Browsing by Author "Sener, Ipek Nese"

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    Data Mining to Improve Planning for Pedestrian and Bicyclist Safety
    Jahangiri, Arash; Hasani, Mahdie; Sener, Ipek Nese; Munira, Sirajum; Owens, Justin M.; Appleyard, Bruce; Ryan, Sherry; Turner, Shawn M.; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-11)
    Between 2009 and 2016, the number of pedestrian and bicyclist fatalities saw a marked trend upward. Taken together, the overall percentage of pedestrian and bicycle crashes now accounts for 18% of total roadway fatalities, up from 13% only a decade ago. Technological advancements in transportation have created unique opportunities to explore and investigate new sources of data for the purpose of improving safety planning. This study investigated data from multiple sources, including automated pedestrian and bicycle counters, video cameras, crash databases, and GPS/mobile applications, to inform bicycle and pedestrian safety improvements. Data mining techniques, a new sampling strategy, and automated video processing methods were adopted to demonstrate a holistic approach that can be applied to identify facilities with highest need of improvement. To estimate pedestrian and bicyclist counts at intersections, exposure models were developed incorporating explanatory variables from a broad spectrum of data sources. Intersection-related crashes and estimated exposure were used to quantify risk, enabling identification of high-risk signalized intersections for walking and bicycling. The modeling framework and data sources used in this study will be beneficial in conducting future analyses for other facility types, such as roadway segments, and also at more aggregate levels, such as traffic analysis zones.
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    Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego
    Hasani, Mahdie; Jahangiri, Arash; Sener, Ipek Nese; Munira, Sirajum; Owens, Justin M.; Appleyard, Bruce; Ryan, Sherry; Turner, Shawn M.; Machiani, Sahar Ghanipoor (Hindawi, 2019-06-16)
    Over the last decade, demand for active transportation modes such as walking and bicycling has increased. While it is desirable to provide high levels of safety for these eco-friendly modes of travel, unfortunately, the overall percentage of pedestrian and bicycle fatalities increased from 13% to 18% of total road-related fatalities in the last decade. In San Diego County, although the total number of pedestrian and bicyclist fatalities decreased over the same period of time, a similar trend with a more drastic change is observed; the overall percentage of pedestrian and bicycle fatalities increased from 19.5% to 31.8%. This study aims to estimate pedestrian and bicyclist exposure and identify signalized intersections with highest risk for walking and bicycling within the city of San Diego, California, USA. Multiple data sources such as automated pedestrian and bicycle counters, video cameras, and crash data were utilized. Data mining techniques, a new sampling strategy, and automated video processing methods were adopted to demonstrate a holistic approach that can be applied to identify facilities with highest need of improvement. Cluster analysis coupled with stratification was employed to select a representative sample of intersections for data collection. Automated pedestrian and bicycle counting models utilized in this study reached a high accuracy, provided certain conditions exist in video data. Results from exposure modeling showed that pedestrian and bicyclist volume was characterized by transportation network, population, traffic generators, and land use variables. There were both similarities and differences between pedestrian and bicycle models, including different spatial scales of influence by mode. Additionally, the study quantified risk incorporating injury severity levels, frequency of victims, distance crossed, and exposure into a single equation. It was found that not all intersections with the highest number of pedestrian and bicyclist victims were identified as high-risk after exposure and other factors such as crash severity were taken into account.
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    Safety Perceptions of Transportation Network Companies (TNCs) by the Blind and Visually Impaired
    Simek, Christopher L.; Higgins, Laura L.; Sener, Ipek Nese; Moran, Maarit M.; Geiselbrecht, TIna S.; Hansen, Todd W.; Walk, Michael J.; Ettelman, Benjamin L.; Plunkett, Michelle (SAFE-D: Safety Through Disruption National University Transportation Center, 2018-10)
    For individuals that are visually impaired, access to safe and reliable transportation can be a significant challenge. The limited menu of mobility options can culminate in a reduced quality of life and more difficulty accessing housing and employment, relative to sighted individuals. Transportation network companies (TNCs, or ridesharing companies) have emerged as a new mode of travel that has the potential to increase access to transportation for the visually impaired. The opportunities and challenges for TNC use by individuals with blindness or visual impairment has not been widely studied. The goal of this research is to use both qualitative and quantitative methods to identify how this community perceives the safety of TNCs relative to other travel modes, and how they utilize TNCs for safe travel. The findings suggest that TNCs are used by a significant proportion of this population. The findings also suggest that one’s experience (or lack thereof) with TNC use has a strong influence on the safety perceptions of this new mode of travel. Finally, while TNCs present an opportunity for riders that are visually impaired to become more engaged in myriad activities, there are still areas in which ridesharing companies can make improvements.