This dissertation has contributed to the pedestrian safety literature by assessing and comparing safety benefits and traffic efficiency among midblock Rectangular Rapid Flashing Beacon (RRFB) and Pedestrian Hybrid Beacon (PHB) sites. Video trajectory data were used to calculate pedestrian Surrogate Safety Measures (SSMs) and vehicles' delay. Regression models of SSMs and vehicles' delay revealed that PHB sites offer more safety benefits, at the expense of increased vehicles' delay, compared to RRFB sites. The presence of the PHB, weekday, signal activation, lane count, pedestrian speed, vehicle speed, land use mix, traffic flow, time of day, and pedestrian starting position from the sidewalk have been found to be significant determinants of the SSMs and vehicles' delay. Another avenue of pedestrian safety explored in this dissertation is the lag time. The study investigates survival likelihood and the lag time of non-instant pedestrian fatalities using random parameter Binary Logit and Ordered Logit models. The models were run on a dataset obtained from the Fatality Accident Reporting System (FARS) for the period of 2015-2019. The analysis revealed that weather, driver age groups, drunk/ distracted/ drowsy drivers, hit and run, involvement of large truck, VRU age group, gender, presence of sidewalk, presence of intersection, light condition, and speeding were common significant factors for both models. The factor found to be significant exclusively for the Binary Logit model includes Area type. Factors found to be significant exclusively for the Ordered Logit model include Presence of Crosswalk and Fire station nearby. The results validate the use of lag time as an alternative to crash count and crash severity analysis. The findings of this study pave the way for practitioners and policymakers to evaluate the effectiveness of midblock pedestrian crossing facilities, as well as to use lag time to investigate crashes and corroborate results from traditional crash-based investigations.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1052 |
| Date | 01 January 2023 |
| Creators | Anwari, Nafis |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Thesis and Dissertation 2023-2024 |
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