Optimization of VDOT Safety Service Patrols to Improve VDOT Response to Incidents; Transportation Systems as a Political Artifact: How Infrastructure Can Improve the Economy and Public Health on Native American Reservations

With millions of vehicles on the road each day, traffic delays and interstate congestion result in loss of productivity and millions of dollars each year. A majority of these traffic delays are caused by traffic incidents including crashes and disabled vehicles. These incidents are safety hazards and can lead to secondary crashes. Rapid clearance of these events and scene management during an incident can significantly reduce the impact of congestion. To combat hazardous conditions and decrease congestion related delays, the Virginia Department of Transportation (VDOT) has a fleet of Safety Service Patrols (SSP) that monitor highway conditions and assist emergency responders in scene clearance and traffic management. Managers of the SSP program seek to schedule patrollers in a manner that optimizes their influence on safety and congestion. This paper proposes a Genetic Algorithm based route scheduling algorithm that assigns SSP routes with the goal of minimizing the total time vehicles are stranded before an SSP vehicle arrives. The algorithm adapts to different incident rates and response times to produce schedules that vary by time-of-day and day-of-week. To examine the performance of the algorithm, optimal schedules were made for I-95 in Virginia. A regression model was also developed to estimate the incident rates using a combination of daily traffic counts and historic rates that accounts for the under-counting of incidents in non-patrolled regions. Another model was used to estimate the SSP response times that resolves the inconsistencies with historical response times for incidents that occurred outside of the patrolled roadways. The results indicate that a new route schedule could lead to a reduction in total time waiting for VDOT assistance by approximately 20%, helping VDOT maintain safety, increase impact, and Keep Virginia Moving.

The STS research paper explores the economic and public health struggles on Native American reservations and the opportunity transportation infrastructure presents to curb the effects of those socioeconomic inequalities. Despite a vibrant culture of family and tradition, unemployment and poverty have overwhelmed reservations while obesity, vehicle crashes, and suicide have taken countless lives. The paper demonstrates increased transportation infrastructure investments yields job creation, active commuting, and lower vehicle crash rates, all aspects of an improved economy and public health. Although many studies on improved infrastructure focus on rural communities, the wicked problem framing lens used in this research reveals the otherwise hidden connection between rural communities and Native American reservations and a unique solution to the complex socioeconomic problem. The paper also includes a socio-technical synthesis of the historical relationship between tribal and American governments and the effect of that relationship on transportation infrastructure investments in Indian Country. Given a history of displacement of Native American communities, the political artifact theory establishes a level of authority exercised over the minority for the last several hundred years. Historical case studies will prove the historic attitude toward the minority, and how that has manifested itself in infrastructure inequalities on reservations. The research adds an application of the political technology theory to the field of STS. In conclusion, this paper will offer a sovereign way for the United States government to close the infrastructure gap on Native Americans while addressing the history of authority and displacement.

School of Engineering and Applied Science
Bachelor of Science in Systems and Information Engineering
Technical Advisor: Michael Porter
STS Advisor: Bryn Seabrook
Technical Team Members: Elizabeth Campbell, Emma Chamberlayne, Colin Hood, Allison Hudak, Matthew Orlowsky, Emilio Rivero