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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7UHJvYmFiaWxpc3RpYyBtb2RlbHMmcXVvdDsiIG9yaWdpbmFsX3ZhbHVlPSImcXVvdDtQcm9iYWJpbGlzdGljIG1vZGVscyZxdW90OyIgLz48L2ZpbHRlcnM+PHJhbmdlcyAvPjxzb3J0cz48c29ydCBmaWVsZD0icHVibGlzaGVkIiBvcmRlcj0iZGVzYyIgLz48L3NvcnRzPjxwZXJzaXN0cz48cGVyc2lzdCBuYW1lPSJyYW5nZXR5cGUiIHZhbHVlPSJwdWJsaXNoZWRkYXRlIiAvPjwvcGVyc2lzdHM+PC9zZWFyY2g+" rel="self" type="application/rss+xml" />
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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>Probabilistic prediction models for crack initiation and progression of spray sealed pavements</title>
      <link>https://trid.trb.org/View/1563301</link>
      <description><![CDATA[Cracking is one of the primary distress modes in spray (chip)-sealed pavement surface performance and its prediction is a major concern for pavement engineers. In order to identify, manage and assess effectively and efficiently cracked pavement at a network level, a probabilistic modelling approach is utilised to develop cracking initiation and progression models. This study aims to predict the probability of pavement cracks occurring using a binary logistic model and cracks progression over time using an ordinal logistic regression model. These models have been developed to take into account the effect of variations among observations, among sections and among highways. Readily available historical time series data (from 2004 to 2011) from 40 highway segments have been collected and prepared for modelling. These time series include surface cracking as a performance parameter and traffic loading, expansion potential of subgrade soil, climate condition, condition of drainage system and pavement strength as predictor parameters. Cracking data include all types of cracking: transverse, longitudinal and crocodile cracking and are reported as a percent of the affected area. The study estimates the probability of crack initiation at a certain time and predicts the probability of a pavement maintaining its current level of cracking. It is found that with the 50% estimated probability, about 82% of the observations are correctly predicted by the crack initiation model and 65% of the observations are correctly predicted by the crack progression model. The study has concluded that the effect of time is stronger than the other variables on crack initiation and progression. Also, the effect of traffic loading is stronger than the effect of initial pavement strength in crack initiation phase. However, the effect of pavement strength at any time is stronger than the effect of traffic loading in crack progression phase. The predicted probabilities have been successfully validated using another data-set from the same network and the results indicate that the developed probability models are well estimating the crack conditions and have the ability to predict future conditions accurately.]]></description>
      <pubDate>Sat, 03 Nov 2018 15:17:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1563301</guid>
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      <title>Sensitivity Analysis of Performance Metrics to Different Parameters in Pavement Management System</title>
      <link>https://trid.trb.org/View/1495412</link>
      <description><![CDATA[A pavement management system is a useful tool for departments of transportation to address the problems of limited budget and aging infrastructures. Previous research focuses mainly on the budget allocation process, trying to improve the optimization algorithms and consider the uncertainty of predictions for pavement deterioration. In any given pavement management system, there are usually many parameters. However, analysis has not been performed to determine the influence of different parameters on the pavement network performance. In this paper, the sensitivity of performance metrics to different parameters is explored based on the interstate pavement network in the U.S. state of Virginia by a probabilistic allocation network model developed at the Massachusetts Institute of Technology (MIT). A statistical method is applied to conduct the sensitivity analysis. The sensitivity of performance metrics to different parameters is decided by p values, and the relative significance of different parameters is compared and ordered by z-score statistics.]]></description>
      <pubDate>Tue, 06 Feb 2018 18:14:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1495412</guid>
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    <item>
      <title>Probabilistic Approach for Selection of Composition of Freeze–Thaw-Resistant Ordinary Portland Cement Concrete</title>
      <link>https://trid.trb.org/View/1494341</link>
      <description><![CDATA[This paper features the development of a probabilistic model linking freeze-thaw (F-T) performance of concrete mixtures to their composition.  As part of the process of model development, a sensitivity analysis was performed on several concrete mixture parameters to identify these factors that have strong correlations with the F-T resistance of concrete. This sensitivity analysis was performed on 128 sets of experimental F-T test results collected from the literature. The F-T performance level was defined as a discrete measure of the frost resistance of concrete. Finally, a new model to predict the F-T damage of concrete incorporating the variability of the concrete mix parameters (as selected from sensitivity analysis) was developed. This model was developed using only these data sets which contained the results of the relative dynamic modulus of elasticity (RDME) testing performed according to the ASTM C 666 (AASHTO T 161) specifications. Furthermore, only mixtures containing ordinary portland cement (OPC) as a sole type of the binder (i.e., mixtures that did not contain any supplementary cementitious materials) were considered.  Additional experimental test results were utilized to validate the model. The reliability of the model was further demonstrated using several examples of concrete mixtures of various compositions. Furthermore, the effects of the number of F-T cycles, air content, paste content, and w/c ratio on the F-T performance of the concrete mixes were demonstrated using the developed model. Accordingly, this model provides the opportunity to optimize the concrete mix proportion for the required performance level of concrete under F-T exposure condition.]]></description>
      <pubDate>Sun, 21 Jan 2018 17:53:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1494341</guid>
    </item>
    <item>
      <title>A nested modelling approach to infrastructure performance characterisation</title>
      <link>https://trid.trb.org/View/1493706</link>
      <description><![CDATA[Reliable and accurate predictions of infrastructure condition can save significant amounts of money for infrastructure management agencies through better planned maintenance and rehabilitation activities. Infrastructure deterioration is a complicated, dynamic and stochastic process affected by various factors such as design, environmental conditions, material properties, structural capacities and some unobserved variables. Previous researchers have explored different types of modelling techniques, ranging from simple deterministic models to sophisticated probabilistic models, to characterise the deterioration process of infrastructure systems; however, these models have limitations in various aspects. Traditional deterministic models are inadequate to capture the uncertainties associated with infrastructure deterioration processes. State-based probabilistic models can only predict conditions at fixed time points. Time-based probabilistic models require frequent observations that, in practice, are not easy to perform. The goal of this research is to develop a new probabilistic model that is capable of capturing the stochastic nature of infrastructure deterioration, while at the same time avoiding the limitations of previous modelling efforts. The proposed nested model is based on discrete choice model theory. It can be used to predict the probability of an infrastructure system staying at defined condition states by relating an index representing the performance of the infrastructure to a number of explanatory variables that characterise the structural adequacy, traffic loading and environmental conditions of the infrastructure. The proposed model includes different possible implementation paths (sequential versus multinomial) depending on the considered explanatory variables and the available data. In the case study, the proposed probabilistic model is implemented with pavement performance data collected in Texas, yielding promising preliminary results.]]></description>
      <pubDate>Sun, 14 Jan 2018 17:58:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1493706</guid>
    </item>
    <item>
      <title>Slow Crack Growth Evaluation of Vintage Polyethylene Pipes</title>
      <link>https://trid.trb.org/View/1492883</link>
      <description><![CDATA[The primary objective of this project was to provide an integrated set of quantitative tools that provide a structured approach to evaluating the latent risk in vintage polyethylene pipes, such as Aldyl-A, that are common in gas distribution systems. Secondary objectives were: first, to provide a fitness for service approach that can support replacement prioritization; second, to utilize data from multiple sources such as in ditch condition assessment and leak records; third, to provide a means to access the pipe in a congested urban environment. The insights developed from this body of work were not available to regulators and operators prior to this work, which has provided a structured set of tools for assessing the fitness for service of vintage polyethylene pipelines in gas distribution systems. The models developed in this project are comprehensive probabilistic risk models that can be fully integrated into enterprise decision support systems and used to prioritize replacement programs, provide system integrity reports and assist operators in identifying future integrity related problems in their systems. The prototype endoscopic tools have the potential to fundamentally change how integrity data can be gathered to feed improved risk models for vintage pipeline systems. A detailed list of potential follow-on work is provided in the body of the report.]]></description>
      <pubDate>Mon, 01 Jan 2018 17:22:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1492883</guid>
    </item>
    <item>
      <title>Risk based probabilistic pavement deterioration prediction models for low volume roads</title>
      <link>https://trid.trb.org/View/1490065</link>
      <description><![CDATA[Accurate prediction of pavement performance is important for efficient management of road infrastructure. Pavement performance prediction models developed for low-volume roads are mainly based on deterministic approach. The deterministic prediction models are inadequate to completely capture the deterioration mechanism. Uncertainties may occur in pavement behaviour under changing traffic loads and environment conditions, which may not be realistically represented by deterministic model. The objective of this paper is to develop pavement deterioration prediction models by probabilistic approach, for various distresses observed on low-volume roads in the state of Kerala in India, with the help of existing deterministic models. The major distresses observed on low-volume roads were ravelling, pothole and edge failure. Load-associated distresses were rarely observed on these roads as the maximum cumulative standard axle observed was only one million standard axle (msa). Hence, lack of proper drainage and construction quality (CQ) could be attributed as the major reasons for the pavement deterioration. Progression of deterioration of pavements with age has been studied and the intensity of distresses along with corresponding probabilities was arrived at. The distresses predicted by probabilistic models were compared with those predicted by deterministic models and the actual distress values observed in the field. The prediction models were validated using Mean Absolute Percentage Error, a statistical method for accuracy measurement of forecasting models. A risk analysis was then conducted to select the critical percentile value for each type of distress corresponding to varying pavement age. A sensitivity analysis was also carried out to study the effect of pavement age and CQ on the progression of pavement deterioration.]]></description>
      <pubDate>Mon, 11 Dec 2017 20:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1490065</guid>
    </item>
    <item>
      <title>Signal Timing and Coordination Strategies Under Varying Traffic Demands</title>
      <link>https://trid.trb.org/View/1488755</link>
      <description><![CDATA[Current practice for signal timing and signal coordination is to develop and operate a limited number of predetermined time-of-day plans. Coordination plans are commonly developed for and based on weekday morning, mid-day, evening, and weekend peak periods. During the remaining time periods signals are operating either in fully/semi-actuated modes or fixed modes. Engineers often face a dilemma to decide when signals should be coordinated. Numerous studies have been conducted to develop practical guidelines by considering volume, signal spacing, platoon dispersion, and signal timing parameters. Although some guidelines recognize the need of coordination when traffic demand is high, none of them explicitly quantifies the term “high”. Therefore, most decisions are still made based on engineering judgment. This project aims at developing more quantitative guidelines. One of the most significant benefits of signal coordination is the reduction of number of stops which has received less attention in previous studies. For instance, none of the developed guidelines define stop thresholds when determining signal coordination. This research specifically focuses on this aspect. First, a probabilistic model that predicts the expected number of stops on non-coordinated arterials was developed. The model is a function of the effective green to the cycle length (𝑔/𝐶) ratio that considers the effect of traffic demand and capacity indirectly. It is also assumed that vehicle arrival is random which deems reasonable when demand is low in actuated operational mode. The model was validated through VISSIM simulation of a real signalized arterial, Sparks Blvd. in Sparks, Nevada. The results confirmed that the model was highly reliable in estimating the number of stops. Based on the probabilistic model, a practical signal timing guideline was developed according to the percentage of stops on non-coordinated arterials. The guideline states that: 1) If the percentage of stops along a non-coordinated arterial exceeds 50, coordination is recommended for the arterial; 2) If the percentage of stops along a non-coordinated arterial is 20 or lower, an actuated operation is recommended for the arterial; and 3) If the percentage of stops falls between 20 and 50, an engineering judgment should be applied to decide whether to coordination is necessary. Finally, a case study was conducted to demonstrate the application of the guideline. The same Sparks Blvd. arterial was used in the case study but with various traffic demand conditions. The case study confirmed the practicality of the guideline.]]></description>
      <pubDate>Mon, 27 Nov 2017 16:35:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1488755</guid>
    </item>
    <item>
      <title>Visualisation of uncertainty in probabilistic traffic models for policy and operations</title>
      <link>https://trid.trb.org/View/1471167</link>
      <description><![CDATA[This study investigates different methods to visualise uncertainty in static representations of probabilistic traffic models predictions on road-networks. Although various graphical cues may be used to represent uncertainty it is not a priori clear which of them are most suited for this purpose, since their legibility, intelligibility and the degree to which they interfere with other graphical elements in a representation differ widely. Several graphical uncertainty representations were therefore developed and analysed in expert sessions. A selection of the initial set of uncertainty visualisations was further evaluated in a cognitive alternative task-switching experiment. The results show that graphical representations are able to convey uncertainty information relatively accurately, while some uncertainty visualisations outperform others. It depends on the model and scenario which representation is most suited for a given application. This paper presents an overview of possible graphic uncertainty representations and the considerations involved when applying them to uncertainty in traffic model visualisations.]]></description>
      <pubDate>Mon, 17 Jul 2017 09:33:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1471167</guid>
    </item>
    <item>
      <title>Probabilistic Model of Capacity at Signalized Intersection with a Left-Turn Short Lane</title>
      <link>https://trid.trb.org/View/1438624</link>
      <description><![CDATA[It is common to add the number of approaches at intersections to increase the capacity. The added turning lanes are limited by the road configuration, and often exist in the form of a short lane. The paper presents a probabilistic model to estimate the capacity of intersections for a full through lane and a short left-turn lane. The proposed model overcomes one of the major shortcomings of current estimation methodologies by considering not only stochastic arrivals of vehicles, but also the signal timing scheme. The proposed capacity model shows that the capacities of left-turn and through movements are related to the proportion of the left-turn traffic, the length of the short lane, and the effective green time of both left-turn and through signal phases. It is also found that the left-turn capacity increases with the increase of the proportion of the left-turn traffic and the length of the short-lane, while the through capacity declines with an increase in the proportion of the left-turn traffic, but increases with an increase in the length of the short lane. The proposed model was verified using the microscopic traffic software VISSIM. The results show that the capacities obtained by theoretical models are consistent with the simulated results. The capacity models can provide theoretical suggestions for the design of short lanes and signal timing.]]></description>
      <pubDate>Sat, 01 Apr 2017 22:45:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1438624</guid>
    </item>
    <item>
      <title>A probabilistic model of pedestrian crossing behavior at signalized intersections for connected vehicles</title>
      <link>https://trid.trb.org/View/1424412</link>
      <description><![CDATA[Active safety systems which assess highly dynamic traffic situations including pedestrians are required with growing demands in autonomous driving and Connected Vehicles. In this paper, the authors focus on one of the most hazardous traffic situations: the possible collision between a pedestrian and a turning vehicle at signalized intersections. This paper presents a probabilistic model of pedestrian behavior to signalized crosswalks. In order to model the behavior of pedestrian, the authors take not only pedestrian physical states but also contextual information into account. The authors propose a model based on the Dynamic Bayesian Network which integrates relationships among the intersection context information and the pedestrian behavior in the same way as a human. The particle filter is used to estimate the pedestrian states, including position, crossing decision and motion type. Experimental evaluation using real traffic data shows that this model is able to recognize the pedestrian crossing decision in a few seconds from the traffic signal and pedestrian position information. This information is assumed to be obtained with the development of Connected Vehicle.]]></description>
      <pubDate>Fri, 21 Oct 2016 16:32:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/1424412</guid>
    </item>
    <item>
      <title>Probabilistic Modelling of Bridge Safety Based on Damage Indicators</title>
      <link>https://trid.trb.org/View/1421093</link>
      <description><![CDATA[This paper introduces the various aspects of bridge safety models. It combines the different models of load and resistance involving both deterministic and stochastic variables. The actual safety, i.e. the probability of failure, is calculated using Monte Carlo simulation and accounting for localized damage of the bridge. A possible damage indicator is also presented in the paper and the usefulness of updating the developed bridge safety model, with regards to the damage indicator, is examined.]]></description>
      <pubDate>Sun, 16 Oct 2016 16:27:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1421093</guid>
    </item>
    <item>
      <title>Risk Assessment of a Tunnel Collapse in a Mountain Tunnel Based on the Attribute Synthetic Evaluation System</title>
      <link>https://trid.trb.org/View/1417222</link>
      <description><![CDATA[Collapse is one of the common disasters in tunnel construction. In order to ensure the construction safety of mountain tunnels, the attribute recognition model of risk assessment for tunnel collapse is established based on the attribute mathematics theory. By a comprehensive analysis of the influencing factors of a tunnel collapse, the surrounding rock grade, tunnel depth, angle of unsymmetrical pressure, rock mass integrity, earthquake, and the excavation span are selected as the indexes of the risk assessment attribute measurement functions. These were rigorously constructed to compute attribute measurement of single index and synthetic attribute measurement. The identification and classification of risk assessment of mountain tunnel samples were recognized by the confidence criterion. An exemplification study of a tunnel showed that the assessment results, obtained through attribute measurement analysis and the date of geological forecast agreed well, with the tunnel construction. This risk assessment methodology provides a powerful tool for systematically assessing the risk of tunnel collapse.]]></description>
      <pubDate>Mon, 29 Aug 2016 11:14:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1417222</guid>
    </item>
    <item>
      <title>Probabilistic speed–density relationship for pedestrian traffic</title>
      <link>https://trid.trb.org/View/1411072</link>
      <description><![CDATA[The authors propose a probabilistic modeling approach to represent the speed–density relationship of pedestrian traffic. The approach is data-driven, and it is motivated by the presence of high scatter in the raw data that the authors have analyzed. The authors show the validity of the proposed approach, and its superiority compared to deterministic approaches from the literature using a dataset collected from a real scene and another from a controlled experiment.]]></description>
      <pubDate>Wed, 27 Jul 2016 09:49:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/1411072</guid>
    </item>
    <item>
      <title>Model-Based Derivation of Perception Accuracy Requirements for Vehicle Localization in Urban Environments</title>
      <link>https://trid.trb.org/View/1406256</link>
      <description><![CDATA[In this contribution, the authors address the model-based derivation of perception requirements based on upper bounds on vehicle localization uncertainty for urban driver assistance (UDA) and urban automated driving (UAD). The authors show that a probabilistic model for the estimation of map-relative localization accuracy can be obtained and utilized for proper parametrization of a perception system. Therefore, the paper at hand entails two main contributions: i) Proposal of a probabilistic model for localization accuracy in closed form under the assumption of a generic measurement model with Gaussian noise and a stochastic landmark distribution, ii) Presentation of a framework for model-based derivation of perception requirements which permit desired localization performance. To exemplify the application of their method, sensor parameters for a stereo vision system (e.g. stereo base-width) are determined and verified via comprehensive simulation experiments. This is conducted in the context of an urban automated lane keeping system under explicit consideration of non-existent or occluded lane markings and curb stones.]]></description>
      <pubDate>Wed, 15 Jun 2016 12:05:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1406256</guid>
    </item>
    <item>
      <title>Determination of Bridge Deterioration Models and Bridge User Costs for the NCDOT Bridge Management System</title>
      <link>https://trid.trb.org/View/1405296</link>
      <description><![CDATA[The North Carolina Department of Transportation (NCDOT) currently oversees the design, construction, operation, maintenance, repair, rehabilitation, and replacement of more than 17,000 bridges. As funding to match the growing need for new infrastructure and for maintenance, repair, and rehabilitation (MR&R) of existing infrastructure becomes more difficult to obtain, maximizing the service life of existing bridges becomes increasingly critical. In support of data-driven planning, NCDOT’s bridge management system (BMS) stores inventory data, including bridge characteristics, inspection data, and rating information, and uses deterioration models and economic models to predict outcomes and to provide network-level and project-level decisions. The objectives of this project were to provide NCDOT with revised, updated deterioration models and user cost tables for use in the BMS software. Existing data in NCDOT’s BMS were reviewed and steps to address data anomalies were identified and implemented. Updated deterministic deterioration models were developed for the existing data on the family level, with components grouped into families using established a priori classifications. Additionally, a unique statistical regression methodology applying survival analysis techniques to better address characteristics of the historical condition rating data was developed and resulted in probabilistic deterioration models for bridge components and culverts that provide significantly improved predictive accuracy and precision over prior deterministic models. These models include transition probability matrices that account for the effects of design, geographic, and functional characteristics on deterioration rates over different condition ratings. These models were found to provide significantly improved prediction accuracy and precision over typical planning horizons used in network analysis. However, while this advanced model was found to best fit the historical condition rating data and provide unique insight on factors influencing deterioration over the life-cycle of each bridge component, it was also discovered that a simplified implementation of the probabilistic deterioration model was able to achieve similar performance without rigorously incorporating the effects of external factors on deterioration rates. To aid in implementation and technology transfer, a software application was developed to facilitate routine updating of both the deterministic and probabilistic deterioration models. Preliminary work to evaluate the relative impact of individual maintenance activities on element condition ratings was performed, including the development of histograms of condition rating changes from prior actions to aid in development of action effectiveness models. Inputs and methodologies utilized to compute user costs in NCDOT’s BMS were updated and enhanced using relevant, current resources that were locally or regionally sourced when possible. Specifically, the updates and enhancements to the user cost models address average daily traffic (ADT) growth rates, vehicle operating cost, vehicle distribution, vehicle weight distribution, vehicle height distribution, accident injury severity, accident cost, and an equation useful in forecasting the number of annual bridge-related crashes. Analysis performed to generate the bridge-related crash prediction equation resulted in the identification of seven bridge characteristics that are most associated with bridge-related crashes. A sensitivity analysis on user costs indicated that, in NCDOT’s BMS, user costs are most sensitive to accident costs.]]></description>
      <pubDate>Sun, 08 May 2016 19:24:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1405296</guid>
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