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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Traffic Speed Effects on Highway Safety Manual Crash Prediction Models




</title>
      <link>https://rip.trb.org/View/2558395</link>
      <description><![CDATA[Speed management and data-driven safety analysis are priority topics for the highway safety community. A key gap in the body of knowledge is limited understanding of traffic speed effects in the crash frequency and severity prediction models for most facility types. It is known that the severity of motor vehicle crashes increases with increasing traffic speed, and speed may influence crash frequency. However, speed is correlated with nearly every other factor in the American Association of State Highway and Transportation Officials (AASHTO) Highway Safety Manual (HSM) crash frequency and severity prediction methods. As a result, current prediction approaches do not seem to incorporate traffic speed effects well and may even show counterintuitive results.

Improving the consideration of traffic speed effects in the models may result in more realistic and insightful results. Given the correlation of traffic speed with other roadway and intersection features, innovative methods of quantifying speed effects in crash prediction methods in addition to regression modeling should be considered. Research is needed to find ways to incorporate traffic speed effects into HSM crash prediction models to make them more accurate and better suited toward developing designs based on the Safe System Approach.

The objective of the research is to develop implementable methods to incorporate the effects of traffic speeds on the prediction of crash frequency and severity. The proposed methods should be applicable to different roadway facility types, in a format compatible with HSM methods to support the work of state departments of transportation (DOTs) and other users of the manual.]]></description>
      <pubDate>Wed, 28 May 2025 10:16:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558395</guid>
    </item>
    <item>
      <title>Analysis of Contributing Factors in Crashes Involving Electric Vehicles and Vehicles with Automated Features</title>
      <link>https://rip.trb.org/View/2440011</link>
      <description><![CDATA[Most light-duty vehicle (LDV) crashes occur due to human error. The National Highway Safety Administration (NHTSA) reports that eight percent of fatal crashes in 2018 were distraction-affected crashes, while close to ninety-four percent of all crashes occur in part due to human error. Crash avoidance features could reduce both the frequency and severity of light and heavy-duty vehicle crashes, primarily caused by distracted driving behaviors and/or human error by assisting in maintaining control or issuing alerts if a potentially dangerous situation is detected. As the automobile industry transitions to partial vehicle automation, newer crash avoidance technologies are beginning to appear more frequently in non-luxury vehicles such as the Honda Accord and Mazda CX-9.  Additionally, the market penetration of electric vehicles (EVs) is increasing, in turn increasing the weight and size of vehicles on the road. However, the patterns and characteristics of crashes involving EVs or vehicles automated features have not been explored in much detail. This project develops a replicable, open, deployable model that can: (1) assess the distribution of crashes with automated features across factors such as weather conditions, vehicle speed, crash severity, pre-crash movement, facility type, and time of day, (2) estimate the relationship between contributing factors and the severity of crashes involving vehicles with automated features using regression analysis, and (3) assess the patterns and characteristics of crashes involving EVs.

The hypothesis is that current and past automated vehicles (AV) perform well in certain driving scenarios, facility types, and weather conditions but not others. However, there are not many frameworks and tools to help federal, state, and local agencies better understand the causes of crashes involving AVs, the locations of crash hotspots, and the infrastructure improvements and policies that could enhance road safety with AVs. Additionally, this study should help technology providers better understand under which scenarios and conditions further testing and technology development is needed to improve safety and performance. The research team also hypothesizes that the characteristics and patterns of crashes involving EVs differ from crashes not involving this technology. 

Most studies that have assessed the contributing factors in crashes involving vehicles with automated features have mostly focused on California (e.g., Kutela et al., 2022; Liu et al., 2021, 2024; Xu et al., 2019). This study will add to the previous literature due to the overall comprehensiveness of the crash data, which is representative of all 50 states and the District of Columbia.

In the first part of this project, the research team will compile crash data on advanced driver assistance systems (i.e., Level 2 automation) and automated driving systems (i.e., Level 3 and above) to better understand the frequency of crashes with automated features across contributing factors (e.g., weather or vehicle speed). Second, the team will incorporate this data into a regression model to better understand the relationship between the contributing factors and the frequency and severity of crashes. Finally, the team will conduct an exploratory analysis on the contributing factors for crashes involving EVs. To do this analysis, the team will utilize several publicly available datasets such as NHTSA’s AV crash reports, which provides information on crashes involving advanced driver assistance systems and automated driving systems (NHTSA, 2023) and the 2022 Fatality Analysis Reporting System.

In this phase of this project, the team will mainly focus on contributing factors for AV crashes and will use similar datasets to conduct a more in-depth analysis on the contributing factors for EV crashes in future iterations. Because of Tesla’s dominance in both the EV and automated vehicle (AV) market space it’s important to consider both the role that EVs and AVs play in crash safety. By focusing on both technologies independently, we can better understand the different ways they are impacting crash safety and how to mitigate any negative effects through policy.
]]></description>
      <pubDate>Sat, 12 Oct 2024 12:00:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440011</guid>
    </item>
    <item>
      <title>The Role of Built Environment Factors in Enhancing Pedestrian and Bicycle Safety: A Comprehensive Analysis and Policy Implications</title>
      <link>https://rip.trb.org/View/2401750</link>
      <description><![CDATA[Despite recent efforts to achieve Vision Zero goals in the US, pedestrian and bicycle safety remains a critical issue that affects individuals and communities. The nearly 7,500 pedestrian fatalities annually and 1,000 bicyclist fatalities in recent years highlight the urgent need to address pedestrian and bicycle safety, particularly in urban, suburban, and rural areas where exposure and crash risks are changing. Importantly, the role of the built environment in such environments is changing, e.g., disadvantaged communities, including low-income neighborhoods and communities of color, can face higher risks of pedestrian and bicycle crashes. This project focuses on enhancing pedestrian and bicycle safety through a detailed analysis of built environment features at the neighborhood level. As part of the Center for Pedestrian and Bicyclist Safety's (CPBS's) priorities on Safety Design, it explores the impact of factors such as street lighting, sidewalk availability, road design, land use type/mix and density, and traffic volumes on pedestrian and bicycle crashes frequency and their severity. With a particular emphasis on safety disparities in disadvantaged communities, this research utilizes a variety of data sources, including police crash reports, census data, land use data, and the Equitable Transportation Community (ETC) data released by the US Department of Transportation. The study will employ both traditional statistical methods and explainable artificial intelligence techniques to analyze data and identify key contributors to crash occurrences and severity during both day and night. Techniques such as negative binomial models, ordered probability models and structural equation modeling will be used to understand the direct and indirect effects of built environment features on safety outcomes. Special attention will be given to the role of these features in disadvantaged communities, aiming to develop targeted interventions to reduce crashes and enhance pedestrian and bicycle safety.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401750</guid>
    </item>
    <item>
      <title>Pedestrian Fatalities &amp; Injuries in Hit-and-Run Crashes in California, Tennessee &amp; the US: Recent Trends and Risk Factors</title>
      <link>https://rip.trb.org/View/2401742</link>
      <description><![CDATA[Both pedestrian fatalities and overall hit-and-run (HAR) fatalities in the US are at a 40-year high, but no post-COVID trends in fatal HAR pedestrian crashes have been examined despite increased reports of reckless driving, increasing vehicle weight and height, and increases in distracted driving. Further, few studies have examined trends in non-fatal pedestrian HAR crashes. Using 2009-2022 national crash fatality data and data on crashes at all severity levels in California and Tennessee, the research team will examine time trends in all HAR crashes, all pedestrian victim crashes, and how these are related. The team will also examine the risk of serious injury or death among HAR vs. non-HAR crashes to try to elucidate the relationship between HAR and outcome severity. Using regression techniques, the team will then examine risk factors for single vehicle-pedestrian crashes, including comparing risk factors for HAR vs non-HAR crashes and predictors of whether drivers are eventually identified in HAR crashes. Factors to be examined include crash characteristics, victim characteristics, and driver/vehicle characteristics, where available. The team also plans to examine the joint characteristics of driver-pedestrian pairs, such as by age, race or sex, to understand whether this pairing affects the likelihood of fleeing. Finally, the team will examine the effect of several inflection points on HAR crash rates and outcomes for pedestrians, including the effects of the COVID pandemic and, potentially, the effects of specific state-level policy changes around licensing laws, given past research linking HAR to unlicensed drivers.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401742</guid>
    </item>
    <item>
      <title>Effect of Bridge Width on Crashes in Nebraska</title>
      <link>https://rip.trb.org/View/2387176</link>
      <description><![CDATA[Bridges and bridge approaches sometimes are changed to accommodate increased traffic volume, lane expansion, maintaining adequate clearance, non-motorized accommodation, or for construction and maintenance cost optimization. Such changes may alter the relationship between bridge and approach widths and may impact the safety of the travelling public. The Nebraska Department of Transportation (NDOT) “Bridge Office Policies and Procedures,” initially published in January 2000 and revised in December 2016, serves as the reference and design guideline for NDOT bridge engineers, designers, detailers, and consultants working with the NDOT bridge office. However, NDOT staff lacks comprehensive guidelines regarding the impact of bridge and approach widths on crash frequencies and severities. A comprehensive research-based study is required to determine how bridge widths should be designed in relation to approach widths, while considering crash prevention and ensuring motorists have unrestricted mobility between bridge lanes.]]></description>
      <pubDate>Tue, 04 Jun 2024 12:42:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2387176</guid>
    </item>
    <item>
      <title>Safe System Approach for Including Trees in Urban and Suburban Roadway Contexts



</title>
      <link>https://rip.trb.org/View/2381741</link>
      <description><![CDATA[Roadside design guidance typically instructs agencies to limit fixed objects, including trees, along roadways to provide a safer recovery area for errant vehicles. On urban streets, trees are amenities that benefit pedestrians, bicyclists, residents, and others by providing shade, potential traffic calming and speed reductions, and aesthetic appeal.  However, trees are fixed objects that can cause serious injury or fatality if struck by an errant vehicle.

NCHRP Project 17-82, “Proposed Guidance for Fixed Objects in the Roadside Design Guide” explored crash prediction methods and developed design guidelines regarding trees and utility poles in rural non-freeway settings. On roadways with posted speed limits of 30 mph and less, trees are generally accepted as part of the roadside environment. At posted speed limits of 55 mph and greater, facilities for non-motorized users are less likely to be present and vehicular collisions with trees have the potential to result in serious injuries or fatalities. However, research is needed for urban and suburban roadways, particularly in posted speed limit settings between 35 and 50 mph. On these roadways, vehicle collisions with trees are a significant safety concern; however, maximizing properly and responsibly designed tree placements in the roadside environment can benefit public health and community livability.

Research is needed to help state departments of transportation (DOTs) and other transportation agencies advance the knowledge on the safety effects of trees and support guidelines to inform tree planning and landscaping policies, procedures, and practices that support the needs of all roadway users.

The objective of this research is to develop a practitioner’s guide for evaluating the safety effects of trees on urban and suburban roadways with a focus on posted speed limits of 35 to 50 mph.

The practitioner’s guide will include a framework for the placement, maintenance, removal, and replanting of trees and complementary features in roadway environments. This research will consider the needs of all users of the transportation system and support implementation of the Safe System approach.]]></description>
      <pubDate>Wed, 22 May 2024 11:59:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2381741</guid>
    </item>
    <item>
      <title>Estimating the Effects of Vehicle Automation and Vehicle Weight and Size on Crash Frequency and Severity: Phase 1</title>
      <link>https://rip.trb.org/View/2292646</link>
      <description><![CDATA[Most light-duty vehicle (LDV) crashes occur due to human error. The National Highway Safety Administration (NHTSA) reports that eight percent of fatal crashes in 2018 were distraction-affected crashes, while close to ninety-four percent of all crashes occur in part due to human error. Crash avoidance features could reduce both the frequency and severity of light and heavy-duty vehicle crashes, primarily caused by distracted driving behaviors and/or human error by assisting in maintaining control or issuing alerts if a potentially dangerous situation is detected. As the automobile industry transitions to partial vehicle automation, newer crash avoidance technologies are beginning to appear more frequently in non-luxury vehicles such as the Honda Accord and Mazda CX-9. Additionally, the market penetration of electric vehicles (EVs) is increasing, in turn increasing the weight and size of vehicles on the road. This project develops a replicable, open, deployable model that can: (1) estimate the upper-bound crash avoidance potential that could be achieved as the effectiveness of warning and partial automation systems improve and adoption increases, (2) estimate the societal costs and benefits of fleet-wide deployment of crash avoidance technologies considering technology costs and benefits from avoided and less severe crashes, (3) estimate the number of lives that have been saved by forward collision warning, lane departure warning, and blind spot monitoring, and (4) estimate the effects of vehicle weight and size on crash frequency and severity.  The hypothesis is that crash avoidance features are becoming more effective over time and helping to reduce the severity and frequency of crashes. However, there are not many frameworks and tools to help state and local agencies assess the private and societal cost and benefits of increased market adoption and how many lives have been saved by these technologies. The research team also hypothesizes that the trend of increasing vehicle weight and size increases the severity of crashes. This paper builds off previous UTC research by starting with a method similar to Harper et al. (2016) and Khan et al. (2019) but uses more recent insurance and crash data, contributes estimates of lives saved in addition to private net benefits and overall societal net benefits, and conducts exploratory analysis on the role that EVs and heavier vehicles play in crash safety.  In the first part of this project, the team will compile insurance institute data on crashes and crash severity, helping us to better understand observed changes in crash frequency and severity in vehicles that are equipped with warning and partial automation systems. Second, the team will conduct a cost-benefit analysis to estimate the net-private and net-societal benefits of fleet-wide deployment of existing partial automation and warning systems. Third, the team will develop a method to estimate the number of lives saved by crash avoidance technologies. Finally, the team will conduct exploratory analysis on the role that EVs and heavier vehicles play in crash safety. To do this analysis, the team will utilize several publicly available datasets such as 2022 Fatality Analysis Reporting System and observed insurance data from the Insurance Institute for Highway Safety.   In this phase of this project, the team will mainly focus on automation and its effects on crash severity and frequency and will use similar datasets to conduct a more in-depth analysis on how EVs and heavier vehicles affect crash safety in future iterations. By focusing on both technologies independently (i.e., non-EVs with partial automation and EVs without partial automation), we can better understand the different ways they are impacting crash safety and how to mitigate any negative effects through policy.  References Harper, C. D., Hendrickson, C. T., & Samaras, C. (2016). Cost and benefit estimates of partially-automated vehicle collision avoidance technologies. Accident Analysis & Prevention, 95, 104-115.  Khan, A., Harper, C. D., Hendrickson, C. T., & Samaras, C. (2019). Net-societal and net-private benefits of some existing vehicle crash avoidance technologies. Accident Analysis & Prevention, 125, 207-216.]]></description>
      <pubDate>Mon, 20 Nov 2023 20:20:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292646</guid>
    </item>
    <item>
      <title>Urban Demographic Shift of Pedestrian and Bicyclist Collisions, Equity, and Police Enforcement</title>
      <link>https://rip.trb.org/View/2232673</link>
      <description><![CDATA[The project aims to address the rising pedestrian fatalities nationally by examining the geographic shifts in pedestrian fatalities and injuries and their relationship with neighborhood demographic changes, police enforcement, and social equity patterns. The research team will analyze multi-year crash and injury data from various regions and collaborate with police departments to study the correlation between police enforcement and collision rates. The project will produce a technical report, interactive data visualization tools, and recommendations for safety treatments and equitable police enforcement.]]></description>
      <pubDate>Wed, 23 Aug 2023 20:51:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2232673</guid>
    </item>
    <item>
      <title>Creating a Data Resource of California Police Stops for Use in Traffic Safety Applications</title>
      <link>https://rip.trb.org/View/2229363</link>
      <description><![CDATA[Traffic stops are one the most common ways in which the American public interacts with police. Although one of the leading reasons given for police traffic stops is a violation of the vehicle code, there is limited and mixed research on the impact of traditional police traffic enforcement on traffic safety outcomes. At present, few large data resources with an appropriate level of detail exist to facilitate investigations of this type. The 2015 Racial and Identity Profiling Act (RIPA) requires all law enforcement agencies in California to collect and submit vehicle (including bicycle) and pedestrian stop data to the State Department of Justice annually, starting no later than 2022. This project will use 2018-2022 confidential RIPA stop data to categorize all police traffic stops using known risk factors for fatal and severe collisions and to create new variables relevant to traffic safety, yielding a standardized statewide data set useful for examining and controlling for police traffic stops as they relate to traffic safety outcomes. Further, the research team will both establish clear guidance for how to process RIPA data efficiently for future data releases and will also will geospatially join the processed RIPA data files with traditional transportation and land use data sources using stop location so that this data resource can be made available to others for future research. ]]></description>
      <pubDate>Thu, 17 Aug 2023 08:10:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2229363</guid>
    </item>
    <item>
      <title>Subdural Hematoma Injury Risk Curve Development for Older Occupants</title>
      <link>https://rip.trb.org/View/2050298</link>
      <description><![CDATA[Because the proportion of older people in the US population is increasing, and older occupants are more likely to become injured and die from vehicle crashes compared to younger occupants, improving safety in vehicles for older occupants has become an important focus. Among older occupants, serious head and thorax injuries are the most frequent and life-threatening. This project addresses subdural hematoma, which becomes more frequent and more life-threatening with age. This project aims to provide human head kinematic and brain motion data during crash level severity tests to be used in development of an injury risk function for subdural hematoma.]]></description>
      <pubDate>Tue, 25 Oct 2022 10:24:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2050298</guid>
    </item>
    <item>
      <title>Incorporating Driver Behavior and Characteristics into Safety Prediction Methods</title>
      <link>https://rip.trb.org/View/1996243</link>
      <description><![CDATA[Driver behavior and characteristics are influential contributing factors to traffic crashes, but current safety analysis tools primarily incorporate infrastructure-related factors affecting crashes. The lack of behavior and characteristic information can create a problem when considering safety applications since some of the most important factors are not included. This could lead to safety solutions that may not work as well as intended. In the AASHTO Highway Safety Manual (HSM), the measures of driver characteristics are divided into several categories such as attention and information processing, vision, perception-reaction time, and speed choice. However, these characteristics are provided at a very high level. Police officers usually report driver characteristics such as gender, age, speeding, blood alcohol content, seat belt use, and distracted driving. While several studies have evaluated the impact of these factors on crash severity, there is a need to incorporate these factors in crash prediction methods to achieve a better picture of the true potential effects on crash severity and frequency for decisions in planning, design, and operations. Research is needed to develop a methodology to incorporate a variety of factors related to driver behavior and characteristics into crash prediction methods to allow for a more comprehensive assessment of existing and expected safety performance, for use in design and operational decision-making, and incorporation into the AASHTO HSM and other safety tools and guidelines.

OBJECTIVE: The objective of this research is to continue and complete the work begun under NCHRP Project 22-47 to develop a methodology to incorporate driver characteristics and behavior into safety prediction methods to estimate the expected crash frequency and severity related to infrastructure features for use in planning, design, and operational decisions.

TASKS: Phase I: (Task 1) Update Literature Review - An amplified research plan (ARP) will be prepared and uploaded to the CRP project management website for review within 15 calendar days after the effective date of the executed contract. The research team shall present to the Panel via a virtual kick-off meeting to be arranged via the TRB program officer. The research team shall prepare a draft meeting agenda and presentation slides in advance of the meeting. The research team shall gather feedback from the Panel and revise the ARP to incorporate and reflect this input. The research team shall submit the ARP along with notes from the kickoff meeting two weeks after the meeting.

The research team shall update the literature review developed under NCHRP 22-47. The literature review for NCHRP 22-47 was comprehensive, so in Task 1, the research team shall focus on two aspects of the literature on driver behavior: (1) how different driver behaviors influence crash risk, and (2) how driver behavior is influenced by roadway infrastructure.

The research team shall also develop a matrix of known relationships between driver behavior and crashes based on previous literature and team expertise. The research team shall identify gaps in this knowledge and a literature review will be conducted to fill in these gaps. The literature review shall inform the selection of driver behaviors and facility types to target in the work plan.

(Task 2) Propose Work Plan - The research team shall develop a proposed work plan for creating and validating the predictive methodology to estimate crash frequency and severity using driver behavior data as input. The proposed work plan must be achievable within the available time and budget, which requires high confidence that data can be acquired and models fit in a relatively short timeframe. 

The research team shall summarize the planning work completed via Tasks 1 and 2 as Technical Memorandum 1. The Technical Memorandum 1 shall be submitted within 3 months of project initiation. Within one month of submission of the Technical Memorandum 1, the research team shall host an in-person meeting with the NCHRP project panel to discuss the technical memo. The discussions shall include results of the research completed to date and the work plans for the remainder of the research. Work on Phase II of the project will not begin until authorized by the NCHRP. 

Phase II: (Task 3) Develop a Validated Predictive Methodology - The research team shall identify statistical methods that can be used to incorporate driver behavior into the HSM.  The research team shall examine the basic logic of the HSM’s approach and extend it to account for driver behavior measured in the field. The research team shall also assess statistical methods used to develop SPFs and CMFs as well as methods that might be used to develop CMFs that incorporate driver behavior data.

(Task 4) Develop Spreadsheet Tool(s) - The research team shall develop a spreadsheet tool to implement the methodology. The spreadsheet tool shall be usable with Microsoft Excel or Google Sheets and shall include step-by-step instructions and an easy-to-use interface.

To ensure the accuracy of the spreadsheet implementation, the research team shall produce a number of sample results using statistical software and compare them to results from the spreadsheet tool. The research team shall follow examples from previous similar products to identify recommended practices and use the selected examples as a baseline to ensure consistency across spreadsheet tools that practitioners may use.

(Task 5) Prepare Datasets and Data Dictionaries - The research team shall execute the data collection plan identified in Task 2 and prepare the dataset and data dictionaries. 

(Task 6) Develop Calibration Factors or Functions - The research team shall develop calibration factors to adjust the Task 3 models for specific conditions at any given location. This may include the development of:

Traditional calibration factors, such as the method in the 1st edition of the HSM to apply to the SPFs developed in this project, or
Development of a spreadsheet tool that can use SPF predictions (e.g., HSM or other existing SPFs), observed crashes, and the factors analyzed in this project (e.g., operating speed variables) and use these to run a simple optimization to estimate a local calibration factor that incorporates the included variables (i.e., a calibration function). 
Detailed guidance for using both calibration methods described above shall be provided. Examples using crash data from this project shall be used in the guidance/training materials for calibration.

(Task 7) Ingest New Network Speed Data - The research team shall obtain new network speed data, link to the existing dataset of rural two-lane highway road segments, and create metrics from those data for use in prediction models. The dataset to be ingested shall be the National Performance Management Research Dataset (NPMRDS). If NPMRDS cannot be obtained, the research team shall purchase additional data from a third party data vendor to increase the existing analysis dataset sample size. For either dataset, the data shall be linked to the existing road segments through map-matching. The metrics to be developed shall emphasize high percentiles of speed and variability in speed in each segment. In addition, the metrics shall consider time of day, especially with the goal of eliminating rush-hour traffic when speeds may be constrained by congestion. The completion of linkage and metrics development will result in an analysis-ready dataset with available network-speed predictors for modeling.

(Task 8) Conduct Network Speed Analyses - The research team shall conduct analyses (additional to Task 3) using the Task 7 dataset. The approach to these analyses shall be to predict crash count as a function of the existing HSM model and the different speed metrics. The team shall assess model fit and conduct validation assessments. The validation assessments shall measure how much additional predictive power is added to the model by the inclusion of network speed. Models shall be run for all of the crash types covered in Task 2 (within limits of sample size) for rural two-lane highways.

(Task 9) Final Deliverables - The research team shall submit draft final deliverables three months prior to the end of the subaward. The draft deliverables shall include: (1) a draft final report with the methodology to incorporate driver characteristics and behavior into safety prediction methods to estimate the expected crash frequency and severity related to infrastructure features for use in planning, design, and operational decisions;  (2) a spreadsheet tool to support implementing the proposed methodology; (3) recommendations for additional research; and (4) a stand-alone technical memorandum titled “Implementation of Research Findings and Products.” ]]></description>
      <pubDate>Tue, 19 Jul 2022 14:24:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/1996243</guid>
    </item>
    <item>
      <title>Impact of Autonomous Vehicle (AV)-Based on Demand Transportation Services on Traffic Crashes</title>
      <link>https://rip.trb.org/View/1948953</link>
      <description><![CDATA[There has been two recent major advancements in the area of transportation industry which facilitated the mobility across the nation. The first technological advancement has brought Autonomous Vehicles (AVs) into reality, and the other major advancement is the emergence of on-demand ride services which has overcome many of the conventional travel barriers and improved personal mobility options. Although some studies explored the benefits and positive outcomes of both AVs and on-demand services; however, there are some other studies which found different outcomes and do not recommend the technology and service for safety reasons. Therefore, there is a need to study the impact of AV-based on-demand services on crashes as this will be future of the transportation systems in various cities and states. This study aims to evaluate the impact of AV-based on demand transportation services on traffic crashes with different severity levels. This project will analyze the (1) impact of the AV-based on-demand transportation services on frequency (number of) of crashes, (2) impact of the AV-based on-demand transportation services on total number of injuries, and (3) impact of the AV-based on-demand transportation services the number of serious injuries to analyze the crash patterns before and after the deployment of these services. To successfully achieve the objectives of this project, this study will use the data from City of Arlington, Texas Department of Transportation (TxDOT), and Arlington RAPID project. Arlington RAPID project is an implementation case study which has integrated the AVs into on-demand transportation services in Arlington, Texas. This study will use Difference in Difference, Time Series analysis, and Multivariate Regression modeling to evaluate the safety aspects of this integration and develop models for planners and policy-makers to estimate the changes in the number and severity of the crashes in their area while integrating AVs into their on-demand public transportation services. The results will be implemented in three other cities with different congestion and populations. The outcomes of this study will help with the planning for future deployment of AVs and integration of them into on-demand transportation services.]]></description>
      <pubDate>Mon, 09 May 2022 11:04:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/1948953</guid>
    </item>
    <item>
      <title>Impact of COVID-19 Pandemic on Road Safety in Region 6</title>
      <link>https://rip.trb.org/View/1948658</link>
      <description><![CDATA[COVID-19 was declared a pandemic since March 2020. Different state and local agencies
and private employers introduced unprecedented public health measures to contain and reduce
its spread and protect the public. These measures included closures of government offices,
businesses, major factories, and educational institutions. As a results, driving patterns and
behaviors in the United States changed significantly during the COVID-19 pandemic. An analysis
of traffic patterns during this period has identified that reduction in the miles traveled has a
significant negative correlation with COVID-19 cases and deaths across the USA. Preliminary
statistics in the US suggest an increase in fatal crashes over the period of the lockdown in
comparison to the same period in previous years. Moreover, while total crashes are down, motor
vehicle crashes involving non-motorists became more prevalent. At the same time, non-motorist
traffic has increased while motor vehicle traffic and crashes have shifted to the local systems.
The main goal of this research is to perform a comprehensive evaluation of the changes
in travel patterns, and crash risk factors and severity during COVID-19 pandemic and compare
crash characteristics to those immediately before the pandemic at different temporal and spatial
levels. This includes developments of a database of crash reports in Texas, calculation of crash
counts and rates, acquiring traffic volumes, and identifying high risk and/or vulnerable groups
during the shutdown such as long-haul truck drivers, bicyclists, and pedestrians. The analysis will
also include the types of collisions, e.g., head-on, fender-bender, etc. Additionally, injury severity
analyses will be performed to understand the association between various crash factors and crash
outcomes before and after the lockdown order. This will be done to assess whether the factors
that influenced crash outcomes differed before and during the pandemic. Addressing the needs
of vulnerable road users requires that transportation agencies understand how their risks might
have changed during the COVID-19 pandemic. The impacts of the changes in mobility and travel
during the COVID-19 pandemic will be investigated through detailed analysis of their impact on
the spatiotemporal patterns of crashes in a number demographically different counties in Texas.
The results can provide useful lessons for road safety improvements during extreme
events that may require statewide lockdown, as has been done with the COVID-19 pandemic and
offers the opportunity for traffic safety professionals to plan appropriate countermeasures for a
new COVID-19 wave or even future pandemics. Moreover, the research findings are expected to
provide a data-driven foundation to prioritize road safety strategies in order to minimize the effects
of the COVID-19 pandemic on road safety.
In order to provide an efficient solution to the research
problem, the research team aims to carry out the tasks outlined below:
(1) The research team will first undertake a thorough review of published literature on
COVID-19 pandemic impacts and related safety countermeasures, a review of
changes in traffic patterns, speeds and times, an analysis of changes in crash and
injury counts and rates, and a survey of transportation officials. Available past
research and reports of a related nature, from Texas, Region 6, across the nation, and
internationally, will be reviewed. Some of these resources will be listed and individually
described elsewhere in this proposal.
(2) The research team will compile operational and safety data from sources such as the
Texas Crash Records Information System (CRIS), the national Fatality Analysis and
Reporting System (FARS) for other states of Region 6, which has far more specialized
detail on fatal crashes, Texas Department of State Health Services records, and crash
narratives.
(3) The research team will use data mining to examine space-time indicators that may
reveal information about the correlation between changes in crash counts and severity
and traffic volumes, common characteristics of the built environment that contribute to
unsafe actions and conditions, and other factors.
(4) The research team will use different data collection techniques to understand road
user’s behavior and review crash narratives and diagrams. These observations will
not only be helpful in the analysis of risk factors, but also provide a framework that
guides decision-making throughout the entire process, from identifying a problem to
implementing a countermeasure.
(5) The research team will employ spatial analysis techniques including Geographic
Information System (GIS)-based methods to visualize the spatial differences in crash
density and crash severity patterns between and pre-pandemic and COVID-19
pandemic periods, non-parametric statistic methods (such as Kruskal–Wallis) to
examine whether the changes in crash densities and severity are statistically
significant, and models, such as the negative binomial regression-based approach, to
identify the significant socio-demographic and traffic-related factors contributing to
crash count and severity changes during the COVID-19 pandemic.]]></description>
      <pubDate>Fri, 06 May 2022 15:53:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/1948658</guid>
    </item>
    <item>
      <title>COVID-19 and Traffic Safety: Role of Infrastructure and Exposure</title>
      <link>https://rip.trb.org/View/1904961</link>
      <description><![CDATA[At the time of this writing, the COVID-19 pandemic continues to intensify, with 106,314,695
confirmed cases and 2,320,720 deaths spread across nearly every country on Earth. Early
lockdowns largely kept people at home, thereby reducing traffic levels. Theoretically, reduced
traffic exposure should result in reduced motor vehicle crashes. However, a variety of factors
complicate this relationship. Empty streets provide increased opportunity for speeding and
aggressive driving behavior. More consumption of alcohol and drugs during lockdowns could
translate to more driving while intoxicated. Public transit trips have decreased by 85% in some
cities, with many of these trips converting to less-safe personal vehicles. Similarly, higher levels
of vulnerable road users on the streets may lead to worse safety outcomes. Emergency services
were stretched thin, so enforcement and emergency response times to motor vehicle collisions
may have decreased in some locations.
The overall goal will be to understand how the COVID-19 pandemic has impacted travel behavior,
collision frequencies, and collision severity. The research team has three initial hypotheses. First, overall
decreases in traffic exposure have resulted in decreases in crash frequency but increases in crash
severity. However, these increases in injuries and fatalities will not be distributed evenly.
Therefore secondly, large and fast roadways and networks based on the functional classification
system will have experienced worse safety outcomes while traditional networks with smaller
roadways will have experienced improved outcomes. Third, the impacts of roadway design and
networks will outweigh the impacts of other contributing factors or changes to exposure.
This project will consist of three analyses. First, the team will perform a national analysis of motor
vehicle fatalities using Fatality Analysis Reporting System data. The team will compare 2015-2019
fatalities to those that occurred in 2020. Limited variables are available on the national scale, but
the team anticipates using FARS attributes (including drug/alcohol involvement, contributing factors, and
travel mode), census data (including urban/rural, household income, and population density), and
road networks (while the team can analyze network connectivity on the national level, the team will not be
able to analyze speed limits or functional classification). The purpose of this national analysis is
to identify variables of interest for the second and third models.
The second analysis will focus on three states (to be determined based on national results
and data availability). While the team will explore the same variables as before, the critical benefit of
this analysis is that the team will also analyze non-fatal collision trends. This will inform them of whether
collision frequencies and/or severities have changed. The team will also examine spatial clustering to
guide site selection for the third analysis.
The third analysis will analyze one local region. While the team will analyze the same variables as
above, the critical benefit of the local analysis is that the team will incorporate exposure into the models.
The team anticipates obtaining exposure data from aggregated cell phone data. The team will also incorporate
detailed factors that would be difficult to account for on a larger scale such as roadway functional
classification, posted speed limit, number of lanes, and land use. The team will identify collision hotspots
and compare them to areas where collisions have not increased to understand which factors have
contributed to traffic safety outcomes.
Findings will be important in two regards. First, they may inform actions for future epidemics,
pandemics, economic downturns, and natural disasters. For example, if the team finds  that drug/alcohol
involvement has increased, the team may be able to better prepare treatments for similar future events.
Second, findings will reveal the intrinsic safety of street and network typologies. This body of
research has developed significantly over recent decades and findings will provide important
contributions to the body of knowledge, providing benefits even outside of pandemics.]]></description>
      <pubDate>Thu, 20 Jan 2022 14:38:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/1904961</guid>
    </item>
    <item>
      <title>Effectiveness of Safety Countermeasures on Dockless E-Scooter Crashes</title>
      <link>https://rip.trb.org/View/1881795</link>
      <description><![CDATA[E-scooter rentals have become available in almost every US city over the last two years.  The City of Austin and the University of Texas campus are now served by 10 different private sector vendors providing over 14,000 e-scooters.  One of the first significant studies of e-scooter safety was done in Austin during the Fall of 2018.  That study examined crash data for e-scooters, conducted interviews of e-scooter crash victims and provided basic characterizations of e-scooter crashes and rider injuries. One of the primary concerns about e-scooter safety stems from the speeds that can be developed by a rider.  Based upon safety concerns for riders and pedestrians, the University of Texas implemented an agreement with e-scooter vendors to electronically reduce maximum e-scooter speeds to 8 mph on most of the campus.  
The research question to be examined in this study is whether the mandatory speed reduction has had any impact on the number of scooter involved crashes and their severity.  The research team surveyed campus scooter renters during the spring 2019 semester to characterize e-scooter user habits.  The survey surprisingly indicated that nearly all scooter rentals are done for “business” use such as traveling to classes, meetings or work.  Most trips are relatively short distances and as Morano determined almost no renters wear head protection. Potential speeds for e-scooters can be well more than 20 mph particularly when negotiating a down-hill grade. This study will compare crash frequency and injury severity for e-scooter crashes before and after the implementation of the 8 mph maximum speed on the UT campus. 
The objective of this project is to characterize the impact of mandatory speed reduction on e-scooter crash frequency and injury severity. The proposed work will address at least two CAMMSE research thrusts: Generate innovations in multi-modal planning and modeling for high-growth regions; and Innovations to improve multi-modal connections, system integration and security.
]]></description>
      <pubDate>Mon, 04 Oct 2021 11:42:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/1881795</guid>
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