Human factors design of automated highway systems: first generation scenarios. Final report.

Federal Highway Administration, Office of Safety and Traffic Operations Research and Development, McLean, Va.

Impact study on driving by special populations, final report. Volume I - conduct of the project and state of the art.

National Highway Traffic Safety Administration, Washington, D.C.

Safety features of stop signs at rail-highway grade crossings. Volume II. Technical report. Final report.

Federal Highway Administration, Office of Research and Development, Washington, D.C.

Visual detection of driving while intoxicated. Project interim report: identification of visual cues and development of detection methods.

National Highway Traffic Safety Administration, Washington, D.C.

The visual detection of driving while intoxicated; field test of visual cues and detection methods. Final report.

National Highway Traffic Safety Administration, Office of Driver and Pedestrian Research, Washington, D.C.

Examination of unsignalized intersection, straight crossing path crashes and potential IVHS countermeasures. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Examination of signalized intersection, straight crossing path crashes and potential IVHS countermeasures. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Highway operation problems of senior drivers in Illinois. Final report.

Illinois State Department of Transportation, Springfield

A study of self test devices. Technical report.

National Highway Traffic Safety Administration, Washington, D.C.

Human factors analysis of automotive adaptive equipment for disabled drivers. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Safety features of stop signs at rail-highway grade crossings. Volume I. Executive summary. Final report.

Federal Highway Administration, Offices of Research and Development, Washington, D.C.

Effective highway accident countermeasures. Status report.

Mode of access: Internet.

An analysis of PIE efforts associated with airman orientation at MacDill AFB. Tampa ASAP analytic study #7. Final report.

National Highway Traffic Safety Administration, Office of Driver and Pedestrian Programs, Washington, D.C.

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

Safety Assessment Tool for Construction Zone Work Phasing Plans, 2016

The Highway Safety Manual (HSM) is the compilation of national safety research that provides quantitative methods for analyzing highway safety. The HSM presents crash modification functions related to freeway work zone characteristics such as work zone duration and length. These crash modification functions were based on freeway work zones with high traffic volumes in California. When the HSM-referenced model was calibrated for Missouri, the value was 3.78, which is not ideal since it is significantly larger than 1. Therefore, new models were developed in this study using Missouri data to capture geographical, driver behavior, and other factors in the Midwest. Also, new models for expressway and rural two-lane work zones that barely were studied in the literature were developed. A large sample of 20,837 freeway, 8,993 expressway, and 64,476 rural two-lane work zones in Missouri was analyzed to derive 15 work zone crash prediction models. The most appropriate samples of 1,546 freeway, 1,189 expressway, and 6,095 rural two-lane work zones longer than 0.1 mile and with a duration of greater than 10 days were used to make eight, four, and three models, respectively. A challenging question for practitioners is always how to use crash prediction models to make the best estimation of work zone crash count. To solve this problem, a user-friendly software tool was developed in a spreadsheet format to predict work zone crashes based on work zone characteristics. This software selects the best model, estimates the work zone crashes by severity, and converts them to monetary values using standard crash estimates. This study also included a survey of departments of transportation (DOTs), Federal Highway Administration (FHWA) representatives, and contractors to assess the current state of the practice regarding work zone safety. The survey results indicate that many agencies look at work zone safety informally using engineering judgment. Respondents indicated that they would like a tool that could help them to balance work zone safety across projects by looking at crashes and user costs.