive model directly within the optimization process. It is comparatively more capable of handling data-driven optimization problems than traditional two-stage methods. In this study, computational experiments show that how DFL outperforms traditional methods, achieving 1.29% lower Normalized Regret, which translates to predictions resulting in approximately $22,730 higher profit for liner services. These findings offer valuable managerial insights into improving efficiency and sustainability in liner shipping operations by making better decisions under real world uncertainties.]]></description>
      <pubDate>Tue, 24 Feb 2026 09:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2626024</guid>
    </item>
    <item>
      <title>MDSS Implementation Costs in Wisconsin</title>
      <link>https://trid.trb.org/View/2635951</link>
      <description><![CDATA[Maintenance Decision Support System (MDSS) technology is a tool initially developed using Federal Highway Administration (FHWA) funding. It is designed to integrate state-of-the-art weather forecasting and pavement modeling with an agency’s rules of practice for winter operations to generate recommended maintenance actions. The goal is to enhance winter maintenance decision making in order to provide more effective responses to winter weather events. This report attempts to quantify the costs associated with the deployment of the Pooled Fund Study MDSS in Wisconsin so that other state departments of transportation (DOTs) have an idea of the various levels of deployment possible and the costs associated with each.]]></description>
      <pubDate>Mon, 23 Feb 2026 16:30:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635951</guid>
    </item>
    <item>
      <title>Effects of functional spaces on small logistics facilities: machine learning-based decision-support tool</title>
      <link>https://trid.trb.org/View/2663881</link>
      <description><![CDATA[Urban logistics have shifted from a “goods-to-store” to a “goods-to-residents” paradigm, resulting in the proliferation of small logistics facilities such as micro-hubs and pick-up points in urban areas. As these facilities are increasingly embedded within urban functional spaces, understanding the driving factors of their spatial layout has become ever more crucial. This study examines the spatial layout characteristics and influencing factors of small logistics facilities in the Wuhan Metropolitan Development Area between 2010 and 2018. A Gradient Boosting Decision Tree (GBDT) model is employed to analyze the nonlinear relationships and threshold effects of urban spatial variables on facility layout. The results indicate that land price exerts the strongest influence on spatial distribution, followed by transportation accessibility and demand-related variables in 2010 and 2018, respectively. Based on these findings, a decision-support tool is developed to optimize facility placement, offering policy-relevant insights for sustainable urban logistics planning from a supply–demand perspective.]]></description>
      <pubDate>Fri, 20 Feb 2026 14:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663881</guid>
    </item>
    <item>
      <title>Advanced Framework for Risk Coupling Analysis applied to Ship Groundings in Shenzhen Port</title>
      <link>https://trid.trb.org/View/2630792</link>
      <description><![CDATA[In a socio-technical system, risk coupling mainly refers to the interaction among risk factors that jointly amplify the likelihood or severity of accidents. Grounding is a major type of maritime accident with significant environmental, economic, and societal impacts, making it essential to understand both individual risk factors and their coupling effects for effective prevention. However, existing coupling analysis methods often fail to account for multiple homogeneous risk factors, the uncertainty associated with risk coupling effects, and the specific operational conditions of vessels. This paper proposes a novel framework for quantifying risk coupling effects that addresses these limitations. By modifying the traditional N-K model, we quantify the mutual information (MI) of both homogeneous and heterogeneous coupled risk factors under specific conditions, such as seasonal variation, with interval-valued MI to represent coupling effects. The results reveal that these effects can be either amplified or attenuated depending on the risk group and the operational context. Within a single risk factor group, multiple factors exert a significant, though not absolute, influence. Based on an analysis of rescue and response measures, effective decoupling strategies are proposed. The findings enhance the understanding of the complex risk dynamics in grounding incidents and offer practical decision-support tools for ship crews, emergency responders, and maritime authorities.]]></description>
      <pubDate>Wed, 18 Feb 2026 13:22:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630792</guid>
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