<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?tc=NN%3AXbkjc%2A" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. 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>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Resource Assessment Tool for Effective Unmanned-Aerial-Vehicle-Assisted Bridge Inspections</title><link>http://pubsindex.trb.org/view/2414228</link><description><![CDATA[It is critically important to plan properly for integrating and deploying unmanned aerial vehicles (UAVs) in the bridge inspection process, there is a need for tools to support implementation and decision-making regarding the use of UAVs at specific structures. In this study, a resource estimation tool that can be used to estimate the resources required for UAV-assisted bridge inspections is developed. The tool can aid inspectors in determining the estimated flight time and resources required for using a specific UAV and operator during the inspection of a specific bridge. The tool supports the development of optimal flight paths based on the structural geometry and positioning of structural elements of a bridge, establishes a range of recommended flight speeds for conducting reliable UAV-assisted bridge inspections based on the skill level(s) of the pilot(s) who were involved in conducting inspections. The developed tool also establishes a recommended range of wind speed and the corresponding standoff clearance information for safely conducting UAV-assisted bridge inspections. The tool also provides an estimated number of batteries required to allow the estimated required flight time. In this paper, the development of the tool is described, and the advantages of the tool are illustrated by its application in a case study involving a 10-span steel continuous multi-beam bridge with a reinforced concrete deck. The tool is developed as a spreadsheet and is publicly available through a GitHub page, accessible at https://github.com/ACCESSLab/Resource-Assessment-Tool-for-Effective-UAV-Assisted-Bridge-Inspection.]]></description><pubDate>Mon, 12 Aug 2024 08:51:40 GMT</pubDate><guid>http://pubsindex.trb.org/view/2414228</guid></item><item><title>User’s Guide for Quantifying the Effects of Vehicle Mix on Crash Frequency and Crash Severity</title><link>http://pubsindex.trb.org/view/2339964</link><description><![CDATA[The first edition of the Highway Safety Manual (HSM) has provided methods and procedures in estimating total crashes, crashes by type, and crashes by severity at the site level, project level, and corridor level. The development of HSM in 2010 provided a compendium of practically deployable quantitative safety methods for adoption by practitioners at various agencies including states, counties, and metropolitan planning organizations. Broadly, the quantitative model components can be classified as safety performance functions (SPFs), severity distribution functions (SDFs), SPF adjustment factors (AFs), and crash modification factors (CMFs). These four model components currently do not accommodate the influence of vehicle mix, a factor shown to be valuable for explaining both crash frequency and severity. Recent research efforts have shown that heavy vehicle traffic and vehicle mix have a substantial impact on crash frequency and severity. These studies indicate that the consideration of vehicle mix would improve predictive methods for crash frequency and severity. Improved methods will result in better use of the limited funds and resources available for improving the safety of the highway system and supporting performance-based approaches. To examine how vehicle mix data can influence the model infrastructure in HSM, this project titled “The Effect of Vehicle Mix on Crash Frequency and Crash Severity, NCHRP 22-49” sets the following objectives: Develop and validate a statistically valid predictive methodology to quantify the effect of vehicle mix on crash frequency and severity for various facility types; and Develop a spreadsheet tool for practitioners to quantify the effect of vehicle mix on safety performance across the range of highway activities including planning, design, operations, and safety management.]]></description><pubDate>Sun, 25 Feb 2024 17:13:35 GMT</pubDate><guid>http://pubsindex.trb.org/view/2339964</guid></item><item><title>Maximizing Proceeds from the Fleet Asset Disposal Sales Process</title><link>http://pubsindex.trb.org/view/2271402</link><description><![CDATA[This report describes approaches to selling surplus vehicles and equipment that can increase realized prices and net returns to a state department of transportation (DOT). Accompanying the report is a spreadsheet-based tool for comparing potential returns from various sales channels. The report and accompanying resources will be of interest to fleet managers and others responsible for decisions about the disposal of fleet assets.]]></description><pubDate>Sat, 21 Oct 2023 16:10:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2271402</guid></item><item><title>Right-Turn-on-Red Site Considerations and Capacity Analysis: Practitioner's Guide</title><link>http://pubsindex.trb.org/view/2204412</link><description><![CDATA[Research conducted under this NCHRP Project 03-136 sought to develop improved techniques for estimating the performance of right-turn-on-red (RTOR) movements through two methods: estimating the RTOR volume and estimating RTOR capacity. Both methods primarily use data types that would typically be available to practitioners, such as turning movement counts, basic signal timing data, and basic intersection attributes. The resulting RTOR volume estimates can be used with existing implementations of the HCM methodology by adjusting the input volume for right-turn movements, while the alternative capacity expression requires adjustment of the capacity calculation method internal to signal analysis models. Model 1B uses a statistical model form that accounts for the distribution of RTOR volumes and uses variables that are more likely to be available from field count data. Therefore, it is the most promising of the model forms for implementation. Additional models are presented that use simpler mathematical forms or fewer variables. Concurrent with this research, the team developed a spreadsheet tool to permit users to enter values corresponding to various scenarios and obtain results from the developed methods. This document is aimed at transportation practitioners and provides guidance on use of the spreadsheet tool as well as information about the developed methods.]]></description><pubDate>Sat, 15 Jul 2023 18:25:32 GMT</pubDate><guid>http://pubsindex.trb.org/view/2204412</guid></item><item><title>Macro-Level Analysis of Safety Planning and Crash Prediction Models: A Guide</title><link>http://pubsindex.trb.org/view/2204421</link><description><![CDATA[This guide provides guidance on how to use a spreadsheet tool developed during NCHRP Project 17- 81, “Proposed Macro-Level Safety Planning Analysis Chapter for the Highway Safety Manual.” The purpose of the spreadsheet tool is to support the testing and implementation of the NCHRP Project 17- 81 macro-level crash prediction models (CPMs). Macro-level CPMs predict an average annual crash frequency, by crash type and severity, for a defined geographic area, such as a block group, Census tract, traffic analysis zone (TAZ), county, or other safety analysis zone (SAZ). The tool implements the NCHRP Project 17-81 macro-level CPMs, which are applicable to Census block groups.]]></description><pubDate>Sun, 09 Jul 2023 18:28:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/2204421</guid></item><item><title>MatFEA: Efficient Finite Element Framework for Analyzing Pavement Structures With Nonlinear Unbound Materials</title><link>http://pubsindex.trb.org/view/2111885</link><description><![CDATA[This paper reports the details of a computationally efficient finite element analysis code (called MatFEA), designed specifically for flexible pavements, where stress-dependent characteristics of unbound layers are considered. The stress-dependent nonlinearity of the unbound materials has been modeled in MatFEA through an iterative approach. MatFEA was mainly developed to improve the Mechanistic-Empirical Asphalt Pavement Analysis (MEAPA) web application with capability of analysis/design of nonlinear pavement structures. A specific-purpose mesh generation algorithm was developed for the MatFEA in which the mesh density was dynamically adjustable based on the expected stress magnitude. To verify the results of the MatFEA, 12,000 nonlinear pavement structures with different structural and loading properties were generated and analyzed with MatFEA. The same pavement structures were modeled in a general-purpose FE-based ABAQUS™ program. The results of this study showed that the critical pavement structural responses from the MatFEA were almost identical with those modeled by ABAQUS™ program. The average runtime of MatFEA for analyzing each pavement structure was about 1.13s. Finally, it was concluded that application of the dynamically adjustable mesh generation algorithm in the MatFEA helped achieve a reasonably high accuracy as well as efficient runtime, which brings the advantage of implementing the MatFEA as a pavement structural response model in the mechanistic-empirical pavement analysis/design procedures.]]></description><pubDate>Tue, 07 Feb 2023 18:34:30 GMT</pubDate><guid>http://pubsindex.trb.org/view/2111885</guid></item><item><title>Guide for Roadway Cross Section Reallocation</title><link>http://pubsindex.trb.org/view/2054774</link><description><![CDATA[A process for making community-minded decisions about street design is the focus of this guide. The guide describes how street design decisions impact communities and clarifies how different street elements influence not just transportation outcomes, but livability, economic and environmental health, equity, and many other concerns. The guide includes a framework that offers practitioners a straightforward way to consider all these community goals and choose a street cross section that serves everyone. The guide is accompanied by a spreadsheet tool (Cross Section Decision-Making Tool, Appendix A) that walks practitioners through the decision-making process, incorporating the data and information presented throughout the guide.]]></description><pubDate>Wed, 02 Nov 2022 17:13:02 GMT</pubDate><guid>http://pubsindex.trb.org/view/2054774</guid></item><item><title>Systematic Approach for Determining Construction Contract Time: A Guidebook</title><link>http://pubsindex.trb.org/view/1930537</link><description><![CDATA[This report provides state departments of transportation (DOTs) guidance for producing consistently credible, reliable, and defensible contract time estimates. This guidebook also addresses the relationship of contract time to risk management and the post-construction feedback loop for continuous process improvement. The methods and tools discussed in this guide have also been integrated into an accompanying spreadsheet toolkit. These deliverables should be of immediate use to practitioners responsible for effectively developing, maintaining, and applying contract time determination procedures.]]></description><pubDate>Wed, 23 Mar 2022 16:12:25 GMT</pubDate><guid>http://pubsindex.trb.org/view/1930537</guid></item><item><title>Evaluation of an Automatic, Individual Computer-Based Driver Education and Training Program</title><link>http://pubsindex.trb.org/view/1846391</link><description><![CDATA[Driver behavior is the primary contributing factor in the majority of crashes. Thus, safety technologies and programs aimed at reducing or eliminating risky driving behaviors may prevent a large number of crashes. One technology that has been found effective at reducing risky behaviors is an onboard safety monitoring (OSM) system. OSM systems incorporate in-vehicle recording technology that continuously measures and records the driver’s performance. However, data suggest that OSM systems alone are insufficient to create lasting behavioral change. Instead, lasting behavioral change results from using the data from OSM systems to offer individualized driver coaching/training. Predictive Coach, a software program, uses kinematic-based OSM devices to monitor instances of risky driving, which are subsequently communicated to the back-office software and assigned to the driver in the vehicle. Once the maximum number of risky driving behaviors specified by the end-user fleet is reached, Predictive Coach automatically assigns driver training tailored specifically to address the risky behavior identified for that driver. The driver has one week to complete the self-paced training course. After completion, the results are automatically transmitted back to Predictive Coach and the OSM system for tracking and manager follow-up (if needed). Finally, the course results, along with driver identification, risky driving thresholds, and behavioral trends, are made available via the OSM system dashboard and reports. Results from this study showed that the Predictive Coach program was associated with a reduction in bus drivers’ risky driving behaviors, including a 63% reducing in excessive speeding events. It offers fleets an objective method of identifying drivers in need of training, offers targeted training courses based on individual driving habits, and does all of this automatically without the need for fleet intervention. Additionally, the results showed that Predictive Coach program provides a complimentary system to video-telematics OSM systems to help fleets further reduce risky driving.]]></description><pubDate>Tue, 13 Apr 2021 16:42:36 GMT</pubDate><guid>http://pubsindex.trb.org/view/1846391</guid></item><item><title>Forecasting Federal Transportation Performance Management Bridge Condition Measures for Bridge Management</title><link>http://pubsindex.trb.org/view/1759352</link><description><![CDATA[Many common processes of bridge management can benefit from network-level analysis of long-term costs and condition, on a time frame of about 10 years. Such processes include development and implementation of Transportation Asset Management Plans, long-range needs analysis, capital budgeting and programming, and policy analysis. The ability to forecast federal Transportation Performance Management (TPM) condition measures would provide managers with a way of evaluating the possible outcomes of funding, programming, and policy decisions. A model for this purpose has been developed as a part of StruPlan, an open-source spreadsheet for long-range renewal planning for transportation structures. Element condition state data are found to be highly exponential in distribution, while the federal measures “Percent Good” and “Percent Poor” are categorical when applied to specific bridges. Element data, providing more detail about the type, severity, and extent of defects, are valuable for deterioration modeling, while the TPM measures are simpler for reporting to stakeholders. A set of models was developed to bridge the gap between these measures. Thus far, the models have been calibrated and pilot tested using Idaho, South Dakota, and Kentucky data. The model is a novel approach that has not been attempted elsewhere, that may simplify important parts of bridge management and provide some valuable new ideas for researchers and developers.]]></description><pubDate>Thu, 04 Feb 2021 10:57:29 GMT</pubDate><guid>http://pubsindex.trb.org/view/1759352</guid></item><item><title>Designing and Implementing Dynamic Modulus Models of Asphalt Mixtures</title><link>http://pubsindex.trb.org/view/1759241</link><description><![CDATA[Regression and machine learning-based |E*| models have previously been proposed for use in asphalt design procedures. Regression models include the linear, interactions linear, stepwise linear, robust linear, Hirsch, revised Hirsch, Al-Khateeb 1&amp;2, NCHRP 1-40D, simplified global, and Bari-Witczak models. The advantage of these models is that the output of the regression is a closed-form equation which is relatively easy to implement. However, all the aforementioned models showed a significant bias in prediction when the dynamic modulus database was large and included unique mixtures such as those containing RAP. There was not one regression model that produced an R²&gt;0.9 on testing on such a database. To address this issue, several machine learning-based |E*| models were developed using the following algorithms: genetic expression programming (GEP), regression trees, SVMs, GPRs, ensembles of trees, ANFIS, and artificial neural networks (ANNs). Generally, machine learning-based models had a better performance than regression models, especially considering that a significant portion of the test database included mixtures containing RAP. The issue to date with machine learning models is that to most engineers, they appear to be a black box with no ability to create practical equations or be implemented in a spreadsheet. In this paper, a step-by-step process is shown to allow a practicing engineer to directly implement a complicated ANN model using a spreadsheet software.]]></description><pubDate>Thu, 04 Feb 2021 10:57:25 GMT</pubDate><guid>http://pubsindex.trb.org/view/1759241</guid></item><item><title>Guidance for Calculating the Return on Investment in Transit State of Good Repair</title><link>http://pubsindex.trb.org/view/1665777</link><description><![CDATA[This report provides guidance and a spreadsheet tool for calculating the return on investment (ROI) for a specific investment or program of investments to achieve and maintain transit assets in a state of good repair (SGR). The results of this research are intended to be immediately applicable for individuals at transit agencies, in particular transit staff involved with long-range planning and capital programming.]]></description><pubDate>Mon, 11 Nov 2019 17:17:23 GMT</pubDate><guid>http://pubsindex.trb.org/view/1665777</guid></item><item><title>Incorporating Product Choice into Transit Fare Policy Scenario Models</title><link>http://pubsindex.trb.org/view/1593485</link><description><![CDATA[Customer fare product choices can affect both ridership and revenue, so they are strategically important for transit agencies. Nearly all major agencies offer choices between pay-per-use and pass products, and with each potential fare change, agencies face decisions about whether to modify pass “multiples”—the number of rides needed to “break even” on a pass purchase. However, the simple elasticity spreadsheet models often used to analyze the potential ridership and revenue impacts of fare changes make little or no adjustment for shifts in fare product choices. This paper reviews different options for incorporating product choice into fare policy scenario models, and it presents a ridership and revenue prediction procedure that combines a multinomial logit fare product choice model with the logic of an elasticity spreadsheet model. This combination facilitates evaluation of complex fare changes that are likely to alter fare product market shares while maintaining much of the flexibility and simplicity of a traditional spreadsheet model. Additionally, the proposed model uses only preexisting, revealed-preference automated fare collection data rather than requiring customer surveys. The proposed model is demonstrated using examples at the Chicago Transit Authority (CTA). The CTA experienced a large shift from passes to pay-per-use following a fare change in 2013, illustrating the potential value of accounting for fare product choices in fare scenario evaluation.]]></description><pubDate>Mon, 22 Apr 2019 16:06:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/1593485</guid></item><item><title>A Computer Program for Top-Down Cracking of Asphalt Pavement Layers Under Thermal Loading</title><link>http://pubsindex.trb.org/view/1495740</link><description><![CDATA[Top-down cracking is primarily fatigue cracking and exists in asphalt pavement layers that initiates at the surface of the pavement and propagates downward through the asphalt layer. Top-down cracking is affected by multiple factors including pavement materials, pavement structures, heavy traffic, and the climate. The “thermal loading”, also called temperature variation, is primarily considered for top-down cracking in this paper. The paper develops a mechanistic-empirical approach to predict the top-down cracking under thermal loading in the asphalt pavement layer. This proposed mechanistic-empirical method is based on the modeling of pavement temperature, thermal stress and Artificial Neural Network (ANN) of thermal J-integral and crack growth. The climate data used in our model including hourly solar radiation, daily air temperature, and wind speed are collected from the Long-Term Pavement Performance (LTPP) database and National Climate Data Center (NCDC) database for development of the pavement temperature model. The viscoelastic thermal stress model has been developed by the finite difference solution to the viscoelastic constitutive equation based on Boltzmann’s Superposition Principle using the Prony series representation of relaxation modulus. The prediction of top-down cracking in asphalt pavement layer is established by using Paris law and computed by programming in the C# language. Its fracture coefficients are determined by pavement materials and the thermal J-integral is determined by results from the ANN model. Aging of asphalt mixture is taken into consideration for prediction of the thermal J-integral in this study.]]></description><pubDate>Wed, 28 Feb 2018 09:26:11 GMT</pubDate><guid>http://pubsindex.trb.org/view/1495740</guid></item><item><title>Choosing Safe, Cost-Effective Intersection Designs</title><link>http://pubsindex.trb.org/view/1479679</link><description><![CDATA[This issue explores the Life-Cycle Cost Estimation Tool (LCCET), a spreadsheet-based tool for comparing the life-cycle costs of alternative designs for new and existing intersections. Designed for flexibility, the tool allows engineers to conduct analyses across different system configurations and consider a wide range of impacts. Conducting these analyses helps agencies identify locations where alternatives to traffic signals may be safer and less costly while also improving traffic flow. The tool is part of the NCHRP Web-Only Document 220: Estimating the Life-Cycle Cost of Intersection Designs.]]></description><pubDate>Wed, 09 Aug 2017 11:10:42 GMT</pubDate><guid>http://pubsindex.trb.org/view/1479679</guid></item></channel></rss>