Browsing by Author "Tsou, Ming-Hsiang"
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- Big Data Visualization and Spatiotemporal Modeling of Risky DrivingJahangiri, Arash; Marks, Charles; Machiani, Sahar Ghanipoor; Nara, Atsushi; Hasani, Mahdie; Cordova, Eduardo; Tsou, Ming-Hsiang; Starner, Joshua (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-07)Statistical evidence shows the role of risky driving as a contributing factor in roadway collisions, highlighting the importance of identifying such driving behavior. With the advent of new technologies, vehicle kinematic data can be collected at high frequency to enable driver behavior monitoring. The current project aims at mining a huge amount of driving data to identify risky driving behavior. Relational and non-relational database management systems (DBMSs) were adopted to process this big data and compare query performances. Two relational DBMSs, PostgreSQL and PostGIS, performed better than a non-relational DBMS, MongoDB, on both nonspatial and spatial queries. Supervised and unsupervised learning methods were utilized to classify risky driving. Cluster analysis as an unsupervised learning approach was used to label risky driving during short monitoring periods. Labeled driving data, including kinematic information, were employed to develop random forest models for predicting risky driving. These models showed high prediction performance. Open source and enterprise visualization tools were also developed to illustrate risky driving moments in space and time. These tools can be used by researchers and practitioners to explore where and when risky driving events occur and prioritize countermeasures for locations in highest need of improvement.
- Evaluating the Safe Routes to School (SRTS) Transportation Program in Socially Vulnerable Communities in San Diego County, CaliforniaFernandez, Gabriela; Etaati, Bita; Mercado, Andrick; Jahangiri, Arash; Machiani, Sahar Ghanipoor; Tsou, Ming-Hsiang; Mejia, Christian (Safe-D University Transportation Center, 2023-05)Child safety concerns are among the strongest impediments to children walking or biking to school, but some students must walk or bike due to financial or other circumstances. These travel modes are more than twice as common among students from low-income households than students from higher income households. The Safe Routes to School (SRTS) program fosters opportunities for students to walk and bike to school safely and routinely. This study provides insights into the SRTS program’s effectiveness and potential to improve walking and biking safety in socially vulnerable communities by evaluating the program’s impact on schools in the Chula Vista Elementary School District, a vulnerable area in San Diego County. (i) A linear regression model was used to assess the program’s impact on each school, and a logistic regression model was employed to identify factors influencing students’ walking behavior. (ii) An SRTS web-based interactive tool (ArcGIS Experience) was developed to identify traffic incident hot spots and facilitate future routing improvements. (iii) A virtual reality (VR) road safety training tool for children was developed, and a case study at Feaster Charter Elementary School was conducted to assess its effectiveness. Twenty-six students played the VR game before and after watching traffic safety educational videos, and observations from the VR session were recorded. (iv) The outreach and deliverables from this study strengthened community collaboration across San Diego County.