Safety through Disruption (SAFE-D) University Transportation Center (UTC)

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Safety Perceptions of Transportation Network Companies (TNCs) by the Blind and Visually Impaired
Simek, Christopher L.; Higgins, Laura L.; Sener, Ipek Nese; Moran, Maarit M.; Geiselbrecht, TIna S.; Hansen, Todd W.; Walk, Michael J.; Ettelman, Benjamin L.; Plunkett, Michelle (SAFE-D: Safety Through Disruption National University Transportation Center, 2018-10)
For individuals that are visually impaired, access to safe and reliable transportation can be a significant challenge. The limited menu of mobility options can culminate in a reduced quality of life and more difficulty accessing housing and employment, relative to sighted individuals. Transportation network companies (TNCs, or ridesharing companies) have emerged as a new mode of travel that has the potential to increase access to transportation for the visually impaired. The opportunities and challenges for TNC use by individuals with blindness or visual impairment has not been widely studied. The goal of this research is to use both qualitative and quantitative methods to identify how this community perceives the safety of TNCs relative to other travel modes, and how they utilize TNCs for safe travel. The findings suggest that TNCs are used by a significant proportion of this population. The findings also suggest that one’s experience (or lack thereof) with TNC use has a strong influence on the safety perceptions of this new mode of travel. Finally, while TNCs present an opportunity for riders that are visually impaired to become more engaged in myriad activities, there are still areas in which ridesharing companies can make improvements.
Exploring the Science of Retroreflectivity: Curriculum for Grades 4 through 6
Finley, Melisa D.; Hanover, Stephanie; Chrysler, Susan T. (SAFE-D: Safety Through Disruption National University Transportation Center, 2018-10)
According to the United States Department of Commerce, careers in science, technology, engineering, and mathematics (STEM) are growing faster than occupations in other areas. However, in-class academic concepts can seem abstract with little relevance to a student’s life. There is therefore a need for in-class curricula that links academic concepts with real-world STEM applications. Over the past 10 years, Texas A&M Transportation Institute (TTI) researchers have developed many educational activities for elementary and middle school students (K–8) that provide an opportunity to gain hands-on experience and insight into what transportation engineering and other STEM careers have to offer. In 2011, a TTI researcher taught approximately 300 fifth graders about the scientific principles of reflection, refraction, and retroreflectivity through a brief history of sign sheeting, hands-on activities, and a laboratory exercise. While these activities successfully engaged the students, it is not possible for one researcher to visit the numerous K–12 classrooms in their area, much less on a state- or nation-wide level. Therefore, TTI researchers created a curriculum and associated materials that can be used by teachers and other professionals to connect real-world applications in transportation to academic concepts to enhance the STEM learning experience for students.
Exploring the Science of Reflectivity: Curriculum for Grades 4 through 6
Finley, Melisa D.; Hanover, Stephanie; Chrysler, Susan T. (2018-10)
According to the United States Department of Commerce, careers in science, technology, engineering, and mathematics (STEM) are growing faster than occupations in other areas. However, in-class academic concepts can seem abstract with little relevance to a student’s life. There is therefore a need for in-class curricula that links academic concepts with real-world STEM applications. Over the past 10 years, Texas A&M Transportation Institute (TTI) researchers have developed many educational activities for elementary and middle school students (K–8) that provide an opportunity to gain hands-on experience and insight into what transportation engineering and other STEM careers have to offer. In 2011, a TTI researcher taught approximately 300 fifth graders about the scientific principles of reflection, refraction, and retroreflectivity through a brief history of sign sheeting, hands-on activities, and a laboratory exercise. While these activities successfully engaged the students, it is not possible for one researcher to visit the numerous K–12 classrooms in their area, much less on a state- or nation-wide level. Therefore, TTI researchers created a curriculum and associated materials that can be used by teachers and other professionals to connect real-world applications in transportation to academic concepts to enhance the STEM learning experience for students.
Sources and Mitigation of Bias in Big Data for Transportation Safety
Griffin, Greg P.; Mulhall, Meg; Simek, Christopher L. (SAFE-D: Safety Through Disruption National University Transportation Center, 2018-11)
Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognize that big data includes biases in who the data represents and accuracy related to transportation safety statistics. This study systematically reviews both the sources of bias and approaches to mitigate bias through review of published studies and interviews with experts. The study includes quantified analysis of topic frequency and evaluation of the reliability of concepts by using two independent trained coders. Results show a need to keep transportation experts and the public central in determining the right goals and metrics to evaluate transportation safety, in the development of new methods to relate big data to the total population’s transportation safety needs, in the use of big data to solve difficult problems, and to work ahead of emerging trends and technologies.
Street Noise Relationship to Bicycling Road User Safety
Griffin, Greg P.; Hankey, Steven C.; Simek, Christopher L.; Le, Huyen; Buehler, Ralph (SAFE-D: Safety Through Disruption National University Transportation Center, 2018-12)
Vulnerable road users, such as bicyclists, experience road noise directly. This study explored the relationship between bicycle crash risk and street-level road noise as measured in Austin, Texas and the Washington, D.C. metropolitan area, in addition to other factors. Construction and validation of a method to measure noise directly using consumer-accessible tools supports additional studies as well as potential public crowdsourcing applications for urban planning. Results from the two case sites were mixed. Street noise, as measured on our chosen routes, was not a consistent predictor of bicycle crash risk. However, our model explained over 87% of the variation in crash risk in the Washington, D.C. metropolitan area route, considering infrastructure, nearby bicycle commute mode share, and street noise. Findings from the two routes using our modeling approaches are not exhaustive, but rather an initial exploration of these relationships to support further work on the role of street noise in planning for safety.
Vehicle Operating Speed on Urban Arterial Roadways
Fitzpatrick, Kay; Das, Subasish (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-01)
This research explored (1) the relationship between suburban vehicle operating speed and roadway characteristics,especially the presence of bicyclists and (2) whether crowdsourced speed data could be used to estimate theunconstrained speed for a location. Both vehicle volume per lane and bicycle volume were found to be influential inaffecting average speed on lower speed urban arterial roadways. For 40.3 km/hr (25 mph) sites, an increase of 19vehicles per 15-min period would decrease average speed by 1.6 km/hr (1 mph), and an increase of more than 39bicyclists per 15-min period would decrease average vehicle speed by a similar amount. Because of the limited numberof 15-min periods with bicycle counts greater than 1, the research team also developed a model using all available 15-min periods with on-road speed data. Speed and volume data in 15-min increments for 2 weeks at nine sites wereobtained using on-road tubes and via a vendor of crowdsourced speed data. The difference between the tube data andthe crowdsourced data was calculated and called TMCS as a representation of tube (T) minus (M) crowdsourced (CS).The geometric variables that had the greatest influence on TMCS were the number of signals and the number ofdriveways within a corridor. When only including non-congested periods, weekends (Saturday or Sunday) wereassociated with the smallest TMCS.
Behavior-based Predictive Safety Analytics – Pilot Study
Engström, Johan; Miller, Andrew M.; Huang, Wenyan; Soccolich, Susan A.; Machiani, Sahar Ghanipoor; Jahangiri, Arash; Dreger, Felix; de Winter, Joost (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-04)
This report gives an overview of the main findings from the Behavior-based Predictive Safety Analytics – Pilot Study project. The main objective of the project was to investigate the possibilities of developing statistical models predicting individual driver crash involvement based on individual driving style, demographic and behavioral history variables, using large sets of naturalistic driving data. The project was designed as a pilot project with the objective of providing the basis for a future more comprehensive research effort. Based on Second Strategic Highway Research Program (SHRP2) data, a subset of behavior and crash data including 2,458 drivers was created for analysis. The data were analyzed to investigate to what extent these drivers were differentially involved in crashes and near crashes, to what extent this was associated with individual characteristics, and if it is possible to predict individual drivers’ crash and near crash involvement based on variables representing individual characteristics. The results clearly demonstrated the presence of differential crash and near crash involvement and showed significant associations between enduring personal factors and crash involvement. Moreover, logistic regression and random forest classifiers were relatively successful in predicting crash and near crash involvement based on individual characteristics, but the ability to specifically predict involvement in crashes was more limited.
Older Drivers and Transportation Network Companies: Investigating Opportunities for Increased Safety and Improved Mobility
Tooley, Melissa; Zmud, Johanna; Ettelman, Benjamin L.; Moran, Maarit M.; Higgins, Laura L.; Shortz, Ashley; Wheeler, Eric (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-06)
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.
Motorcycle Crash Data Analysis to Support Development of a Retrofit Concrete Barrier System for Freeway Ramps
Wilson, Jonathan; Sulaica, Heather; Dobrovolny, Chiara Silvestri; Perez, Marcie (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-07)
This project was intended to review the most relevant national and international studies, as well as protocols and standards that were developed to support motorcycle safety on roadways. In addition, crash data analysis was conducted to identify relevant factors involved with motorcycle accidents where the bike has impacted roadside safety barriers on flyovers/connectors or on curves. This crash data review was developed in support of an existing research project sponsored by the Texas Department of Transportation, which aims at identifying and testing retrofit options for existing concrete barriers to contain errant motorcycle occupants during an impact event.
Analyzing Highway Safety Datasets: Simplifying Statistical Analyses from Sparse to Big Data
Lord, Dominique; Geedipally, Srinivas Reddy; Guo, Feng; Jahangiri, Arash; Shirazi, Mohammadali; Mao, Huiying; Deng, Xinwei (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-07)
Data used for safety analyses have characteristics that are not found in other disciplines. In this research, we examine three characteristics that can negatively influence the outcome of these safety analyses: (1) crash data with many zero observations; (2) the rare occurrence of crash events (not necessarily related to many zero observations); and (3) big datasets. These characteristics can lead to biased results if inappropriate analysis tools are used. The objectives of this study are to simplify the analysis of highway safety data and develop guidelines and analysis tools for handling these unique characteristics. The research provides guidelines on when to aggregate data over time and space to reduce the number of zero observations; uses heuristics for selecting statistical models; proposes a bias adjustment method for improving the estimation of risk factors; develops a decision-adjusted modeling framework for predicting risk; and shows how cluster analyses can be used to extract relevant information from big data. The guidelines and tools were developed using simulation and observed datasets. Examples are provided to illustrate the guidelines and tools.
Driver Training Research and Guidelines for Automated Vehicle Technology
Manser, Michael P.; Noble, Alexandria M.; Machiani, Sahar Ghanipoor; Shortz, Ashley; Klauer, Charlie; Higgins, Laura L.; Ahmadi, Alidad (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-07)
The advent of advanced driver-assistance systems presents the opportunity to significantly improve transportation safety. Complex sensor-based systems within vehicles can take responsibility for tasks typically performed by drivers, thus reducing driver-related error as a source of crashes. While there may be a reduction in driver errors, these systems fundamentally change the driving task from manual control to supervisory control. A significant challenge, given this fundamental change in the driving task, is that there are no established methods to train drivers on the use of these systems, which may be counterproductive to safety improvements. The aim of the project was to develop training protocol guidelines that could be used by advanced driver-assistance system trainers to optimize driving safety. The guidelines were developed based on the results of three activities that included the development of a taxonomy of the knowledge and skills necessary to operate advanced driver-assistance systems, a driving simulator study that examined the effectiveness of traditional training protocols, and a test track study that examined the efficacy of a vehicle-based training protocol. Results of both studies suggest that differing training protocols are most beneficial in terms of driver cognitive load and visual scanning as opposed to short-term changes in performance.
Design and Evaluation of a Connected Work Zone Hazard Detection and Communication System for Connected and Automated Vehicles (CAVs)
Mollenhauer, Michael A.; White, Elizabeth E.; Roofigari-Esfahan, Nazila (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-08)
Roadside work zones (WZs) present imminent safety hazards for roadway workers as well as passing motorists. In 2016, 764 fatalities occurred in WZs in the United States due to motor vehicle traffic crashes, which are the second most common cause of worker fatalities. The advent of connected and connected automated vehicles (CVs/CAVs) is driving WZ safety practitioners and vehicle designers towards implementing solutions that will more accurately describe activity in WZs to help identify and communicate imminent safety hazards that elevate crash risks. A viable solution to this problem is to accurately localize, monitor, and predict WZ actors’ collision threats based on their movements and activities. This information along with CV/CAVs’ trajectories can be used to detect potential proximity conflicts and provide advanced warnings to workers, passing drivers, and CAV control systems. This project aims to address WZ safety by delivering a real-time threat detection and warning algorithm that can be used in wearable WZ communication solutions in conjunction with CVs/CAVs. As a result, this research provides a key element required to significantly improve the safety conditions of roadside WZs through prompt detection and communication of hazardous situations to workers and CVs/CAVs alike.
Examining Senior Drivers Adaptation to Mixed Level Automated Vehicles: A Naturalistic Study
Liang, Dan; Antin, Jonathan F.; Lau, Nathan; Stulce, Kelly E.; Baker, Stephanie Ann; Wotring, Brian (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-08)
Advances in the development of advanced vehicle technologies (AVTs), such as blind spot alerts, lane keep assist,lane alert, and adaptive cruise control, can benefit senior drivers by reducing exposure to hazards andcompensating for diminished cognitive abilities sometimes seen in this population. However, the degree to whichsuch benefits can be realized in this vulnerable population depends largely on the degree to which senior driverswill accept, adopt, and adapt to these features. This study investigated how 18 seniors, aged 70–79, accepted,trusted, and used mixed-function AVTs when provided an AVT-equipped vehicle to drive as they desired for a 6-week period. Researchers assessed attitudes and the effect of exposure via before-and-after exposure surveys, briefweekly check-in surveys during the driving exposure period, and focus group sessions conducted after theconclusion of the driving exposure period. Analyses revealed that seniors prefer technologies that inform, such asblind spot alert, over those that assert independent control over the vehicle, such as lane keep assist. Increasedconfidence in and willingness to use AVTs correlated positively with exposure, with adequate time for orientationand appropriate user documentation emerging as key factors determining senior drivers’ acceptance.
Emerging Legal Issues for Transportation Researchers Using Passively Collected Data Sets
Stoeltje, Gretchen; Moran, Maarit M.; Zmud, Johanna; Ramsey, Nijm; Stibbe, Jayson (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-08)
With the advent of new technologies to gather and process data, large data sets are being collected that are of interest to transportation researchers. However, legal and ethical questions around data ownership and protection in the context of emerging technologies, especially with regard to emerging automated and connected vehicle technologies, are still being formulated and addressed, but are not settled. This research compares the uses of primary and secondary, passively collected data sources to identify legal considerations affecting access to these data for transportation researchers. With privately sourced data becoming more prevalent, researchers are faced with additional duties and changing practices. This exploratory research aims to provide guidance to transportation researchers on the legal and ethical requirements for data protection.
Factors Surrounding Child Seat Usage in Rideshare Services
Owens, Justin M.; Womack, Katie N.; Barowski, Laura (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-09)
This project represents a collaborative, multimodal effort to understand the current state of child passenger safety with respect to rideshare vehicles, with the aim of using this information to develop an effective set of outreach tools. The project team included faculty and student members from the Virginia Tech and Texas A&M Transportation Institutes. Project phases included an in-depth review of the child passenger safety regulatory literature across the United States, a series of focus groups with rideshare riders and drivers, a nationwide internet survey of riders’ and drivers’ knowledge and attitudes toward child passenger safety, and the development of an informational website with a corresponding media outreach campaign. Researchers found that there is a general lack of knowledge of and awareness about the issues surrounding transporting children in this new transportation paradigm, and efforts must continue from both educational and regulatory perspectives to clarify in what ways parents and rideshare drivers can and must safely transport children.
Implications of Truck Platoons for Roadside Hardware and Vehicle Safety
Dobrovolny, Chiara Silvestri; Untaroiu, Costin D.; Sharma, Roshan; Jin, Hanxiang; Meng, Yunzhu (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-10)
Platooning is an extension of cooperative adaptive cruise control and forward collision avoidance technology, which provides automated lateral and longitudinal vehicle control to maintain short following distances and tight formation. The capacity and adequacy of existing roadside safety hardware deployed at strategic locations may not be sufficient to resist potential impact from an errant fleet of multiple trucks platooning at high speed. It is unknown how these impacting trucks might interact with roadside safety barriers after leaving their platoon and what the occupant risks associated with such impacts may be. This research identifies and prioritizes the critical Manual for Assessing Safety Hardware TL5 roadside safety devices for truck platooning impact assessment in order to understand the associated roadside and occupant risks and hazards. Finite element models of the trucks and roadside safety devices are examined using multiple computer simulations for various scenarios. Occupants injury risks during truck collision simulations are assessed using dummy and human finite element models. The results and implications can provide a better understanding of whether any roadside safety device improvements and/or platooning constraint modifications will be necessary before implementing truck platooning.
Data Mining to Improve Planning for Pedestrian and Bicyclist Safety
Jahangiri, Arash; Hasani, Mahdie; Sener, Ipek Nese; Munira, Sirajum; Owens, Justin M.; Appleyard, Bruce; Ryan, Sherry; Turner, Shawn M.; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-11)
Between 2009 and 2016, the number of pedestrian and bicyclist fatalities saw a marked trend upward. Taken together, the overall percentage of pedestrian and bicycle crashes now accounts for 18% of total roadway fatalities, up from 13% only a decade ago. Technological advancements in transportation have created unique opportunities to explore and investigate new sources of data for the purpose of improving safety planning. This study investigated data from multiple sources, including automated pedestrian and bicycle counters, video cameras, crash databases, and GPS/mobile applications, to inform bicycle and pedestrian safety improvements. Data mining techniques, a new sampling strategy, and automated video processing methods were adopted to demonstrate a holistic approach that can be applied to identify facilities with highest need of improvement. To estimate pedestrian and bicyclist counts at intersections, exposure models were developed incorporating explanatory variables from a broad spectrum of data sources. Intersection-related crashes and estimated exposure were used to quantify risk, enabling identification of high-risk signalized intersections for walking and bicycling. The modeling framework and data sources used in this study will be beneficial in conducting future analyses for other facility types, such as roadway segments, and also at more aggregate levels, such as traffic analysis zones.
Vehicle Occupants and Driver Behavior: A Novel Data Approach to Assessing Speeding
Martin, Michael W.; Green, Lisa L.; Shipp, Eva; Chigoy, Byron; Mars, Rahul (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-11)
The question of whether driver behavior, and speeding in particular, differs based on passenger(s) presence requires the use of large amounts of data, some of which may be difficult to accurately obtain. Traditional methods of obtaining driver behavior information result in datasets that either lack passenger information altogether (i.e., insurance companies using telematics) or rely on rough estimates of passenger age and gender obtained from blurred photos (i.e., naturalistic driving studies like the Second Strategic Highway Research Program). This research project represents a novel, data-driven approach to assessing passenger impact on speeding. Household travel survey demographic information and GPS traces were linked to HERE network speed limit to study the impact of vehicle occupancy on speeding. Survey responses from 11 study areas were cleaned, merged, and ultimately used in developing binomial logistic regression models. Of particular interest were the following driver groups: teenagers, adults driving with child passenger(s), and older drivers. The models suggest that drivers speed less when there is a passenger in the vehicle, particularly adult drivers with a child passenger(s).
Standardized Performance Evaluation of Vehicles with Automated Capabilities
Basantis, Alexis; Harwood, Leslie C.; Doerzaph, Zachary R.; Neurauter, Luke (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-12)
Advanced driver-assistance systems (ADAS) are becoming widely available in the new vehicle landscape, increasing of both vehicle occupants’ and other road users’ safety. In some vehicles, longitudinal and lateral positioning under certain conditions can be maintained, designating them as having either SAE level 1 (L1) or level 2 (L2) automated features. By developing a standardized set of tests to be applied to current L1 and L2 vehicles, while keeping the future advancement of automation in mind, these vehicles’ system performance, feature limitations, and performance consistency can be systematically evaluated. This project sought to develop an easily implementable, standardized set of testing procedures that could be quickly and inexpensively performed on automated vehicles to characterize their feature capabilities and limitations. Such information is useful to private or public organizations interested in a standardized approach to classifying vehicle capabilities, whether for informing the expectation of operators, or for cataloging and learning from the variety of implementation alternatives. Although not the primary purpose, this project may also help inform efforts to develop certification or other standardized vehicle performance efforts. The results of this project showed that specific roadway factors affected automated feature performance and that there was significant performance variability across test vehicles.
Optimizing the Lateral Wandering of Automated Vehicles to Improve Roadway Safety and Pavement Life
Zhou, Fujie; Hu, Sheng; Xue, Wenjing; Flintsch, Gerardo W. (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-12)
Because most automated vehicles (AVs) are programmed to follow a set path and maintain a lateral position in thecenter of the lane, over time significant pavement rutting will occur. This study directly measured AV lateralwandering patterns. It was found that the wandering patterns of both AVs and human-driven vehicles could bemodeled with a normal distribution but have significantly different standard deviations, with AV lateral wanderingbeing at least 3 times smaller than the wandering of human-driven vehicles. Modeling with the TexasMechanistic-Empirical Flexible Pavement Design System (TxME) found that the AVs with smaller lateralwandering would shorten pavement fatigue life by 22 percent and increase pavement rut depth by 30 percent,which leads to a much higher risk of hydroplaning. Researchers also calculated the maximum tolerable rut depthsat different hydroplaning speeds. AVs have a much smaller tolerable rut depth than human-driven vehicles due togreater water film thickness in the rutted wheel paths. To reduce the negative impact of AVs on roadway safetyand pavement life, this research recommends an optimal AV wandering pattern, a uniform distribution, whichresults in prolonged pavement life and decreased hydroplaning potential.

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