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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>
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    <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>Efficiency Assessment of Extended Change and Clearance Intervals on Signalized Intersections and Corridors</title>
      <link>https://trid.trb.org/View/2592311</link>
      <description><![CDATA[Traffic signal control systems play a critical role in managing urban mobility by regulating the flow at intersections. The Florida Department of Transportation (FDOT) installed a new signal timing system at several signalized intersections along multiple corridors in Central Florida. In December 2013, Orange County began implementing this system, which was completed in June 2015. This action was taken to reduce the frequency of redlight running incidents. The primary objective of this study was to assess how signalized intersections and corridors are affected by extended change and clearance intervals. Specifically, it aimed to evaluate FDOT’s signal timing effort and its potential impact on the safety and operational performance of selected intersections. To address this, twenty signalized intersections along three corridors in Central Florida were investigated. Additionally, three signal timing patterns were examined to evaluate the effectiveness and safety of the baseline (Pattern 1), the current FDOT implementation (Pattern 2), and the proposed alternative (Pattern 3). Microsimulation analysis was conducted using SimTraffic, a component of the Synchro 8 software. The study found that extended signal timing in Pattern 2 and the proposed Pattern 3, which incorporate longer change and clearance intervals, significantly increased intersection delay and volume-to-capacity (V/C) ratios. Furthermore, these patterns also led to noticeable increases in overall delay and travel time along the studied corridors.]]></description>
      <pubDate>Thu, 30 Oct 2025 08:49:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592311</guid>
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    <item>
      <title>Investigation of the Relationship between Red-Light-Running Frequencies and Intersection Features</title>
      <link>https://trid.trb.org/View/2203098</link>
      <description><![CDATA[Red-light running is a complex problem. There is no simple reason to explain why drivers run red lights. The purpose of this study is to improve understanding of the red-light running and its relationship with the intersection features so that efficient countermeasures can be developed to improve intersection safety. Five large four-legged signalized intersections in Orange County in Central Florida, U.S., were selected. Data collected for these intersections include intersection features and red-light-running violations in 2006 and 2007. Red-light-running violations were modeled at the approach level using Generalized Estimating Equations (GEE) with Negative Binomial as the link function to account for site correlation among the data. The following factors were identified that have significant contributions to red-light-running frequencies: traffic flows to which the conflicting traffic belongs, the number of through lanes on the conflicting approach and speed limit of conflicting approach, and yellow time of a near-side approach.]]></description>
      <pubDate>Fri, 31 May 2024 08:57:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2203098</guid>
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      <title>Detecting School Zones on Florida’s Public Roadways Using Aerial Images and Artificial Intelligence (AI2)</title>
      <link>https://trid.trb.org/View/2222641</link>
      <description><![CDATA[Collecting up-to-date roadway geometry data is essential for transportation agencies so they can undertake planning, maintenance, design, and rehabilitation of roadways. Collection methods can be categorized into two distinct groups: land-based methods (e.g., field inventories, mobile mapping, and image logging); and aerial-based methods (e.g., satellite imagery, drones, and laser scanning). Because using land-based methods for thousands of miles of highways is tedious, costly, and risky for crew members, there is a need to develop better methodologies to collect these data faster, and more safely and cheaply. The increasing availability of high-resolution images and recent advances in computer vision and object detection techniques have enabled the automated extraction of roadway geometry features. This novel study proposes a computer vision-based methodology to detect school zone pavement markings from high-resolution aerial images and determine school zones on Florida’s public roadways. This is critical information for transportation agencies, and they use it for a variety of different purposes: identifying those markings that are old and invisible; comparing the school zone locations with other geometric features such as crosswalks; and analyzing crashes that occur around the zones. Compared with the ground truth data obtained for Leon County, Florida, 94% accuracy was observed at the 90% confidence level. The model was then used to detect school zones in Orange County, Florida, and approximately 500 school zone markings were identified automatically. The road geometry data extracted can be integrated with crash and traffic data to advise policymakers and roadway users.]]></description>
      <pubDate>Thu, 03 Aug 2023 11:40:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2222641</guid>
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    <item>
      <title>Investigating the effects of left-turn distracted drivers on signalized intersections’ traffic operations</title>
      <link>https://trid.trb.org/View/2198081</link>
      <description><![CDATA[Distracted driving represents a serious obstacle to maintaining an efficient transportation system. The safety impacts of distracted driving had been thoroughly explored. However, the traffic operations impact has received less attention. Few studies addressed distracted driving behaviors in through lanes, but less focus has been provided on the left turners. The goal of this study is to fill this gap in the research and address the impact of distracted driving for left turners on traffic operations at signalized intersections. Field data were collected from six (6) intersections, with thousands of observations analyzed in Orange County, Florida, studying different land use, lane configurations, and peak periods. The results demonstrated that 87% of all drivers were distracted. 28% were distracted by cell phone usage and showed significant effect only during the AM peak. During the PM peak, talking to passengers and dashboard categories were significant. In all peak periods, the category of “not identified” distractions was dominant (48%), indicating drivers not paying attention and staring through the windshield. Drivers in the first row in the queue experienced more distractions in the PM peak than in the AM peak. Motorists in residential & school land use had lower headway than those in mixed land use. This can be attributed to motorists' cautious driving behavior due to the existence of school zones and pedestrian crossings. In contrast, motorists in mixed land use tend to be more distracted by the commercial and tourist areas. The statistical models demonstrated that the overall effect of distracted drivers in the left lanes on the discharge headway at signalized intersections is significant. The TOD analysis showed that the distractions increased the headway by 40%, 37%, and 43% during the AM, MD and PM peak hours, respectively. Conversely, the overall distractions model results showed that the base headway increased by 40% resulting in reducing the intersection’s capacity by 30%.]]></description>
      <pubDate>Wed, 28 Jun 2023 14:56:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2198081</guid>
    </item>
    <item>
      <title>Why do lower-income areas experience worse road safety outcomes? Examining the role of the built environment in Orange County, Florida</title>
      <link>https://trid.trb.org/View/2036117</link>
      <description><![CDATA[Lower-income areas experience an increased incidence of traffic crashes, injuries, and deaths than more affluent ones. Researchers tend to blame these outcomes on differences in car ownership, with lower-income households expected to use vulnerable active modes at higher rates, as well as being more likely to drive older vehicles with fewer safety features. While these explanations seem plausible enough, they likely fail to provide a complete picture of the problem. This study examines the role of the built environment in road safety outcomes for lower- and higher-income block groups in Orange County, Florida. The authors find that the hazard posed by urban arterials is three times greater for lower-income environments than for more affluent communities. Sidewalk buffers, ordinarily regarded as a pedestrian amenity, were associated with crash increases in affluent areas but not lower-income ones. Areas with concentrations of black residents were found to be increased risk, even after accounting for differences in income. Considered as a whole, the risk factors for lower-income and high-income populations are not the same. This article examines the underlying reasons that lead to these outcomes and discusses the need to better account for the manner in which design may uniquely affect the safety of different demographic cohorts.]]></description>
      <pubDate>Wed, 19 Oct 2022 13:31:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2036117</guid>
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    <item>
      <title>Distracted Driving Effects on Headways at Signalized Intersections</title>
      <link>https://trid.trb.org/View/2018229</link>
      <description><![CDATA[Distracted driving poses one of the most difficult challenges to ensuring a safe and efficient transportation system. Modern communications have delivered greater convenience. However, this has come at the cost of attention spans. Safety has been thoroughly explored from the perspective of distracted driving. However, impacts on traffic operations have received minimal research attention. A few studies have provided a theoretical mechanism on how intersection operations can be affected, but fail to quantify the real-life impacts on traffic operations. This research aims to quantify how distracted driving affects vehicle discharge headways at signalized intersections for through lanes. Thousands of observations were collected from four intersections in Orange County, Florida, covering a variety of land uses, intersection configurations, and periods of high demand. The results demonstrated that approximately a quarter of all drivers were distracted. Drivers were less distracted in commercial zones and more attentive to signal changes compared to school and residential areas. Cell phone usage had the primary effect on headways among distraction types with a 20% increase, which resulted in reducing the intersection capacity by 16.5%. The statistical model demonstrated that the overall effect of distraction on the discharge headway is significant. The base headway increased by 0.93?s, which resulted in reducing the intersection capacity by 45.5%. The results also revealed that the 10th vehicle position in the queue had a detrimental effect on the headway and the overall intersection capacity. The green phase gaps out because of the amount of time needed to reach the stop bar.]]></description>
      <pubDate>Thu, 08 Sep 2022 09:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2018229</guid>
    </item>
    <item>
      <title>The Influence of the Built Environment on Crash Risk in Lower-Income and Higher-Income Communities</title>
      <link>https://trid.trb.org/View/1850469</link>
      <description><![CDATA[This report presents the findings of a study that uses negative binomial regression models to examine differences in the relationship between the built environment and crash incidence for block groups in Orange County, Florida. It finds notable differences in the modelled variables between these income groupings, both in the magnitude and the direction of effects. First, while urban arterials are a risk factor for lower- and higher-income block groups alike, their negative effect on safety is profoundly greater in lower-income environments. For higher income communities, each additional mile of urban arterial is associated with a 9% increase in total and fatal-and-injury crashes (KAB crashes), though it did not have a statistically meaningful relationship with pedestrian crashes. For lower-income communities, each mile of urban arterial is associated with a nearly 30% increase in total and KAB crashes, as well as a 19% increase in pedestrian crashes. This study further finds that “livability” features have different safety effects in high-income and low-income communities. While it is widely presumed that the presence of sidewalks and sidewalk buffers enhance safety, the results of this study suggest that the relationship between these features and traffic safety is more complicated. For more affluent areas, which contain residents who are less dependent on walking as a primary means of transportation, sidewalk buffers were found to be associated with significant increases in total, injurious, and pedestrian collisions alike, while the presence of sidewalks was associated with a significant increase in injuries involving all road users. For lower income communities, sidewalks and sidewalk buffers were not significantly related to increases in crashes or injuries; indeed, the increased presence of these features tended to be associated with reductions in injurious and pedestrian crashes. Finally, race proved to have an important role on crash risk. While the percentage of white residents was not meaningfully associated with crash risk in more affluent block groups, race emerges as an important factor for understanding crash risk in lower-income communities, with higher concentrations of non-white residents being associated with significant increases in total, injurious, and pedestrian-related crashes. This report concludes by discussing the likely causes and broader implications of these findings.]]></description>
      <pubDate>Fri, 21 May 2021 10:54:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/1850469</guid>
    </item>
    <item>
      <title>Systemic Approach to Improve Safety of Urban Unsignalized Intersections: Development and Validation of a Safety Index</title>
      <link>https://trid.trb.org/View/1696481</link>
      <description><![CDATA[Methods based on crash data analysis are effective in identifying intersections with a potential for safety improvement. However, it is well recognized that crash data suffer from several shortcomings and that there are clues to safety other than crash occurrence. The systemic approach is an alternative method to address safety issues. In this approach, a transportation agency is able to identify priority locations based on the presence of risk factors rather than actual crashes. To promote wider use of the systemic safety approach, this paper aims at developing and validating a procedure to rank unsignalized urban intersections for safety improvement based on the evaluation of risk factors by road safety inspections. The procedure assesses a Safety Index (SI) that measures the safety performance of unsignalized urban intersections. The SI is formulated by combining two components of risk: the exposure of road users to road hazards (Exposure) and the risk factors, which increase the probability of involvement in crashes (Risk Index). The SI is made of two elements, one related to vehicles and one to pedestrians. Twenty-three detailed safety issues and ten general safety issues are assessed to compute the vehicle Risk Index and the pedestrian Risk Index. Safety issues were selected considering that they are common issues and that effective remedial measures exist and have already proven their effectiveness. Finally, criteria for identifying and ranking safety issues were defined. The SI has two main practical applications. High risk intersections, where safety measures that can reduce crash frequency exist, can be identified and ranked by the SI. Specific safety issues, that give more contribution to unsafety, are pointed out in order to give indication about more appropriate safety measures according to the systemic safety approach. The procedure was validated with a sample of eighty-nine urban intersections located in Orange County (Florida, U.S.). For these intersections, the SI scores, the Empirical Bayes (EB) safety estimates, and the potential for improvement (PFI) were compared. The correlation between the SI scores and the EB estimates was highly significant both for vehicles (R2 = 0.66) and pedestrians (R2 = 0.58) as well as for the total crashes (R2 = 0.68). The results from the Spearman's rank-correlation analysis provided further validation for the SI indicating that ranking from the SI and the EB estimates agree at the 99.9% confidence level for vehicles (ρs = 0.78), pedestrians (ρs = 0.93), and total (ρs = 0.93).]]></description>
      <pubDate>Tue, 26 May 2020 10:22:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1696481</guid>
    </item>
    <item>
      <title>14 sustainable maintenance ideas</title>
      <link>https://trid.trb.org/View/1401668</link>
      <description><![CDATA[Two divisions of a county public works department - Fleet Management and Roads & Drainage - have found unique ways to lower air and water pollution created by the intersection of two critical infrastructure systems. Sustainable operations and maintenance initiatives have been implemented by Orange County's Public Works Department, headquartered in Orlando, FL. Their efforts to manage runoff, utilize alternative fuel, retrofit equipment to be more environmentally friendly, filter out pollutants from stormwater, and use biological control for vegetation management are presented here. Also discussed are the use of aquifer inflow treatment, porous pavements, cold-in-place recycling, and warm-mix asphalt.]]></description>
      <pubDate>Thu, 24 Mar 2016 10:49:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1401668</guid>
    </item>
    <item>
      <title>Evaluation and spatial analysis of automated red-light running enforcement cameras</title>
      <link>https://trid.trb.org/View/1336424</link>
      <description><![CDATA[Red light cameras may have a demonstrable impact on reducing the frequency of red light running violations; however, their effect on the overall safety at intersections is still up for debate. This paper examined the safety impacts of Red Light Cameras (RLCs) on traffic crashes at signalized intersections using the Empirical Bayes (EB) method. Data were obtained from the Florida Department of Transportation for twenty-five RLC equipped intersections in Orange County, Florida. Additional fifty intersections that remained with no photo enforcement in the vicinity of the treated sites were collected to examine the spillover effects on the same corridors. The safety evaluation was performed at three main levels; only target approaches where RLCs were installed, all approaches on RLC intersections, and non-RLC intersections located on the same travel corridors as the camera equipped intersections. Moreover, the spatial spillover effects of RLCs were also examined on an aggregate level to evaluate the safety impacts on driver behavior at a regional scale. The results from this study indicated that there was a consistent significant reduction in angle and left-turn crashes and a significant increase in rear-end crashes on target approaches, in addition, the magnitude and the direction of these effects, to a lesser degree, were found similar on the whole intersection. Similar trends in shift of crash types were spilled-over to non-RLC intersections in the proximity of the treated sites. On an aggregate county level, there was a moderate spillover benefits with a notable crash migration to the boundary of the county.]]></description>
      <pubDate>Mon, 02 Feb 2015 10:24:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/1336424</guid>
    </item>
    <item>
      <title>A Method for Linking Motor Vehicle Victim and Collision Data Collected by Multiple County Agencies</title>
      <link>https://trid.trb.org/View/1278627</link>
      <description><![CDATA[This retrospective cohort study evaluated motor vehicle crashes in Orange County Florida in 2009 that became medical examiner cases. Data from the Department of Highway Safety and Motor Vehicles (DHSMV), emergency medical services (EMS), a level I trauma center, and the medical examiner were integrated for the analysis. The primary outcome measure was early death, defined by death within 48 hours of a motor vehicle trauma. Both traditional and nontraditional predictors of early mortality were assessed. The most significant factors associated with early mortality were as follows: (1) From autopsy: hemothorax (odds ratio [OR] = 8.26, 95% confidence interval [CI]: 1.83–37.3) and liver injury (OR = 4.26, 95% CI: 1.70–15.6); (2) from hospital data: systolic blood pressure (OR = 0.98, 95% CI: 0.96–0.99) and having cardiopulmonary resuscitation (CPR) performed in the emergency department (OR = 13.4, 95% CI: 1.51–118.72); and (3) from DHSMV: involvement of drugs and/or alcohol (OR = 4.27, 95% CI: 1.33–13.6), total fatalities (OR = 6.07, 95% CI: 1.57–23.5), speed of vehicle (OR = 1.06, 95% CI: 1.02–1.09), and number of lanes at the crash scene (OR = 1.58, 95% CI: 1.13–2.20). These results were made possible by integrating 4 distinct data sources.  As future research in traffic-related injury moves toward prevention, it will be critical to evaluate new preventative strategies quickly and effectively. A unique number that is both patient and event specific that could be incorporated into each of these databases would make such integration seamless. Successful methods for linking data collected by the multiple agencies involved in motor vehicle collisions will ultimately provide invaluable information for medical personnel, researchers, engineers, planners, and policy makers at the local, state, and national levels to identify safety priorities to reduce crash-related injuries and fatalities.]]></description>
      <pubDate>Fri, 30 May 2014 11:13:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1278627</guid>
    </item>
    <item>
      <title>Investigation of road network features and safety performance</title>
      <link>https://trid.trb.org/View/1251731</link>
      <description><![CDATA[The analysis of road network designs can provide useful information to transportation planners as they seek to improve the safety of road networks. The objectives of this study were to compare and define the effective road network indices and to analyze the relationship between road network structure and traffic safety at the level of the Traffic Analysis Zone (TAZ). One problem in comparing different road networks is establishing criteria that can be used to scale networks in terms of their structures. Based on data from Orange and Hillsborough Counties in Florida, road network structural properties within TAZs were scaled using 3 indices: Closeness Centrality, Betweenness Centrality, and Meshedness Coefficient. The Meshedness Coefficient performed best in capturing the structural features of the road network. Bayesian Conditional Autoregressive (CAR) models were developed to assess the safety of various network configurations as measured by total crashes, crashes on state roads, and crashes on local roads. The models’ results showed that crash frequencies on local roads were closely related to factors within the TAZs (e.g., zonal network structure, TAZ population), while crash frequencies on state roads were closely related to the road and traffic features of state roads. For the safety effects of different networks, the Grid type was associated with the highest frequency of crashes, followed by the Mixed type, the Loops & Lollipops type, and the Sparse type. This study shows that it is possible to develop a quantitative scale for structural properties of a road network, and to use that scale to calculate the relationships between network structural properties and safety.]]></description>
      <pubDate>Mon, 17 Jun 2013 14:16:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/1251731</guid>
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    <item>
      <title>CA-Based Urban Land Use Prediction Model: A Case Study on Orange County, Florida, U.S.</title>
      <link>https://trid.trb.org/View/1238578</link>
      <description><![CDATA[For a long time, interactions between land use and transportation have been one of the research hotspots in urban planning, which however, has not been investigated in much detail until recently. This paper started from spatial changes of regional land use, with an objective of understanding the relationship between trip generation and urban land use. Cellular automata and geographical information system techniques were used to store and update the spatial data dynamically. In addition, MATLAB software was adopted to conduct the logistic regression using land use/land cover data from Orange Country, Florida, U.S. (1990 and 2000). With fully utilizing the advantages of cellular automaton models, simulation results indicate the enhenced reliability of the model, which consequently assists to understand evolution of urban land uses.]]></description>
      <pubDate>Fri, 01 Feb 2013 10:10:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1238578</guid>
    </item>
    <item>
      <title>LandSys: An Agent-Based Cellular Automata Model of Land Use Change Developed for Transportation Analysis</title>
      <link>https://trid.trb.org/View/1223134</link>
      <description><![CDATA[This paper reports on LandSys, a land use model that integrates a Cellular Automata (CA) model and agent-based models to facilitate transportation demand modeling and analysis. The LandSys model simulates the spatial suitability of land use change over time based on both the land use suitability and the impacts of neighboring land use types in a CA model framework, coupled with individual decision-makers' behaviors with agent-based models (e.g., household, employment, and developer agents). To generate inputs for transportation models, two equilibriums in the land use market are considered: land development equilibrium simulates land use change at a manageable cell level (50 m x 50 m), while land supply-demand equilibrium allocates firms and agents based on the bid-rent theory. Data from Orange County, Florida, are used in a case study for model estimation and validation. A comparison of model results with actual data from 2000 shows that the proposed LandSys model is effective in estimating land use change, with an accuracy of 85.4%. The model is also responsive to land use policies and could be used for what-if analysis.]]></description>
      <pubDate>Mon, 31 Dec 2012 17:54:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1223134</guid>
    </item>
    <item>
      <title>Incorporating Road Network Structures into Macro Level Traffic Safety Analysis</title>
      <link>https://trid.trb.org/View/1115488</link>
      <description><![CDATA[Traffic safety has received increasing attention. Many macro level safety models explore the relationship between crash occurrence and explanatory variables. Although road network pattern is an essential aspect for transportation planning, studies of its safety effect are limited. In this paper, the meshedness coefficient was used to mirror the network structure and examine its effect on traffic analysis zone (TAZ) level safety. Data of 662 TAZs from Orange County, Florida, U.S.A. were collected. A conditional autoregressive model which considers the spatial correlations among TAZs was developed. Estimation results showed the meshedness coefficient performed well in capturing the nature of network patterns, as well as in building relationship with zonal level crashes. This study indicates that, besides traditional network size variables such as road length, structure of road network should also be considered in developing more precise zonal safety prediction models.]]></description>
      <pubDate>Thu, 13 Dec 2012 09:27:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/1115488</guid>
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