Safe-D: Safety through Disruption

Webinar Archive

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Recording Title/Project/Date Speaker Webinar Overview
Link (YouTube) Title: Autonomous Delivery Vehicle as a Disruptive Technology: How to Shape the Future with a Focus on Safety?

Project: Safe-D 05-087

Date: October 20, 2022

Subasish Das, Ph.D.
Subasish Das, Ph.D.,
Texas A&M Transportation Institute
The National Highway Traffic Safety Administration (NHTSA) recently granted permission to deploy low-speed autonomous delivery vehicles (ADVs) on roadways. Although the mobility of ADVs is limited to low-speed roads and these vehicles are occupantless, frequent stops and mobility among residential neighborhoods cause safety-related concerns. There is consequently a need for a comprehensive safety impact analysis of ADVs. This study examined the safety implications and safety impacts of ADVs by using novel approaches. This research prepared several datasets such as fatal crash data, aggregated ADV trips and trajectories, and real-world crash data from the scenario design for an ADV-related operational design domain (ODD). Association rules mining was applied to the datasets to identify significant patterns. This study generated a total of 80 association rules which provide risk patterns associated with ADVs. The rules can be used as prospective benchmarks to examine how these rule-based risk patterns can be reduced by ADVs that replace human-driven trips.
Link (YouTube) Title: Smart Work Zone System

Project: Safe-D VTTI-00-036

Date: September 29, 2022

Mike Mollenhauer
Mike Mollenhauer,
Virginia Tech Transportation Institute
Jean Paul Talledo Vilela
Jean Paul Talledo Vilela,
Virginia Tech Transportation Institute

Will Vaughan
Will Vaughan,
Virginia Tech Transportation Institute

Currently, there are limited products to alert work zone workers available in the market. Those devices were mostly passive and did not actively track work zone workers’ activity or position within the activity area. VTTI and VTRC worked together to research a system that helps to alert workers using different HMI outputs including lights, audio, and vibration factors to address the sensory challenges present in different work zone environments.

As such, this project aimed to develop a Smart Work Zone System, including the addition of a C-V2X Base Station linked to Smart Vests and an array of Smart Cones. The C-V2X Base Station acts as the core of the system by communicating with the vests and cones. CAVs approaching a work zone can communicate with the Base Station over a 4G-LTE network to receive information regarding the location and configuration of the work zone. The Smart Vests, worn by road workers, transmit information regarding worker location to the base station.

The vests also provide auditory, visual, and tactile feedback to alert the wearers of a potential collision threat or work zone boundary crossing. The Smart Cone array is an add-on component that can be attached to work zone drums or cones to define the boundaries of the work zone for the Smart Vest and extend the wireless link with the Base Station. This Smart Work Zone System has tremendous potential to improve road construction safety by increasing worker and vehicle awareness of threats in the work zone.

Link (YouTube) Title: ADS Sensor Degradation Testing and Modeling

Project: Safe-D VTTI-00-034

Date: August 19, 2022

Steven Huggins
Steven Huggins,
Global Center for Automotive Performance Simulation
Autonomous vehicles rely on sensors to feed information about the surrounding environment to the system CPU to allow for real-time object avoidance and path planning. Tier 1 sensors consist of LiDAR, radar, visual cameras, ultrasonic, and global navigation satellite system (GNSS) sensors. The sensor information can be affected by various forms of degradation such as weather and information corruption due to a cyberattack. This webinar will review techniques used to characterize sensors with and without weather effects. Test data of Radar and Lidar sensors taken in multiple situations such as static objects and dynamic objects will be shown. Varying rain intensity applied at VTTI’s Smart Road is one of the degradations applied to the sensor. From the raw collected data, post-processing techniques to characterize the sensor and the effects of the degradation will be detailed. This post-processed data was used to create empirical models for use in simulations. A review of the model creation steps will be included. Lastly, an example of the use of the models for creating detection algorithms will be shown.
Link (YouTube) Title: Improving Methods to Measure Attentiveness through Driver Monitoring

Project: Safe-D 05-091

Date: June 17, 2022

Eileen Herbers
Eileen Herbers,
Virginia Tech Transportation Institute

Marty Miller
Marty Miller,
Virginia Tech Transportation Institute

Jacob Walters
Jacob Walters,
Virginia Tech Transportation Institute

Distracted driving is a huge problem today contributing to many crashes and vehicle fatalities every year. This project focused on identifying driver distraction using Driver Monitoring System data and vehicle kinematic parameters. The research team developed a set of benchmark driving events and reviewed them to determine drivers’ distraction levels. A series of algorithms were developed and assessed using these benchmark events, including both buffer-based attention algorithms and deep neural networks based models. This seminar will discuss the creation of benchmark events and the challenges associated with identifying driver distractions manually. Also included in the topics will be the development and performance of the algorithms in question as well as a discussion of some key points to take into account when trying to assess driver attentiveness using DMS and vehicle kinematic data./td>
Link (YouTube) Title: A Sensor Fusion and Localization System for improving Vehicle Safety in Challenging Weather Conditions

Project: Safe-D 04-117

Date: March 11, 2022

Dr. Vamsi Vegamoor
Dr. Vamsi Vegamoor,
Texas A&M Transportation Institute

SAE Level 5 autonomy requires the autonomous vehicle to be able to accurately sense the environment and detect obstacles in all weather and visibility conditions. This sensing problem becomes significantly challenging in weather conditions which include sudden change in lighting, smoke, fog, snow, and rain. There is no standalone sensor currently in the market that can provide reliable perception data in all conditions. We demonstrate that a combination of Long Wave Infrared (LWIR) cameras with radar provide a viable sensing system that is robust to adverse visibility conditions. We have validated this prototype system both in simulation as well as with real-world traffic using a 2017 Lincoln MKZ in College Station, TX./td>
Link (YouTube) Title: Crash Compatibility of Automated Vehicles with Passenger Vehicles

Project: Safe-D 05-098

Date: February 24, 2022

Dr. Chiara Silvestri Dobrovolny,
Dr. Chiara Silvestri Dobrovolny,
Texas A&M Transportation Institute

Automated Vehicles have been one of the most sought-after concepts to make transportation more effective and safer. One such class of vehicles is the no-occupant vehicles with automated driving systems (ADS), which are primarily intended for goods transportation services. This vehicle class presents a body structure different than that of a passenger vehicle. Yet, these no-occupant automated vehicles are sharing the roads and could potentially be involved in crashes with passenger vehicles. Occupant safety can be compromised if vehicles are not compatible from a crashworthiness perspective. ADSs vehicles should consider appropriate vehicle crashworthiness compatibility given the potential for interactions with vulnerable road users and other vehicle types. Investigation of the level of automated vehicle crashworthiness compatibility with human-driven vehicles can lead to more appropriate vehicle designs, as well as more suitable and better passive protection systems for occupants in such crash scenarios. This research project considers finite element crash computer simulation investigation between ADS and passenger vehicles with the intent to provide a better understanding of the differences in crashworthy behavior of ADS vehicles.
The objective of this research was to Investigate the crash compatibility of no-occupant automated vehicles of various class with passenger vehicles and to study a potential impacting location to evaluate crashworthiness criteria critical for vehicles with no crumple zone.Finite element computer simulations were performed which were developed and used for LS-Dyna multiphysics simulation software. Front and Side Impact locations were selected to measure intrusion in the occupant compartment and time history data for the accelerometer node was used to evaluate occupant injury criteria.
Link (YouTube) Title: An Intelligent Visual Discovery Mapping Platform for Massive Heterogeneous Datasets

Project Site: https://urbands.github.io/

Date: January 24, 2022

Xinyue Ye,
Xinyue Ye,
Texas A&M Transportation Institute

Jeremy Sudweeks,
Jeremy Sudweeks,
Virginia Tech Transportation Institute

Data reuse is becoming critical in a wide spectrum of science. For example, thousands of results of scientific publications on the topic of COVID-19 have emerged within a short time, raising significant challenges for scholars to organize the research, or even synthesize the knowledge in a timely or comprehensive manner. Dataverse is a platform for sharing and archiving research data, initially focused on social sciences, but now extended to other research communities. However, most researchers only download and reuse datasets that they already know about, but have difficulties in exploring and discovering the unknown. This vast amount of research data, which keeps increasing day by day, could have huge potential for providing insights to advance science if researchers knew how to find the data that they might be interested in. An Intelligent Visual Discovery Mapping Platform would enable data search through a Web-based interface consisting of a rich set of visualizations and interactions in multidimensional visualization, semantic visualization, network visualization, and geospatial visualization. Users can customize the analyses and visualizations by specifying different models and parameters. The semantic map visualizes research topics based on customized natural language processing algorithms, which helps users to identify their content of specific interest beyond keyword searches in web search engines. The resultant geospatial map extracts all the location names mentioned in the data. The implementation of Dataverse datasets and Kaggle Covid-19 publications will be illustrated as examples of the proposed Intelligent Visual Discovery Mapping Platform.
Link (YouTube) Title: Data Fusion for Non-Motorized Safety Analysis

Project: Safe-D 03-049

Date: November 18, 2021

Dr. Ipek Sener
Dr. Ipek Sener
Texas A&M Transportation Institute

Silvy Sirajum Munira
Silvy Sirajum Munira
Texas A&M Transportation Institute

Nonmotorized activity, despite sharing a low percentage of total trips in the United States, contributes to a disproportionate share of total fatal and serious injury crashes. The exigency of the issue has alarmed safety advocates in multiple areas, resulting in their persistent efforts to explore and develop evidence-based, data-driven strategies to reduce nonmotorized crashes. However, such efforts are often stonewalled due to a lack of robust and reliable exposure measures. This webinar presents a SAFE-D research study, which explored an emerging research territory, a fusion of nonmotorized traffic data for estimating reliable and robust exposure measures. The research was divided into three sequential stages. The first stage involved developing and applying a guideline to process and homogenize available data sources to estimate annual average daily bike volume at intersections. The research team selected the City of Austin as the study area, gathered five bike data sources (using both traditional and crowdsourced data sources) and developed bike demand models to be used as inputs into the fusion mechanisms developed as part of the study. The second stage was focused on developing and applying the fusion framework—demonstrating the efficacy of multiple fusion algorithms, including two novel mechanisms, suited to the data characteristics and based on the availability of actual counts. The analysis of actual and simulated data illustrated that the fusion methods outperformed the individual estimates in most cases. In the third stage, the fused data were applied in both macro (hot-spot analysis in block group level) and micro (individual safety-related perception) models in Austin to ascertain the significance of incorporating exposure in safety analysis. While the fusion framework contributes to the research in the field of decision fusion, the demand and crash models provide insights to help stakeholders formulate policies to encourage bike activity and reduce crashes.
Link (YouTube) Title: Reference Machine Vision for ADAS Functions

Project: Safe-D 04-115

Date: October 15, 2021

Abhishek Nayak, Ph.D. Candidate
Abhishek Nayak, Ph.D. Candidate
Texas A&M Transportation Institute

The objective of this project is to develop a reference system for evaluating different lane markings and perception algorithms. This project validates the effectiveness of different types of lane markings for detectability on state-of-the-art lane detection (LD) algorithms. An in-depth study into the different parameters affecting the performance of LD algorithms was conducted by incorporating pavement marking material characteristics into the evaluation framework. The effect of environmental factors (Day vs Night), driving direction, lane marking material characteristics (reflective properties like Qd/RL, marking quality), lane making layouts (30ft gap vs 40ft gap, 4inch wide vs 6 inches wide), and LD evaluation characteristics (Type of LD algorithm, Near Field-of-view (FOV) vs Far FOV) were studied. Observations were made on how these different factors interact with each other and affect LD performance. Three different annotated image datasets were also generated which includes the (1) College Station Dataset (On-road with Material data), (2) 3M panel dataset (Closed course with material data), and (3) US290 Dataset (On-road special type of markings without material data). These datasets can be used as a reference/benchmark system by researchers to evaluate their LD algorithms and infer how their performance relates to different types of lane markings and their material characteristics. In this presentation, we will present an overview of the project, the methods used and the results obtained in this project.
Link (YouTube) Title: Evaluation of a Narrow Automated Vehicle-Exclusive Reversible Lane on an Existing Smart Freeway

Project: Safe-D 04-101

Date: September 29, 2021

Dr. Sahar Ghanipoor Machiani
Dr. Sahar Ghanipoor Machiani
San Diego State University

Alidad Admadir
Alidad Admadi
Transportation Engineer/Planner

AVs are dependent on several sensors to recognize the surrounding environment and navigate the roadway. The operational features and logic of AVs are different from human-driven vehicles where operational decisions are made based on driver capabilities and behavioral characteristics. AVs’ lane-keeping capabilities could allow for infrastructure standard adjustments, such as narrower lanes, fewer lanes, and smaller and less signage, which could result in more efficient mobility. A full infrastructure adaptation to AVs will not take place quickly, especially given that the transportation system will be serving both AVs and human-driven vehicles for quite some time. Therefore, a mix of dedicated AV lanes and normal vehicle lanes seems to be a viable solution. This webinar shares results of recent research that evaluates implications of an innovative infrastructure solution, exclusive AV lanes, for safe and efficient integration of AVs into an existing transportation system. Examining a real-world case study, this project investigates implications of adding a narrow reversible AV exclusive lane to the existing configuration of the I-15 expressway in San Diego, resulting in a 9-foot AV reversible lane, and in both directions of travel, two 12-feet lanes for HOV and HOT vehicles. A series of tasks were completed, including a literature review, an AV manufacturers product review, expert interviews, a consumer questionnaire review, a crash data analysis, and a traffic simulation analysis. This webinar details these tasks with the main focus being on the traffic simulation part of the study.
Link (YouTube) Title: Driving Risk Assessment Based on High-frequency, High-resolution Telematics Data

Project: Safe-D VTTI-00-028

Date: August 26, 2021


Dr. Feng Guo
Virginia Tech Transportation Institute

The high-frequency, high-resolution telematics driving data provide valuable information on both long-term driver behavior as well as instantaneous driving conditions. This project uses telematics driving data to modeling driving risk with three primary objectives: 1) characterize the high-frequency kinematic signatures for safety-critical events; 2) modeling driver level crash risk prediction based on kinematics features, and 3) instantaneous crash risk assessment.

The research team proposes a state-of-the-art approach for characterizing the high-frequency kinematic signatures. We developed several features representing driver behavior and underlying driving risk. These features were applied to several large-scale ride-hailing data and naturalistic driving study data to predict driving risk.

The high-frequency kinematic data coupled with the rarity of crashes demand novel modeling approaches. We developed deep learning-based models and variational inference-based rare event modeling to predict crashes from normal driving as well as predict high-risk drivers. A convolutional neural network and long short-term memory network is developed to predict crashes, near-crashes, and normal stopping behaviors. We developed a novel Variational Information for Extremal (VIE) framework for modeling rare events through deep learning models.

This project addressed key methodological challenges in predicting driving risk using high-frequency telematics data. The findings of the project will benefit driving data processing at scale, driver safety management program, and real-time risk prediction.

Link (YouTube) Title: Development of a Connected Smart Vest for Improved Roadside Work Zone Safety

Project: Safe-D 04-104

Date: July 28, 2021

Dr. Nazila Roofigari-Esfahan
Dr. Nazila Roofigari-Esfahan
Virginia Tech

Mike Mollenhauer
Mike Mollenhauer
Virginia Tech Transportation Institute

Jean Paul Talledo Vilela
Virginia Tech Transportation Institute

Roadside work zones (WZs) present imminent safety hazards for roadway workers as well as passing motorists. In 2016, 764 fatalities occurred in work zones in the United States due to motor vehicle traffic crashes. A number of factors (aging highway infrastructure, increased road work, increased levels of traffic, and more nighttime WZs) have led to an increase in WZ crashes in the past few years. Consequently, WZs are becoming increasingly dangerous for workers as well as passing motorists. The standard work zone safety signage and personal protective equipment (PPE) worn by workers at roadside WZs have not been completely effective in controlling work zone crashes.

This project was an effort in addressing this problem through designing a wearable device to accurately localize, monitor, and predict potential collisions between WZ actors based on their movements and activities, and communicate potential collisions to workers, passing drivers, and connected and automated vehicles (CAVs). The project developed a prototype of a wearable worker localization and communication device (i.e., Smart Vest) that utilizes the previously developed Threat Detection Algorithm (Safe-D project 03-050) to communicate workers’ locations to passing CAVs and proactively warn workers and passing motorists of potential collisions. The research successfully completed project objectives and developed a working prototype of a smart vest. The product was evaluated through multiple field experiments, to assess the capability of the vest in accurately localize workers and detect proximity hazards. As a result, this research is expected to significantly improve the safety conditions of roadside WZs through prompt detection and communication of hazardous situations to workers and drivers.

Link (YouTube) Title: Data Mining Twitter to Improve Automated Vehicle Safety

Project: Safe-D 04-098

Date: June 22, 2021


Dr. Tony McDonald
Texas A&M University

Dr. Bert Huang
Tufts University

Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. Prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs; however, the exact impact and dynamics of this influence are not well understood. This webinar will discuss a novel three-step process that not only measures this impact but also translates it into a set of guidelines for reporting on AV events designed to calibrate driver trust and expectations. The steps include the development of a novel search method to identify AV-relevant user comments on Twitter via a semi-supervised learning approach. A topic modeling and sentiment analysis of the identified tweets to analyze the influence of crashes and news events on user sentiment about AVs, and a mixed-methods analysis to translate these findings into guidelines for AV reporting.
Link (YouTube) Title: Examining Senior Drivers’ Adaptation to Mixed-Level Automated Vehicles

Project: Safe-D 04-103

Date: May 28, 2021


Dr. Jon Antin
Virginia Tech Transportation Institute

Advanced driver assistance systems (ADAS) may benefit senior drivers, reducing driving risk and compensating for diminished cognitive or other abilities. The degree to which such benefits can be realized depends on the facility with which drivers adapt to these features and the extent to which their use enhances mobility and driver performance. Phase I of this study included naturalistic and other data collection, investigating the attitude adaptation of eighteen senior drivers to driving ADAS- equipped vehicles for six weeks each. Participants included 18 men and women aged 70-79. The effect of exposure on driver confidence in and satisfaction with ADAS was measured.

Phase II involved the analysis of the naturalistic driving data collected during Phase I. Analyses included a comparison of mobility and safety-related kinematic data with similar data collected from older drivers in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS). The research team developed and employed an object detection algorithm to identify trips where Adaptive Cruise Control (ACC) was active. Kinematic data from these trips were compared with data from matched SHRP 2 trips and with data from Phase I trips where ACC was not activated.

Results indicated that ADAS-equipped vehicles may influence seniors’ driving performance both in positive as well as negative ways. Seniors generally displayed better speed management performance while driving the ADAS-equipped vehicles. However, less stable lateral control performance during ACC-use trips was also observed.

Link (YouTube) Title: Use of Disruptive Technologies to Support Safety Analysis and Meet New Federal Requirements

Project: Safe-D 04-113

Date: April 9, 2021


Ioannis (Yianni) Tsapakis
Texas A&M University


Subasish Das
Texas A&M University


Ali Khodadadi
Texas A&M University

States are required to have access to annual average daily traffic (AADT) for all public paved roads, including non-Federal aid system (NFAS) roadways. The expectation is to use AADT estimates in data-driven safety analysis. Because collecting data on NFAS roads is financially difficult, agencies are interested in exploring affordable ways to estimate AADT. The goal of this project was to determine the accuracy of AADT estimates developed from alternative data sources and quantify the impact of AADT on safety analysis. The researchers compared 2017 AADT data provided by the Texas Department of Transportation (DOT) and the Virginia DOT against probe-based AADT estimates supplied by StreetLight Data Inc. Further, the research team developed safety performance functions (SPFs) for Texas and Virginia and performed a sensitivity analysis to determine the effects of AADT on the results obtained from the empirical Bayes (EB) method that uses SPFs. The results showed that the errors stemming from the probe AADT estimates were lower than those reported in a similar study that used 2015 AADT estimates. The sensitivity analysis revealed that the impact of AADT on safety analysis mainly depends on the size of the network, the AADT coefficients, and the overdispersion parameter of the SPFs.
Link (YouTube) Title: Modeling Driver Behavior during Automated Vehicle Platooning Failures

Project: Safe-D 03-036

Date: February 23, 2021


Dr. Tony McDonald
Texas A&M University


Dr. Abhijit Sarkar
Virginia Tech Transportation Institute

The great promises of automated vehicles will only be realized if designers factor in human capabilities, which is challenging given the complexity of human behavior and its interactions with the surrounding driving environment. Driver process models offer a potential solution to this challenge because they can simulate realistic human responses to environmental conditions. These simulations can be used to calibrate design variables (e.g., following distance) to ensure a safety barrier. Despite the fact that driver models are frequently used to assess manual driving safety and advanced safety systems, there have been few attempts to extend these models to automated vehicle control transitions, particularly in platooning environments. The goal of this talk is to discuss a model development process to predict driver behavior following an unexpected prompt from the automated vehicle for a control transition. The discussion will introduce a model development approach that is grounded in contemporary cognitive theory, empirical findings, and actual on-road observations, and show that this process leads to accurate predictions of driver decisions, braking reactions, and steering behavior. The talk will close with a discussion of future directions and applications. After registering, you will receive a confirmation email containing information about joining the meeting.
Link (YouTube) Title: Implication of Truck Platoons for Roadside and Vehicle Safety Hardware

Project: Safe-D Project 01-006

Date: December 11, 2020


Dr. Chiara Dobrovolny
Texas A&M University


Dr. Costin Untaroiu
Virginia Tech

Platooning is an expansion of Cooperative Adaptive Cruise Control (CACC). It involves automated longitudinal and lateral control of vehicles moving in a tight arrangement and short following distances. Platooning, when deployed, is expected to improve fuel efficiency while maintaining occupant and roadside safety. It is unknown whether existing roadside safety devices are adequate to withstand a potential impact from an errant platoon. Additionally, the interactions of impacting trucks with roadside safety devices and the risks they pose to occupants are unknown as well.

This webinar described a strategy created to simulate multiple tractor-trailer impacts into roadside safety concrete barriers. This method was used to understand how existing concrete roadside safety devices will perform under multiple impacts due to errant truck platoons and to examine the associated injury risks for occupants within each tractor.

Finite element (FE) crash models were developed and validated against respective full-scale vehicle crash tests previously conducted at the Texas A&M Transportation Institute. Once validated, the sequential tractor-trailer collisions were simulated by using the outputs of each barrier impact as the inputs for the subsequent impact.

Next, the time history data for each node within the barrier simulations formed inputs for a detailed interior cabin simulation with occupant injury risks measured with validated Hybrid III/THOR dummy models. The Abbreviated Injury Scale (AIS) was used to classify the severity of occupant injury risk based on the simulation results. In addition, a seat position sensitivity study was performed to understand the risk severity for four different occupant seat rotation angles (−15°, 30°, 45° and 60°) to begin investigating the safety impacts of unconventional seating arrangements possibly enabled by automated systems.

Link (YouTube) Title: Analysis of an Incentive-based Smart Phone App for Young Drivers

Project: Safe-D Project TTI-01-01

Date: February 25, 2020

Russell Henk
Texas A&M Transportation Institute
Traffic crashes continue to be the leading cause of unintentional death and injury of youth across the United States. New and innovative interventions continue to be developed to address this public health issue for this high-risk driving population. This webinar will provide a variety of data associated with an incentive-based smartphone app developed by the Texas A&M Transportation Institute as part of the peer-to-peer safe driving program, Teens in the Driver Seat®. One of the core features of the app involves a reward system in which drivers earn points for miles driven without any phone interaction. The points can be redeemed for rewards and as a basis for competitions and achievement of safe driving levels. This project examined data collected from two distinct smartphone app deployments over the timespan of several months each – one in 2017 and one in 2018. The datasets include over 12,200 trips and more than 100,000 miles logged using the app. Statistical analyses were performed to assess the influence of incentives on the frequency of distracted driving. Statistically significant reductions in distracted driving (at the 95 percent confidence level) were shown to have occurred when incentives were awarded for distraction-free driving. Several other data of interest are presented herein as well. Topics discussed will also include lessons learned regarding the pros and cons of smartphone app deployment of this nature.
Link (YouTube) Title: Pavement Perspective on Automated Vehicle Safety Through Optimized Lateral Positioning Pattern

Project: Safe-D Project 02-008

Date: February 18, 2020

Fujie Zhou
Texas A&M Transportation Institute
Deployment of automated vehicles (AVs) has many benefits, such as reduction of congestion and traffic accidents, increased lane capacity, lower fuel consumption, increased transport accessibility, and reduced travel time and transportation costs. However, one aspect of AVs that is worthy of notable attention is their impact on risk of roadway hydroplaning and pavement life. Since most AVs are programmed to follow a set path and maintain a lateral position in the center of the lane, over time, significant pavement rutting will occur. This study directly measured AV lateral wandering patterns and then compared with human driven vehicles. It was found that wandering patterns of both AVs and human driven vehicles could be modeled with a normal distribution but have significantly different standard deviations. AVs wander laterally at least 3 times smaller than regular human driven vehicles. The influence of AVs (all AVs or mixed with regular human driven vehicles) on pavement rutting and fatigue life was analyzed with the Texas Mechanistic-Empirical Flexible Pavement Design System (TxME). The researchers discovered that the AVs with smaller lateral wandering would shorten pavement fatigue life by 22 percent. Meanwhile, pavement rut depth increases by 30 percent which leads to a much higher risk for AVs hydroplaning. The researchers also calculated the maximum tolerable rut depths at different hydroplaning speeds. It is noted that the AVs have a much smaller tolerable rut depth than the regular human driven vehicles due to greater water film thickness in the rutted wheel paths. To reduce the negative impact of AVs on roadway safety and pavement life, this research recommends an optimal AV wandering pattern: a uniform distribution. Not only does the uniform distribution eliminate the negative effect of AVs, but more importantly, it results in prolonged pavement life and decreased hydroplaning potential.
Link (YouTube) Title: Street Noise Relationship to Vulnerable Road User Safety

Project: Safe-D Project 02-027

Date: February 12, 2020


Greg Griffin
Texas A&M Transportation Institute
This webinar shares results of recent research that related bicycle crash rates to noise levels – measured from the bicycle handlebar in two cities. The study developed a method for evaluating street noise and documented crash rates for roadways in Austin, Texas, and Washington, D.C., in a manner that is replicable by researchers and practitioners. Researchers collected street-level noise in both cities over a range of locations, facility types, and times, and compared these against crash records, normalized by bicycle volumes, and other explanatory variables. Modeling explained 87% of the variation in crash risk in our Washington, DC Capital Area route, after controlling for infrastructure differences and nearby bicycle commute mode shares. Further explorations of street noise are needed to improve further guidance for transportation planning and design.
Link (YouTube) Title: Model Selection Heuristics based on Characteristics of Data & Rare Events Modeling

Project: Safe-D Project 01-001

Date: February 5, 2020


Ali Shirazi
Texas A&M University


Feng Guo
Virginia Tech Transportation Institute

Part 1: Model Selection Heuristics Based on Characteristics of Data. Transportation analysts usually employ post-modeling methods, such as Goodness-of-Fit statistics or Likelihood-based Ratio Tests for selecting the best distribution or model. These metrics require all competitive distributions or models to be fitted to the data before any comparisons can be accomplished. Given the continuous growth in introducing new statistical distributions, choosing the best one using such post-modeling methods is not a trivial task, especially given all theoretical or numerical issues the analyst may face during the analysis. Furthermore, and most importantly, these measures or tests do not provide any intuitions about why a specific distribution or model is preferred over another (Goodness-of-Logic). This presentation describes a methodology to design heuristics for Model Selection based on the characteristics of data, in terms of descriptive summary statistics, before the competitive models are fitted. The proposed methodology employs two analytic tools: (1) Monte-Carlo Simulations and (2) Machine Learning Classifiers, to design simple heuristics to predict the label of the ‘most-likely-true’ distribution for analyzing data.

Part 2: Rare Event Modeling. The rare event nature of crashes brings challenges in crash modeling and prediction. This study focuses on the following two aspects: 1) propose bias adjustment for more accurate estimation of the safety impact of a risk factor; 2) develop a decision-adjusted modeling framework to predict high risk drivers based on telematics data. The decision-adjusted framework optimizes predictive performance based on the objective of the study, e.g., top 1% of high risk drivers. In a case study, we developed an optimal driver level risk prediction model based on the telematics data (high G-force events) and driver demographic information using the SHRP2 NDS.

Link (YouTube) Title: Older Drivers and Transportation Network Companies: Investigating Opportunities for Increased Safety and Improved Mobility

Project: Safe-D Project 02-016

Date: January 23, 2020


Melissa Tooley
Texas A&M Transportation Institute
Transportation network companies (TNCs) such as Uber and Lyft offer an increasingly popular alternative to driving a personal vehicle. This project investigated the potential of TNCs to increase the safety and enhance the mobility of older adults who are experiencing a decline in driving ability. Interviews with commercial and non-profit transportation providers and focus groups of adults ranging from age 65 to over 85 identified attitudes and perceptions toward TNCs and related services targeting senior adults, as well as ongoing barriers to TNC use by this demographic. Barriers include insufficient familiarity and comfort with using smartphone applications, a lack of knowledge among older adults about how TNCs operate, and lack of availability of TNC services in many rural areas. Increased availability of TNC services targeted toward older adults may help to overcome some of these barriers. The project team developed outreach and education materials for older adults on how to access and use TNC services.