Date of Award

8-2017

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Civil Engineering

Committee Member

Jennifer H. Ogle, Committee Chair

Committee Member

Mashrur Chowdhury

Committee Member

Wayne Sarasua

Abstract

Under MAP-21 federally mandated requirements, the Moving Ahead for Progress in the 21st Century (MAP-21), South Carolina is implementing a data-driven based safety decisions and roadway safety performance, which is highly evaluated based on the assessment of safety related information including roadway, traffic, and crash data. Roadway characteristics and Traffic data in most case a subset of the Model Inventory Management System (MIRE) version 1.0 for roadway and traffic data, or from Minimum Model Uniformity Crash Criteria (MMUCC) for crash data. For all that, this thesis involves analyzing and investigating the state-of the-practice and the state-of-the-art of the current SCDOT roadways, traffic, and crash data inventories to test the readiness of building an effective and efficient data driven safety required by the new legislated MAP-21. The research team identified gaps in the current data and suggested a potential data set with priorities based on safety data reporting needs per two commonly federally mandated reporting programs (MIRE fundamental data and HPMS full extend), and one analysis tool (HSM required data). Six performance measures (e.g., accuracy, completeness, and uniformity) were employed to evaluate the ability of using the current data as a prerequisite to extend the data scope to include state-wide roadway network including local roads. Then, the previous successful implementation of data collection using technologies such as LiDAR and Air Imagery were tested on wither they can provide means to surpass the limitations of collecting safety data. In our analysis, we discussed a multi-phased approach which was utilized to organize the safety data requirements and identify the SCDOT data characteristics. This process can be used to enhance the state's safety driven data assessment on the roadway network. A number of specific tasks were undertaken towards achieving the objectives discussed earlier. The results in this work found that most previous reporting was based on the Highway Performance Measures System (HPMS) recommendations, which does not cover local roads, given higher crash rates occur on local roads. The results of this research also emphasized a common relationship between the roadway characteristics, traffic conditions, and the crash rates to conduct a data driven safety assessment on the State's highways. Thus, it is crucial to build a state-wide strategic plan to improve the performance measures (i.e., accuracy, completeness, and uniformity) of the recent data and expand the future data capabilities using new technologies to achieve the new safety goals put up by federal agencies. Investigating the usage of data driven approach for safety analysis has led to several findings regarding the importance of linking roadway segment characteristics (including local roads) and crash locations, which represents a major key in understanding safety issues on the related roadways features. This highly suggests the need for developing more comprehensive data plans in the SCODT. Based on this analysis, the previously used technologies such as LiDAR and Arial Imagery found to be promising for new additions to the safety data collection process, where most roadway characteristics and some traffic controls data were collected successfully in UDOT in LiDAR technology. MIRE and MMUC data elements identified in gap analysis and not collected in SCDOT are prioritized based on their importance for safety analysis and provided in this study for future implementation of data collection plans in South Carolina.

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