18,168 research outputs found

    Utilizing Simulated Vehicle Trajectory Data from Connected Vehicles to Characterize Performance Measures on an Arterial After an Impactful Incident

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    Traffic incidents are unforeseen events known to affect traffic flow because they reduce the capacity of an arterial corridor segment and normally generate a temporary bottleneck. Identification of retiming requirements to enhance traffic signal operations when an incident occurs depends on operations-oriented traffic signal performance measurements. When effective and real-time traffic signal performance metrics are employed at traffic control centers, delays, fuel use, and air pollution may all be decreased. The majority of currently available traffic signal performance evaluations are based on high-resolution traffic signal controller event data, which gives data on an intersection-by-intersection basis but requires a substantial upfront expenditure. The necessary detecting and communication equipment also involves costly and periodic maintenance. Additionally, the full manifestation of connected vehicles (CVs) is fast approaching with efforts in place to accelerate the adaptation of CVs and their infrastructures. CV technologies have enormous potential to improve traffic mobility and safety. CVs can provide abundant traffic data that is not otherwise captured by roadway detectors or other methods of traffic data collection. Since the observation is independent of any space restrictions and not impacted by queue discharge and buildup, CV data offers more comprehensive and reliable data that can be used to estimate various traffic signal performance measures. This thesis proposes a conceptual CV simulation framework intended to ascertain the effectiveness of CV trajectory-based measures in characterizing an arterial corridor incident, such as a vehicle crash. Using a four-intersection corridor with different signal timing plans, a microscopic simulation model was created in Simulation of Urban Mobility (SUMO), Vehicles in Network Simulation (Veins) and Objective Modular Network Testbed in C++ (OMNeT++) platforms. Furthermore, an algorithm for CVs that defines, detects and disseminates a vehicle crash incident to other vehicles and a roadside unit (RSU) was developed. In the thesis, it is demonstrated how visual performance metrics with CV data may be used to identify an incident. This thesis proposes that traffic signal performance metrics, such as progression quality, split failure, platoon ratios, and safety surrogate measures (SSMs), may be generated using CV trajectory data. The results show that the recommended approaches with access to CV trajectory data would help both performance assessment and operation of traffic control systems. Unlike the current state of the practice (fixed detection technology), the developed conceptual framework can detect incidents that are not captured by intersection-vicinity-limited detectors while requiring immediate attention

    Understanding Economic Change

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    Full Issue 19(1)

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    Performance Measures to Assess Resiliency and Efficiency of Transit Systems

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    Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

    Exploration Of New Methods In Long Distance Transportation Data Collection And Tourism Travel In Vermont

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    ABSTRACT Human transportation patterns have continued to shift and increase in rate as technology has made travel between spatially disparate locations more feasible. These movements are responsible for approximately one third of global carbon emissions, and account for one half of Vermont’s greenhouse gas output. Modeling transportation behaviors is difficult due to changing travel patterns and issues of surveying human participants. Long distance travel patterns are especially difficult and have not received the attention that urban mobility has within the literature. In this Masters thesis, I describe current methods of transportation data collection and propose new methods, as well as attempt to quantify the impact on Vermont’s roadways of the transportation-based tourism sector. In the first chapter of this thesis, I describe a GPS-based travel survey conducted over the course of one year, coupled with interview data of long distance trips undertaken by 10 participants. Long distance travel has historically been underrepresented in travel surveying due to its infrequency, resulting in decreased likelihood of capturing a long distance trip in a short travel study. By extracting points at intervals from the GPS dataset, it becomes possible to determine accuracy of trip matching between the two datasets with adjusted data collection methods. The second chapter examines transportation related to tourism in Vermont. As one of Vermont’s largest industry sectors, economic impact has been of particular interest to state planners. However, limited analyses of the transportation impacts of this sector are currently available. My research models route choice of drive through tourists, whom constitute 40% of visitors, attempting to begin quantifying tourist mileage and CO2 emissions within the state. Together, these studies expand knowledge on long distance transport data collection and the role of tourism in Vermont’s transportation mileage

    Center for Economic Studies and Research Data Centers Research Report: 2013

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    Many individuals within and outside the Census Bureau contributed to this report. Randy Becker coordinated the production of this report and wrote, compiled, or edited its various parts. Matthew Graham and Robert Pitts authored Chapter 2, C.J. Krizan authored Chapter 3, and Lucia Foster, Todd Gardner, Christopher Goetz, Cheryl Grim, Henry Hyatt, Mark Kutzbach, Giordano Palloni, Kristin Sandusky, James Spletzer, and Alice Zawacki all contributed to Chapter 4. Brian Holly provided the material found in Appendix 3. Our RDC administrators and executive directors helped compile information found in Appendixes 2 and 6. Other CES staff contributed updates to the other appendixes. Linda Chen of the Census Bureau’s Center for New Media and Promotions and Donna Gillis of the Public Information Office provided publication management, graphics design and composition, and editorial review for print and electronic media. Benjamin Dunlap of the Census Bureau’s Administrative and Customer Services Division provided printing management.The Center for Economic Studies partners with stakeholders within and outside the U.S. Census Bureau to improve measures of the economy and people of the United States through research and innovative data products.Research summaries in this report have not undergone the review accorded Census Bureau publications and no endorsement should be inferred. Any opinions and conclusions expressed herein are those of the author(s) and do not necessarily represent the views of the Census Bureau or other organizations. All results have been reviewed to ensure that no confidential information is disclosed

    Examining macro-level correlates of farm equipment theft : a test of routine activity theory and social disorganization theory.

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    This dissertation explores the potential for routine activity theory and social disorganization theory to explain incidence of farm equipment theft at the county level. Relatively few attempts have been made to discern the factors that contribute to such theft. Most are relatively dated, and all focus upon the relationship between victimization risk and the characteristics of individual farms. Accordingly, the current study represents the first attempt to examine the influence of macro-level processes and characteristics upon the problem. Data are gathered for 306 counties housed within four Southeastern States. Counts of farm equipment theft are collected from the 2011-2012 iterations of the National Incident Based Reporting System, and attributed to the county in which they occurred. The routine activity measures employed are based upon the findings of micro-level studies, and drawn primarily from the 2007 version of the Census of Agriculture. Social disorganization measures are created in line with past attempts to explore the applicability of the theory to crime problems outside of metropolitan areas. These measures are derived from the 2010 version of the United States Census. Negative binomial regression analysis suggests that both theories have applicability to our understanding of farm equipment theft incidence. Agricultural characteristics aggregated to the county level appear to condition the number of opportunities available to motivated offenders. Moreover, counties featuring structural characteristics conducive to disorganization appear to experience higher incidence of theft than those that would be considered “more organized.” Based upon these findings, implications for each theoretical framework are addressed. In addition, policy implications are covered, with a specific focus upon strategies designed to reduce opportunities for theft and improve levels of informal social control in rural areas. The dissertation concludes with a brief discussion of limitations associated with the study, directions for future research, and concluding remarks
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