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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=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" 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>In(equity) in accessibility to low-wage jobs by public transit in the fifteen largest cities in the United States</title>
      <link>https://trid.trb.org/View/2706623</link>
      <description><![CDATA[This study measures in(equity) in accessibility to low-wage jobs by public transit in the fifteen largest population-based cities in the United States. First, it employs a cumulative opportunity-based measure of accessibility to estimate transit accessibility to jobs by vulnerable people at the census tract level. Second, it uses the vertical equity indicator and Gini index to measure and analyze inequity in transit accessibility to low-wage jobs by vulnerable people for four socioeconomic factors: household income, unemployment rate, percentage of immigrants to cities, and percentage of households that spend more than 30% of their income on house rent. The study explores that seven cities offer higher access to low-wage jobs by vulnerable groups compared to their access to all jobs. Except for New York City, Chicago, Houston, and Philadelphia, the vulnerable groups in the rest of the cities experience lower access to low-wage jobs than the general population experiences access to all jobs, suggesting that a low degree of vertical equity exists in 11 cities. New York City, Phoenix, Philadelphia, San Jose, and Chicago exhibit high but reasonable inequity in accessibility to low-wage jobs by public transit, as the average Gini index for each of these cities varies from 0.4 and 0.5, while the other ten cities exhibit severe inequity with Gini index values >0.5. The study facilitates the ability to make direct comparisons among cities regarding the effects of varying levels of accessibility and equity to establish equitable transportation options in the domains of land use and transportation planning.]]></description>
      <pubDate>Mon, 24 Aug 2026 09:03:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706623</guid>
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    <item>
      <title>Measuring nonlinear relationships and spatial heterogeneity of influencing factors on traffic crash density using GeoXAI</title>
      <link>https://trid.trb.org/View/2737149</link>
      <description><![CDATA[Traffic crash density (or frequency) remains a critical public safety concern, posing significant challenges for transportation planning and risk mitigation, particularly in rapidly urbanizing regions. Crash occurrence is closely associated with socio-demographic and roadway determinants, which vary substantially across space. As a result, crash patterns often exhibit pronounced spatial heterogeneity because of socio-spatial disparities and differences in regional conditions, especially at finer spatial scales such as the census tract level. This study applies a Geospatial Explainable Artificial Intelligence (GeoXAI) framework through combining a high-performing machine learning model with GeoShapley to analyze the spatially heterogeneous and nonlinear determinants of traffic crash density in Florida census tract-level. Through comparing the results obtained from GeoShapley framework against other established methods (e.g., SHapley Additive exPlanations (SHAP) and Multiscale Geographically Weighted Regression (MGWR)), GeoXAI framework demonstrates its powerful ability to provide interpretable, tract-level insights into how roadway characteristics and socioeconomic factors contribute to crash risk from the perspectives of nonlinearity and spatial heterogeneity simultaneously. Key variables such as road density, intersection density, neighborhood compactness, and educational attainment exhibit complex nonlinear relationships with crashes. Extremely dense urban areas, such as Miami, show sharply elevated crash risk due to intensified pedestrian activities and roadway complexity. Other major metropolitan areas including Orlando, Tampa, and Jacksonville display significantly higher intrinsic crash contributions, while rural tracts generally have lower baseline risk. Based on these findings, the study proposes targeted, geography-sensitive policy recommendations, including traffic calming in compact neighborhoods, adaptive intersection design, speed management on high-volume corridors such as I-95 in Miami, and equity-focused safety interventions in disadvantaged rural areas of central and northern Florida.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:03:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737149</guid>
    </item>
    <item>
      <title>Understanding Speed, Acceleration, and Critical Gaps at First Rotor Turbo Roundabout in the US</title>
      <link>https://trid.trb.org/View/2647092</link>
      <description><![CDATA[Turbo roundabouts are emerging innovative intersection control designs in the US that enhance traffic safety and operational efficiency. This study investigated driver’s speed choice, acceleration profiles, and behavioral parameters (critical gap and follow-up time) at the first rotor turbo roundabout in the US. Speed profiles revealed that vehicles’ speed at the approach and circulating lanes was affected by approach traffic arrival rates and traffic flow within circulating lanes. In addition, slower speed contributed to no speed-related crashes compared with past speed-related severe crashes before the turbo roundabout installation. Observed critical gaps (4.4–5.8?s) were higher than traditional multi-lane roundabouts (4.5–5.3?s). Follow-up time (3.7–4.7?s) was also higher than traditional multi-lane roundabouts, as drivers merged more cautiously in the circulating lanes owing to the raised lane dividers within the circular lanes. Drivers on the turbo roundabout’s inner lane exhibited longer critical gaps than those on the outer lanes owing to relatively complex merging events on the inner lanes, such as crossing multiple lanes to merge into the inner circulating lane. Merging vehicles’ gap acceptance was affected by the speed of vehicles on circulating lanes, merging vehicle type (e.g., passenger car, truck), and traffic flow rate on circulating lanes. Merging vehicles tended to accept larger gaps owing to the higher speed of vehicles on circulating lanes. As expected, heavy vehicles consistently accepted larger gaps than light vehicles. Conversely, higher traffic flow on circulating lanes caused merging drivers to accept shorter gaps after a longer wait time. Circulating lanes’ flow had a statistically significant effect on critical gaps compared with traditional roundabouts, possibly owing to raised lane dividers and local driving behavior. This study’s findings contribute to understanding the turbo roundabout’s performance in the US and the potential impacts of the turbo roundabout design features on capacity estimation and microsimulation considering US driver behavior.]]></description>
      <pubDate>Wed, 07 Jan 2026 14:58:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647092</guid>
    </item>
    <item>
      <title>Marine Investigation Report: Engine Room Fire aboard Dredging Vessel Stuyvesant, November 2, 2024</title>
      <link>https://trid.trb.org/View/2620729</link>
      <description><![CDATA[On November 2, 2024, about 1435 local time, the dredging vessel Stuyvesant, with a crew of 22, was holding station in the St. Johns River, near Jacksonville, Florida, when a fire broke out in the engine room. Two crewmembers were in the machinery control room when the fire started: one escaped, and the other was removed by the shipboard emergency squad and later pronounced dead at a local hospital. After reporting no active fire and removing the crewmember, the crew verified that the engine room was sealed and released the fixed gas fire extinguishing system. No pollution was reported. Damage to the vessel was estimated at $18 million. The National Transportation Safety Board (NTSB) determined that the probable cause of the engine room fire on the dredging vessel Stuyvesant was lube oil spraying from an auxiliary diesel engine (generator) and igniting off a nearby running diesel engine, due to engine crewmembers not reinstalling a plug after routine maintenance in accordance with the engine manufacturer’s instructions and not thoroughly inspecting the port auxiliary engine before initially starting it.]]></description>
      <pubDate>Tue, 18 Nov 2025 09:29:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2620729</guid>
    </item>
    <item>
      <title>Micromobility Analytics in Florida: Usage Patterns, Public Transit Synergies, and Crash Insights</title>
      <link>https://trid.trb.org/View/2590596</link>
      <description><![CDATA[Micromobility has become increasingly popular in cities across Florida and the nation, offering a convenient, flexible, and accessible alternative for short-distance travel, particularly for first- and last-mile transportation. However, most areas of Florida and even the nation currently lack a framework and established practices for micromobility analytics, primarily due to an absence of relevant data, to understand their usage patterns, crash patterns, and relationships with public transit. With available data in at least two Florida cities – Jacksonville and Gainesville, this project aims to conduct micromobility analytics with the following goals: (1) to identify micromobility usage patterns and underlying causes; (2) to examine the relationship between micromobility and public transit in Florida, focusing on accessibility and ridership impacts; and (3) to analyze the statewide patterns of crash events involving non-motorists, such as their spatiotemporal distributions and the street characteristics where crashes frequently occur. The main findings are: (1) shared micromobility usage shows distinct temporal patterns and is highly concentrated in a few census tracts across cities; (2) while shared micromobility extends the reach of public transit, its impact on increasing transit ridership is modest; (3) crashes exhibit similar spatiotemporal patterns to usage, with higher usage increasing crash likelihood, and crashes are also closely linked to specific street and location features, such as availability of bike lanes. The insights gained can provide crucial recommendations for micromobility facility planning, including device and location choices, infrastructure improvements, and rebalancing strategies, to improve system efficiency, encourage use, reduce crashes, and enhance integration with public transit in Florida.]]></description>
      <pubDate>Thu, 28 Aug 2025 17:11:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2590596</guid>
    </item>
    <item>
      <title>BAE Systems Jacksonville Ship Repair—Shiplift Dry-Docking Complex</title>
      <link>https://trid.trb.org/View/2559521</link>
      <description><![CDATA[Currently, under final construction, the BAE Systems (BAE) Jacksonville Ship Repair (BAEJSR) is poised to revolutionize ship docking in Florida and globally with its groundbreaking ship lift system, one of the world’s largest, boasting a remarkable lifting capacity. Amidst an ambitious expansion initiative totaling over $200 million, BAE’s latest endeavor involves the establishment of a state-of-the-art vertical ship lift and dry-docking complex. This cutting-edge infrastructure enhancement aims to bolster the industrial capacity vital for national security and the sustained growth of the US and international maritime sectors.]]></description>
      <pubDate>Tue, 24 Jun 2025 15:24:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559521</guid>
    </item>
    <item>
      <title>JAXPORT Bartram Island Cell C Restoration and Capacity Improvement</title>
      <link>https://trid.trb.org/View/2559519</link>
      <description><![CDATA[Bartram Island, on the St. Johns River in Jacksonville, Florida, consists of several dredged material management areas (DMMAs). The DMMAs provide storage of maintenance dredged materials from the navigation channel and Jacksonville Port Authority’s (JAXPORT’s) berths. These DMMAs are nearly full, and JAXPORT faces a critical need for increased capacity. A 2021 study assessed the dredged material management needs and proposed alternatives to create capacity. One alternative—JAXPORT’s Bartram Island Cell C Restoration and Capacity Improvement project—would create 1,224,000 cu yd (935,815 m3) of capacity. Work began with a geotechnical evaluation of site foundation conditions and stored dredged materials. The design required offloading unsuitable dredged materials and mining suitable materials to raise the containment dikes. The design addressed challenging seepage and slope stability conditions using a stability berm, internal drains, and geotextile reinforcement. One year after design and permitting began, construction commenced in February 2022 and concluded in mid-2024.]]></description>
      <pubDate>Tue, 24 Jun 2025 15:24:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559519</guid>
    </item>
    <item>
      <title>Prediction of Traffic Incident Clearance Duration Using Neural Network for Multimodal Data Distribution</title>
      <link>https://trid.trb.org/View/2400142</link>
      <description><![CDATA[Traffic incidents adversely affect the safety and mobility of our transportation network. As such, accurate prediction of incident duration is critical in developing strategies and deploying resources to quickly clear incidents and restore traffic to pre-incident conditions. This study introduces a mixture density network (MDN) based on Gamma and Weibull distributions to estimate incident clearance duration. The MDN is known for being highly flexible and can recognize multiple components in the distribution of a target variable. A total of 58,167 incidents from highways in Jacksonville, Florida, gathered from 2014 to 2017, were used as a case study. The comparison between MDN, basic artificial neural network (ANN), and XGBoost revealed that the MDN outperformed the other models by having the lowest mean square error (MSE) and mean absolute error (MAE). The MSE of Gamma MDN was the lowest, estimated at 926 min, compared to 935, 945, and 975 min of the Weibull MDN, ANN, and XGBoost, respectively. Based on the Gamma MDN, the key variables influencing incident clearance duration estimated by the permutation feature importance and shapley additive explanations algorithms include the type of agencies that responded to incidents, the number of agencies involved, incident type, and incident severity. The practical contribution and the application of this study in diverse areas have been discussed. It is expected that the findings will help to improve incident clearance strategies. Specifically, the developed model could be utilized to develop incident management strategies that will proactively address the safety and mobility impacts of traffic incidents on roadways.]]></description>
      <pubDate>Mon, 12 Aug 2024 10:26:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2400142</guid>
    </item>
    <item>
      <title>Associating Incident Clearance Duration with Freeway Segment Types Using Hierarchical Bayesian Survival Model</title>
      <link>https://trid.trb.org/View/2048437</link>
      <description><![CDATA[Traffic incidents represent nonrecurring events that have long been found to deteriorate traffic operations and safety. Studies agree that quick incident clearance would translate into substantial savings for the motoring public. However, many factors influence how quickly an incident can be cleared. This paper investigated the influence of the incident location on incident clearance duration using a hierarchical Bayesian survival model. The study presented a statistical approach to examine disparities in incident clearance duration on different freeway segments (i.e., basic, merge, diverge, weaving, on-ramp, and off-ramp sections). In the analysis, data from 58,167 incidents that occurred on freeways in Jacksonville, Florida, for the years 2014–2017 were analyzed. The Bayesian hypothesis testing revealed credible differences in incident clearance durations among most freeway segment pairs. The model results indicated that basic freeway segments had the longest incident clearance durations, followed by diverge segments and off-ramps. Incidents that were crashes, were severe, resulted in shoulder blockage, occurred on weekends, and occurred on segments with high traffic volumes took significantly longer time to be cleared. The study findings could help practitioners strategically position and allocate appropriate incident response resources along the freeway corridors. Practitioners could consider depots along longer basic freeway segments and diverge segments to enable the quick arrival of incident response teams.]]></description>
      <pubDate>Mon, 28 Nov 2022 10:56:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2048437</guid>
    </item>
    <item>
      <title>Effects of risk perception and perceived certainty on evacuate/stay decisions</title>
      <link>https://trid.trb.org/View/2008248</link>
      <description><![CDATA[In hurricane evacuation studies, the extant literature has extensively explored the effect of risk perception on evacuate/stay decisions. However, less attention has been paid to how perceived certainty affects households' evacuate/stay decisions. The objectives of this paper are to explore the effects of (1) perceived certainty about location of impact on both risk perception and perceived certainty about evacuation logistics; (2) risk perception on perceived certainty about evacuation logistics; and (3) perceived certainty about evacuation logistics on evacuate/stay decisions. Survey data gathered from households in the Jacksonville, Florida metropolitan area after Hurricane Matthew (2016) were analyzed using structural equation modeling (SEM). In addition, SEM allowed us to identify the factors that could be used to predict risk perception, perceived certainty about location of impact, and perceived certainty about evacuation logistics. The results showed that perceived certainty about location of impact had a non-significant effect on risk perception. Perceived certainty about location of impact had a positive effect on perceived certainty about evacuation logistics. However, the effect of risk perception on perceived certainty about evacuation logistics was non-significant. Both risk perception and perceived certainty about evacuation logistics had positive effects on households' evacuation decision while perceived certainty about location of impact had a negative effect on households' likelihood of evacuating. Overall, the findings can be used to improve on the prediction of households’ evacuation behavior.]]></description>
      <pubDate>Wed, 05 Oct 2022 14:02:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2008248</guid>
    </item>
    <item>
      <title>Feasibility Analysis of Real-time Intersection Data Collection and Processing Using Drones</title>
      <link>https://trid.trb.org/View/1953284</link>
      <description><![CDATA[Traditional data collection techniques at intersections are known to be time consuming and costly while handling the complexity associated with the heavy traffic volume and travel demand on today’s roadways. Therefore, transportation agencies have been searching for more innovative, safer, and cheaper data collection solutions to have a faster and lower cost collection and analysis of traffic data to obtain traffic volume, speeds, queues, turning movements and conflict points (e.g., vehicle to vehicle, vehicle to pedestrian or bicycle, etc.) at intersections. One innovative solution is using drones in combination with computer vision applications. The overall goal of this project was to provide a feasibility analysis on the utilization of drones and computer vision applications to extract microscopic traffic data at intersections. Findings are expected to help the Florida Department of Transportation (FDOT) in integrating new technologies into their day-to-day data collection operations. Consistent with this goal, the following tasks have been completed as part of the project: (a) perform literature review and analyze state-of the-practice to provide guidance and recommendations on legally and safely using drones with video/image processing techniques for the uniform traffic studies; (b) generate statewide crosswalk inventory using aerial images and artificial intelligence (AI2); (c) investigate the fatal pedestrian-involved crashes that occur at locations other than intersections in Florida and analyze their detailed crash reports; (d) design and conduct exercises with tethered drones to collect intersection data in the cities of Tallahassee and Jacksonville, Florida; and (e) perform a cost analysis comparing traditional methods with different drone-based traffic data collection techniques. Meeting these objectives led to appropriate guidelines and recommendations to FDOT in terms of evaluating and justifying the feasibility of using drones as safer and cheaper data collection alternatives while significantly improving intersection safety and operations. Results and recommendations of this research will also be used by the FDOT consultants who already perform traffic data collection on Florida’s roadways.]]></description>
      <pubDate>Tue, 24 May 2022 10:22:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/1953284</guid>
    </item>
    <item>
      <title>Determinants of Departure Timing for Hurricane Matthew and Anticipated Consistency in Future Evacuation Departures</title>
      <link>https://trid.trb.org/View/1927407</link>
      <description><![CDATA[This work investigated the factors affecting household choice of departure time during evacuations in Hurricane Matthew in 2016. Departure time estimates are needed to predict time-varying evacuation demand for use in simulation models and the development of evacuation traffic management strategies. The research team conducted a household survey after Hurricane Matthew in the Jacksonville, Florida, metropolitan area, with a total sample size of 588 respondents. Newly introduced factors were examined for significance throughout this work and were found to affect evacuation departure timing, such as uncertainty, family relationships, and cohesion. Uncertainty affects how certain the potential evacuees are about hurricane information such as hurricane impact location, whether they live in an evacuation zone, the timing of the hurricane and the evacuation destination and the route by which to get there, as well as the time needed to prepare for the evacuation. Family cohesion is related to decision-making agreement among household members and their preference to stay together in difficult situations. Such factors were poorly presented in previous literature. A Cox proportional-hazards model, a survival analysis technique used to study time till event, was used to model the evacuation departure timing, based on data from a post-Hurricane Matthew survey of Jacksonville, Florida, metropolitan area residents. The final model contained three significant variables, of which two are related to uncertainty and family cohesion. This study also used a binary logit model to examine evacuees’ retrospective preferences about whether they would have changed their evacuation timing. The preferred model contained five significant variables related to past experience, the type of evacuation order received, and the evacuation destination. This work opens several opportunities for additional studies on topics such as the stability of departure time; in addition, new factors presented here should be considered in future studies, such as certainty levels and household cohesion. Additional measures of experience could be incorporated to better understand the nuances of the experiences of different components of evacuation behavior.]]></description>
      <pubDate>Mon, 25 Apr 2022 10:04:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1927407</guid>
    </item>
    <item>
      <title>Estimating the Mobility Benefits of Adaptive Signal Control Technology Using a Bayesian Switch-Point Regression Model</title>
      <link>https://trid.trb.org/View/1920036</link>
      <description><![CDATA[The adaptive signal control technology (ASCT) is a traffic management strategy that adjusts signal timing parameters to optimize corridor performance based on actual traffic demand. This study used a Bayesian switch-point regression model (BSR) to estimate the mobility benefits of the ASCT. A 5.3-km (3.3-mi) corridor of Mayport Road in Jacksonville, Florida, was used as the case study. The results indicated that the ASCT improved travel speeds by 4% on midweekdays (Tuesday, Wednesday, and Thursday) in the northbound direction. However, in the southbound direction, mixed results were observed that may be attributed to higher driveway density and congestion. Moreover, the BSR model results revealed that there is a significant difference in the operating characteristics between with and without ASCT scenarios. Transportation agencies could use the findings of this study to justify and plan the future deployment of the ASCT.]]></description>
      <pubDate>Mon, 28 Mar 2022 10:27:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1920036</guid>
    </item>
    <item>
      <title>Estimating hurricane evacuation destination and accommodation type selection with perceived certainty variables</title>
      <link>https://trid.trb.org/View/1926777</link>
      <description><![CDATA[This paper investigates how perceived certainty factors influenced households’ selection of destination and accommodation type during evacuation. Using survey responses from Jacksonville, FL, multinomial logit models were developed for both choices. For the first, greater understanding of hurricane-related graphics decreased households' probability of staying within their community. Households with a member who has special medical needs and those evacuating with a greater number of vehicles were more likely to stay in the eastern portion of their county. Greater perceived certainty about the hurricane impact location decreased households’ probability of evacuating to the south. For the accommodation model, married evacuees and those who received official evacuation notices had increased likelihood of staying in hotels/motels, while those who evacuated a day before landfall were less likely to do so. Greater perceived certainty about hurricane impact time and frequency of communication with social network members increased the probability of staying in a peer’s home.]]></description>
      <pubDate>Fri, 25 Mar 2022 12:36:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1926777</guid>
    </item>
    <item>
      <title>Volume 1: Integrated ABM-DTA Methods to Model Impacts of Disruptive Technology on the Regional Surface Transportation System – A Feasibility Study</title>
      <link>https://trid.trb.org/View/1853042</link>
      <description><![CDATA[This report investigates using detailed simulation models to help regional and state agencies plan for the effects of connected vehicle (CV) and autonomous vehicle (AV) technologies in long-range planning. The research integrates the DaySim activity-based travel demand model with the TransModeler dynamic traffic simulation model for Jacksonville, Florida, for Exploratory Modeling and Analysis (EMA). The work adapts travel demand models to simulate households’ decisions whether to purchase autonomous vehicles instead of conventional vehicles, and to simulate travelers’ decisions whether to use CAV-based car-sharing and ride-sharing services. The dynamic network models simulate operating characteristics of CAVs—depending on network vehicle mix—and simulate the performance of CAV-only infrastructure under different demand scenarios. The models simulate dozens of different scenario combinations to explore potential outcomes and find critical input assumptions while identifying future policy directions that are likely to be the most robust in the face of “deep uncertainty.”]]></description>
      <pubDate>Wed, 03 Nov 2021 11:46:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1853042</guid>
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