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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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      <title>Characterizing performance resilience of transportation networks against hurricane events</title>
      <link>https://trid.trb.org/View/2613747</link>
      <description><![CDATA[Extreme weather events are posing significant challenges to transportation infrastructure networks, both physically and functionally. While previous studies have examined the performance of infrastructure networks against disruptions, rare research integrates segment-level performance metrics, such as traffic volume and speed, to evaluate spatiotemporal operational responses to climate-disruptive events, like hurricanes. This study highlighted multiple traffic segments in transportation networks and investigated their geospatial changes in average traffic volume and median traffic speed before, during, and after hurricanes to quantify segment-level volume and speed resilience. Analyzing highway networks’ traffic and hurricane data from Miami-Dade County, Florida, we revealed four-quadrant performance resilience patterns, including (1) negative volume, positive speed (80 % of the highway networks); (2) both negative (17 %); (3) both positive (0.6 %); and (4) positive volume, negative speed (2.4 %). Volume resilience ranged within −0.04∼0.001 and speed resilience within −0.3∼0.3, indicating volume changes of <4 % of highway capacity and speed changes of <30 % of speed limits during hurricanes. A Bayesian Additive Regression Trees (BART) model identified highway type, lane direction, demographics, and land use as crucial factors influencing resilience. Highways near densely populated neighborhoods with fewer White residents and more diverse land uses exhibited lower volume but higher speed resilience, suggesting racial disparity. These findings offer valuable insights into network design and adaptation planning strategies to enhance transportation resilience and mitigate the impacts of climatic disruptions on network performance.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613747</guid>
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    <item>
      <title>Accessibility of methadone treatment via public transit for syringe services program participants in Miami-Dade County, Florida</title>
      <link>https://trid.trb.org/View/2590585</link>
      <description><![CDATA[Methadone is an opioid receptor agonist medication used in the treatment of opioid use disorder (OUD). Geographic distance to opioid treatment programs (OTPs) is a major barrier to treatment, given requirements for direct observation of dosing and periodic drug screens, and 'methadone treatment deserts' are defined as a public transit threshold of 30 min. The purpose of this study was to examine public transit access to methadone treatment for participants of a syringe services program (SSP) in Miami-Dade County, Florida. Public transit times were calculated using the R library r5r, which facilitates multi-modal transportation network routing. General Transit Feed Specification data was combined with street network data from OpenStreetMap for Miami-Dade County. Transit times were estimated from the population-weighted centroid of each zip code (n = 73) with participants of Miami's only SSP (n = 1549) to the nearest OTP (n = 4) using 24 departure windows aligned with OTP service hours. The mean one-way transit time from zip codes with SSP participants in Miami-Dade County to the nearest OTP was 79 min. Over 95 % of SSP participants in Miami-Dade County have a mean one-way transit time >30 min, classifying them as residing in 'methadone treatment deserts.' Likewise, 69 of the 73 (95 %) zip codes with SSP participants have a mean transit time to the closest OTP >30 min. Transit times differ substantially between zip codes with different numbers of SSP participants, but not between departure windows. Geographic isolation of methadone treatment from public transit routes represents a significant barrier to equitable OUD treatment.]]></description>
      <pubDate>Wed, 17 Sep 2025 10:55:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2590585</guid>
    </item>
    <item>
      <title>Computational Results For: Automated Knowledge Graphs for Complex Systems (AutoGraCS): Applications to Management of Bridge Networks [supporting dataset]</title>
      <link>https://trid.trb.org/View/2543157</link>
      <description><![CDATA[Abstract of the final report is stated below for reference: With the ability to harness the power of big data, the digital twin (DT) technology has been increasingly applied to the modeling and management of structures and infrastructure systems, such as buildings, bridges, and power distribution systems. Supporting these applications, an important family of methods are based on graphs. For DT applications in modeling and managing smart cities, a large-scale knowledge graph (KG) is needed to represent the complex relationships and model the urban infrastructure as a system of systems. To this end, this paper develops a conceptual framework Automated knowledge Graphs for Complex Systems (AutoGraCS). In contrast to existing KGs developed for DTs, AutoGraCS can support KGs accounting for statistical correlations and interdependencies within the complex systems. The established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling and Bayesian analysis. Besides, AutoGraCS provides flexibility in support of users’ need to implement the ontology and rules when constructing the KG. With the user-defined ontology and rules, AutoGraCS can automatically generate a KG to represent a complex system consisting of multiple systems. The bridge network in Miami-Dade County is used as an illustrative example to generate a KG that integrates multiple layers of data from the bridge network, traffic monitoring facilities, and flood water watch stations.]]></description>
      <pubDate>Tue, 19 Aug 2025 16:34:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543157</guid>
    </item>
    <item>
      <title>Evaluation of Cost-Effective Shoaling Reduction Alternatives for Navigation Channels near Inlets</title>
      <link>https://trid.trb.org/View/2559448</link>
      <description><![CDATA[This study developed and applied integrated MIKE21 Flexible Mesh (FM) hydrodynamic (HD), wave (SW), sediment transport and morphology (ST), and particle tracking (PT) models to analyze the effect of sediment impoundment basins and Intracoastal Waterway (IWW) channel rerouting (near Baker’s Haulover Inlet in Miami-Dade County, Florida) to reduce shoaling and reduce IWW channel maintenance dredging costs. For computational efficiency, the study used a month-long representative period from a representative year to estimate shoaling rates and then prorated the computed shoaling rates for an average year. The prorated shoaling rate offers a good approximation of the shoaling rates of channel shoaling reduction alternatives relative to the shoaling rate for baseline (existing) conditions. Model results show that the best-performing shoaling reduction alternative requires maintenance dredging once every 8 years and provides a substantial annualized cost savings of $249,000 when compared with the baseline dredging frequency of once every 4 years.]]></description>
      <pubDate>Thu, 26 Jun 2025 11:43:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559448</guid>
    </item>
    <item>
      <title>Strengthening Infrastructure Resilience to Hurricanes by Modeling Transportation and Electric Power Network Interdependencies</title>
      <link>https://trid.trb.org/View/2547973</link>
      <description><![CDATA[Community resilience is significantly affected by infrastructure disruptions during hurricanes. Resilience is generally defined as the ability of a system to manage shocks and return to a normal state in response to an extreme event. Due to the interconnected and interdependent relationships among infrastructure systems, the restoration process of a system is further delayed when other systems are disrupted. This study presents an agent-based model (ABM) developed to simulate the resilience of infrastructures to hurricanes, focusing on the interdependencies between electric power and transportation networks. To study infrastructure resilience to a hurricane, a library of agents has been created including electric power networks, transportation networks, wind/flooding hazards, and household agents. The ABM is applied to the households in ZIP Code 33147 of Miami-Dade County, Florida, and the infrastructures supporting these households. Interdependencies between the two networks are modeled in two ways, representing the (1) role of transportation in fuel delivery to power plants and restoration teams’ access to failed power system components and the (2) impact of power outage on transportation network components. The authors simulate three restoration strategies: component-based, distance-based, and traffic light-based restoration. The model is validated against Hurricane Irma data, showing consistent behavior with varying hazard intensities. Scenario analyses reveal the impact of restoration strategies, road accessibility, and wind speed on power service restoration. Results demonstrate that a traffic-light-based restoration strategy efficiently prioritizes signal recovery without delaying household power restoration time. Restoration of power services will be faster if fuel transportation to power plants and restoration efforts are not delayed by inaccessible roads due to flooding. The developed ABM can be used as a decision support tool by policymakers and utility/emergency managers in evaluating power outage restoration strategies using available resources.]]></description>
      <pubDate>Mon, 23 Jun 2025 16:00:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2547973</guid>
    </item>
    <item>
      <title>Computational Results For: Automated Knowledge Graphs for Complex Systems (AutoGraCS): Applications to Management of Bridge Networks [supporting dataset]</title>
      <link>https://trid.trb.org/View/2566907</link>
      <description><![CDATA[Abstract of the final report is stated below for reference: With the ability to harness the power of big data, the digital twin (DT) technology has been increasingly applied to the modeling and management of structures and infrastructure systems, such as buildings, bridges, and power distribution systems. Supporting these applications, an important family of methods are based on graphs. For DT applications in modeling and managing smart cities, a large-scale knowledge graph (KG) is needed to represent the complex relationships and model the urban infrastructure as a system of systems. To this end, this paper develops a conceptual framework Automated knowledge Graphs for Complex Systems (AutoGraCS). In contrast to existing KGs developed for DTs, AutoGraCS can support KGs accounting for statistical correlations and interdependencies within the complex systems. The established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling and Bayesian analysis. Besides, AutoGraCS provides flexibility in support of users’ need to implement the ontology and rules when constructing the KG. With the user-defined ontology and rules, AutoGraCS can automatically generate a KG to represent a complex system consisting of multiple systems. The bridge network in Miami-Dade County is used as an illustrative example to generate a KG that integrates multiple layers of data from the bridge network, traffic monitoring facilities, and flood water watch stations.]]></description>
      <pubDate>Mon, 23 Jun 2025 08:44:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2566907</guid>
    </item>
    <item>
      <title>Automated Knowledge Graphs for Life-cycle Management of Coastal Bridge Networks</title>
      <link>https://trid.trb.org/View/2543156</link>
      <description><![CDATA[With the ability to harness the power of big data, the digital twin (DT) technology has been increasingly applied to the modeling and management of structures and infrastructure systems, such as buildings, bridges, and power distribution systems. Supporting these applications, an important family of methods are based on graphs. For DT applications in modeling and managing smart cities, a large-scale knowledge graph (KG) is needed to represent the complex relationships and model the urban infrastructure as a system of systems. To this end, this paper develops a conceptual framework Automated knowledge Graphs for Complex Systems (AutoGraCS). In contrast to existing KGs developed for DTs, AutoGraCS can support KGs accounting for statistical correlations and interdependencies within the complex systems. The established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling and Bayesian analysis. Besides, AutoGraCS provides flexibility in support of users’ need to implement the ontology and rules when constructing the KG. With the user-defined ontology and rules, AutoGraCS can automatically generate a KG to represent a complex system consisting of multiple systems. The bridge network in Miami-Dade County is used as an illustrative example to generate a KG that integrates multiple layers of data from the bridge network, traffic monitoring facilities, and flood water watch stations.]]></description>
      <pubDate>Wed, 21 May 2025 08:54:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543156</guid>
    </item>
    <item>
      <title>Integrating multi-source data for life-cycle risk assessment of bridge networks: a system digital twin framework</title>
      <link>https://trid.trb.org/View/2516932</link>
      <description><![CDATA[Bridges are critical infrastructure assets that face a variety of stressors throughout their service life, requiring a life-cycle approach to assess their risk profile. Recent advancements in sensing and monitoring technologies provide a powerful data foundation to improve the accuracy of life-cycle risk assessment (LCRA). However, existing works that incorporate data for probabilistic risk assessment typically focus on individual bridges and rely on single-source data, limiting their scope and applicability. To this end, a system digital twin (SDT) framework based on Bayesian network (BN) is proposed to integrate multi-source data for LCRA of bridge networks. Specifically, the SDT can capture correlations and interdependencies across various scales, including within individual components (e.g., multiple failure modes), between components within a system (e.g., bridges along a route), and across interconnected systems (e.g., bridge and hydraulic systems). It integrates data from various sources including bridge inspections, traffic monitoring facilities, and water watch stations. A coastal bridge network in Miami-Dade County, FL, is used as an illustrative example to demonstrate how the SDT integrates multi-source data for risk assessment. Additionally, several future scenarios are hypothesized to showcase the applicability and flexibility of the proposed framework in supporting risk management for infrastructure systems.]]></description>
      <pubDate>Thu, 10 Apr 2025 09:21:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2516932</guid>
    </item>
    <item>
      <title>Integrated Flood and Socio-Environmental Risk Analysis for Prioritizing ABC Activities</title>
      <link>https://trid.trb.org/View/2534033</link>
      <description><![CDATA[The need for accelerated bridge construction (ABC) activities due to flooding (e.g., accelerated bridge upgrade prior to flood events and accelerated bridge repair after flood events) has complex interdependencies with physical, social, and environmental factors in urban areas. The interdependencies get exacerbated in coastal areas because of the pronounced effects of climate change such as extreme weather events and sea level rise impacts on surface and ground waters. Flood related factors can also contribute to bridge scour, the biggest cause of bridge failure in the United States. Due to the limited available budget for accelerated upgrade/repair processes, a comprehensive decision support tool is needed to prioritize bridges in terms of the vulnerability of bridge location and risk level of each bridge to support state Departments of Transportation (DOTs) in project selection. This project presents a spatial, risk-based, multi-criterion decision analysis framework to assign a risk factor to each bridge in the study area based on the vulnerability of urban areas against flood-related, social, and environmental issues, and structural and traffic condition of bridges. The framework is applicable as a decision support tool for prioritizing accelerated rehabilitation projects. As a case study, the developed framework was used for risk-based prioritization of existing bridges in urban areas of the Miami-Dade County, Florida. The proposed decision support tool is simple enough to be used in real projects, yet systematic and structured to be adjusted and implemented in various geographic locations. The tool incorporates social equity and environmental justice into bridge rehabilitation planning process.]]></description>
      <pubDate>Wed, 09 Apr 2025 09:51:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534033</guid>
    </item>
    <item>
      <title>Recent Latino immigrants to Miami-Dade County, Florida: Impaired driving behaviors during the initial years after immigration and the pandemic lockdown</title>
      <link>https://trid.trb.org/View/2445341</link>
      <description><![CDATA[Typically, recent Latino immigrants (RLIs) experience a decline in driving while impaired (DWI) rates soon after immigration, largely due to limited access to vehicles. Such a transitional period offers a window of opportunity for intervention for RLIs at risk of engaging in DWI and riding with an impaired driver (RWID). This manuscript examines the rates of DWI, RWID, and driving while impaired by drugs (DWID) among RLIs upon arrival to Miami/Dade County (MDC), Florida. Collected between 2018 and 2021, data originates from a longitudinal study examining self-reported drinking and driving trajectories among 540 RLIs to MDC. At baseline retrospective pre-immigration data were obtained simultaneously with first-year post-immigration data. Two follow-up surveys conducted one year apart (N=531 and N=522), collect data on RLIs initial 3 years in the United States. Pre- to post-immigration trajectories for mean number of drinks per month (d/m) revealed a “U-shaped” curve: 18.3 d/m, 13.9 d/m, 10.4 d/m, 12.9 d/m, and 16.4 d/m, from pre-immigration (T0), first year (T1), second year before COVID (T2-BC) and during the pandemic lockdown (T2-DC), and third year in the United States (T3). The use of illicit drugs showed a constant decline, from 14.6% at T0 to 2.1% at T3. The prevalence of DWI at T1 was significantly lower compared to rates in the country of origin (T0) and continued declining through T3. DWID rates remained low across the assessment period. RWID was significantly more prevalent than DWI across all study time points. Although the relatively low prevalence of DWI, drug use, and DWID among the RLIs during their initial years in the United States is encouraging, the surge in alcohol use at T3 warns about the need for interventions to prevent increases in DWI. Findings from the present study point to an opportunity to develop early interventions to prevent the escalation of impaired driving among RLIs to MDC.]]></description>
      <pubDate>Fri, 08 Nov 2024 15:49:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2445341</guid>
    </item>
    <item>
      <title>Design and Construction of the S. R. 836 Flyover Bridges</title>
      <link>https://trid.trb.org/View/2209194</link>
      <description><![CDATA[As Miami-Dade County continues its rapid growth west towards the Everglades, local transportation officials find themselves in need of significant capacity improvements to accommodate the increase in vehicular traffic. To address this need, the Miami-Dade Expressway Authority (MDX) has embarked on an expressway expansion program that consists of three design-build contracts. The goal of the expansion program is improved service and heightened aesthetics. This project is the first of the three design-build contracts, is approximately 2.6 miles long and includes the SR 836 Flyover bridges. Within the project limits there are four bridge sites and several innovative sign structures. Three of the bridges in the project are completely new structures employing aesthetic features such as concrete formliners for piers and retaining walls, smooth and flowing structural lines, unusual color schemes, and inlaid tile. The two largest and most innovative bridges in the project. Bridge Nos. 11 and 12, consist of long-span steel box girder superstructures with post-tensioned integral concrete diaphragms, aesthetically enhanced piers founded on spread footing foundations, and spread footing abutments. Prime contractor Condotte America, Inc. teamed with HNTB Corporation for this design-build project. By electing to utilize innovative construction methods and structural elements, the project team won the project with a final bid of $36 million, a construction schedule of 775 days, and a score of 83 on the technical proposal. Four months into the contract, the Owner elected to add a lane to the eastern portion of the project, which increased the contract value to $39 million and added 122 days to the schedule.]]></description>
      <pubDate>Tue, 22 Oct 2024 15:57:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2209194</guid>
    </item>
    <item>
      <title>Equitable Prioritization of Bridge Rehabilitation Projects Using a Spatial Multi-Criteria Decision Support Framework</title>
      <link>https://trid.trb.org/View/2408329</link>
      <description><![CDATA[Transportation infrastructure in the United States faces a significant challenge with more than 40% of bridges surpassing the 50-year mark and 25% requiring rehabilitation, repair, or total replacement. This aging infrastructure, compounded by budget constraints, presents a critical issue that necessitates the prioritization of bridge rehabilitation projects. While traditional prioritization has focused on structural and traffic conditions, the pressing need to address social equity and environmental justice (SEEJ) concerns highlights the importance of a new approach. To address this need, this study aims to incorporate SEEJ considerations into the process of prioritizing bridge rehabilitation projects. To do so, a spatial multi-criteria decision-making (MCDM) method was leveraged to integrate diverse criteria into a coherent bridge rehabilitation prioritization framework. This framework integrates spatial data, encompassing flood-related factors, social aspects, and environmental parameters, with datasets representing structural and traffic conditions of bridges. This amalgamation yields an integrated vulnerability map that assigns vulnerability levels (from very high to very low) to each bridge that can be used as a proxy for prioritization. The practicality of the framework is demonstrated through its application in Miami-Dade County, Florida. The results underscore the importance of SEEJ considerations in bridge rehabilitation to address vulnerabilities, especially within marginalized communities. The proposed framework is adjustable and leverages readily available data, rendering it applicable beyond the study area. The framework can be used as a practical tool by decision makers, such as state Departments of Transportation, to prioritize bridge rehabilitation projects equitably, enhance the resilience of critical infrastructure, and promote inclusive and sustainable communities.]]></description>
      <pubDate>Wed, 31 Jul 2024 10:45:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2408329</guid>
    </item>
    <item>
      <title>Guidelines for Activating Ramp Meters during Off-peak Hours and Weekends</title>
      <link>https://trid.trb.org/View/2389245</link>
      <description><![CDATA[Ramp metering is a Transportation Systems Management and Operations (TSM&O) strategy that utilizes signals installed at freeway on-ramps to dynamically manage traffic entering the freeway. Ramp metering signals (RMSs) are usually activated during peak hours to alleviate recurring congestion. However, recurrent congestion during peak hours constitutes less than half of all congestion. It is the non-recurrent congestion, resulting from traffic incidents, work zones, adverse weather conditions, special events, etc., that adversely impacts the performance of the freeway. The primary goal of this research was to develop specific guidelines to activate ramp meters during off-peak hours and on weekends in response to non-recurring congestion. The analysis was based on a 10-mile section of I95 between Ives Dairy Road and NW 62nd Street in Miami-Dade County, Florida. Real-time traffic data were used to develop the guidelines for activating and deactivating RMSs in response to incidents and adverse weather conditions (i.e., rain) during off-peak hours on weekdays. Since the RMSs are not operational on weekends, a microscopic simulation approach was used to develop the guidelines for activating and deactivating RMSs in response to incidents on weekends. The potential benefits of activating RMSs in response to non-recurring congestion during off-peak hours and on weekends were quantified based on the developed guidelines. Findings suggest that activating the first RMS upstream of the incident location could help improve traffic flow conditions during daytime off-peak periods. During nighttime off-peak periods, results indicated that activating the first RMS upstream and downstream of the incident location could help improve traffic conditions upstream. Findings also suggest that activating the RMSs during daytime and nighttime off-peak periods could help improve traffic flow conditions during rain. During weekends, the results indicated that activation of RMSs in response to incidents increased the average speed and also reduced the average delay of vehicles in the roadway network. The developed guidelines were incorporated into a spreadsheet application designed to automatically determine when to activate or deactivate RMSs during off-peak hours and weekends based on prevailing traffic conditions. Recommendations for the guidelines to be included in the the Florida Department of Transportation (FDOT) District Six Standard Operating Guidelines (SOGs) were also provided. The proposed guidelines will enable the FDOT District Six to use ramp metering to improve traffic operations and safety during off-peak hours and weekends.]]></description>
      <pubDate>Mon, 24 Jun 2024 09:22:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2389245</guid>
    </item>
    <item>
      <title>Recent Latinx immigrants to Miami/Dade County (FL): Travel patterns before, during, and one year after the COVID-19 pandemic lockdown</title>
      <link>https://trid.trb.org/View/2349908</link>
      <description><![CDATA[Understanding the transportation needs of immigrants is crucial for the design and promotion of safe, equitable, and sustainable living environments. This study examines the transportation patterns from a sample of Recent Latinx Immigrants (RLIs) upon arrival to Miami/Dade Co (MDC), Florida. Collected between 2018 and 2021, data came from a longitudinal study examining drinking and driving trajectories among 540 RLIs to MDC. Retrospective pre-immigration data (T0) were obtained simultaneously with the first-year post-immigration data (T1). Follow up surveys were conducted one year later, before (T2-BC) or during a pandemic lockout (T2-DC), and two years later (T3). Descriptive and repeated measures mixed-model regression were used to examine the data. Driving declined from T0 to T1, although remained higher than previously reported for other locations. Not having a valid driver’s license was the main reason for the decline. The initial reduction in driving was paralleled by an increase in the use of transit, riding as passengers in private vehicles, and walking. A year later (T2), as RLIs’ income and access to a driver’s license grew, driving rates increased (even during the pandemic lockdown), while the use of other transportation modes decreased. A year after the pandemic lockdown (T3), driving as well as the use of other transportation modes receded. Reasons for this decline are unclear. RLIs reported elevated driving rates upon their arrival to MDC. The COVID-19 pandemic seems to have altered the RLIs’ transportation patterns, provoking an overall decline in mobility that lasted even after the pandemic lockdown ceased. Transportation planners working on developing safe and equitable transportation systems in MDC should: (1) identify and address barriers to the use of transportation modes other than driving by RLIs; and (2) understand reasons for the broad decline in transportation modes after the pandemic lockdown.]]></description>
      <pubDate>Fri, 05 Apr 2024 09:03:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2349908</guid>
    </item>
    <item>
      <title>Comparison of corridor-level fatal and injury crash models with site-level models for network screening purposes on Florida urban and suburban divided arterials</title>
      <link>https://trid.trb.org/View/2317431</link>
      <description><![CDATA[Develop corridor-level network screening models to identify high-risk corridors where safety improvements could be implemented to reduce fatal and injury (FI) crashes. A novel corridor definition focused on context classification and lane count was developed and applied to urban and suburban four-lane divided arterial roadways in Florida. Negative binomial regression models were developed for multi- and single-vehicle crashes using 80% of the corridors (training set). Crash frequency predictions were obtained from the developed corridor models and similar site-level models from the Highway Safety Manual (HSM) models for the remaining 20% of the corridors (testing set). Results from all models were adjusted using the empirical Bayes (EB) method.   A total of 130 corridors were identified across seven counties. These corridors contained approximately 349 km (217 miles) of roadway and experienced 11,437 multi-vehicle and 746 single-vehicle crashes that resulted in fatalities or injuries from 2017 to 2021. After applying the HSM site-level models and the developed corridor-level models to the testing set (both with and without EB adjustments), the corridor-level models with EB adjustments were the most accurate for corridor crash prediction. Applying the corridor-level models with EB adjustments to the testing set gave a predicted value of 386.44 crashes/year, which was the closest to the observed crash frequency of 383.20 crashes/year. From the corridor-level models, a 3.48-km (2.16-mile) high-risk corridor in Miami-Dade County was identified and analyzed site-by-site using the HSM methodology to identify specific sites within the corridor where safety improvements could provide the most FI crash reductions.   The corridor-level models were more accurate and statistically reliable than similar HSM models while being less data intensive. They also only required corridor-level data rather than data for each intersection and segment. By using readily available data, the methods in this paper can be easily replicated by agencies to develop their own network screening corridor-level models and expedite the identification of corridors in need of safety improvements to reduce FI crashes. Existing site-level network screening methods can be used to supplement the developed corridor-level methodology by identifying high-risk sites within identified high-risk corridors.]]></description>
      <pubDate>Mon, 05 Feb 2024 10:25:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2317431</guid>
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