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Browsing by Author "Sener, Ipek N."

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    Data Fusion for Nonmotorized Safety Analysis
    Sener, Ipek N.; Munira, Silvy; Zhang, Yunlong (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-08)
    This project explored an emerging research territory, the fusion of nonmotorized traffic data for estimating reliable and robust exposure measures. Fusion mechanisms were developed to combine five bike demand data sources in Austin, Texas, and the fused estimate was applied in two crash analyses. The research was divided into three sequential stages. The first stage involved developing and applying a guideline to process and homogenize available data sources to estimate annual average daily bike volume at intersections. The second stage was focused on developing and applying the fusion framework—demonstrating the efficacy of multiple fusion algorithms, including two novel mechanisms, suited to the data characteristics and based on the availability of actual counts. The analysis of actual and simulated data illustrated that the fusion methods outperformed the individual estimates in most cases. In the third stage, the fused data were applied in both macro (hot-spot analysis in block group level) and micro (individual safety-related perception) models in Austin to ascertain the significance of incorporating exposure in safety analysis. While the fusion framework contributes to the research in the field of decision fusion, the demand and crash models provide insights to help stakeholders formulate policies to encourage bike activity and reduce crashes.
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    In-Depth Examination of E-Scooter Safety: A Case Study of Austin, Texas
    Sener, Ipek N.; Koirala, Pranik (Safe-D National UTC, 2023-08)
    While gaining widespread popularity in cities worldwide, electric scooters (e-scooters) have also raised significant safety and other concerns since their emergence in the United States in late 2017. This study addressed these concerns by examining e-scooter safety using multiple data sources. The study utilized data collected from two main sources in Austin, Texas, spanning a period of 4 years (2018 to 2021): hospital emergency room patient records obtained from Dell Seton Medical Center and crash data obtained from Texas Department of Transportation’s Crash Records Information System. Further, field-based micro-level built environment data from the study area as well as macro-level demographic,socioeconomic, and built environment data from publicly available sources was collected. The findings highlighted the importance of improving consistency in incident and injury reporting as well as the development and integration of data from different sources. The exploratory analysis revealed key insights on injured e-scooter riders as well as injury and crash patterns. The findings underscored the importance of targeted safety education, interventions addressing alcohol and drug use, infrastructure planning, and time/location-specific measures to enhance e-scooter safety and reduce incidents. A notable finding pertained to intersections, underscoring the need for improvements in visibility, implementation of traffic calming measures, and provision of education specifically tailored for micromobility riders.