Date of Graduation

12-2020

Document Type

Thesis

Degree Name

Master of Science in Civil Engineering (MSCE)

Degree Level

Graduate

Department

Civil Engineering

Advisor/Mentor

Sarah Hernandez

Committee Member

Andrew F. Braham

Second Committee Member

Suman Mitra

Keywords

Bypass, Community Perceptions, Crash Data, Highway, Impact Analysis, Random Error Negative Binomial

Abstract

Transportation practitioners have proposed the construction of highway bypass and widening projects in rural communities to address traffic-related problems that include noise pollution and congestion, among others. In the past, the construction of bypass projects has led community residents to raise concerns about potential decreases in business activity for businesses located along the bypassed road. For transportation organizations, it is essential to understand the economic, social, and safety impacts of transportation projects in terms of public perceptions as public input is a required part of the project planning phase. Moreover, it is recommended that agencies perform retrospective analyses of project economic and safety impacts to better inform future project planning. Yet, a step-by-step framework to aid transportation agencies in gathering retrospective public perceptions of project impacts has not been documented. Moreover, for safety analysis, there are few tools and models to identify causes of crashes at planning area levels, as most focus on analyses of specific segments.

This thesis contributes to these methodological gaps by (1) developing and applying a systematic framework to assess the public perceptions of transportation project impacts on local economies and highway safety; and (2) quantifying the factors attributed to crash occurrence at a zonal level. The components of the framework include (i) design of a semi-structured phone interview survey protocol with data-driven questions; (ii) methods to select participants, and (iii) survey content analysis. While typical crash studies examine crash causal factors at a segment level, understanding the factors at a larger zonal level more closely aligns with the needs for performance-based planning required by federal transportation legislation. Specifically, in this work, a Random Effect Negative Binomial model (RENB) model is developed to estimate the effect of crash causal factors on crash count at the Traffic Analysis Zone (TAZ) level. By accounting for serial and spatial correlation in longitudinal crash data, the impact of various factors like weather conditions, roadway characteristics, and built-environment can be assessed. Then, planners, engineers, and other traffic safety professionals can identify what countermeasures and programs may be most appropriate for mitigating crashes in a zone.

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