Speaker:Bharath Chandra Anumukonda

Real World Scalable Big Data Processing for Smart Transportation Performance Assessment

Department of Transportation is in many ways making its best efforts to make transportation through roads a better experience each day. Many of the few challenges they have are congestion and increasing lane volumes. The congestion and lane volumes may happen either due to vehicle crashes, accidents, overturned trucks, road work, peak hours, etc. With the accelerating increase in new technologies and softwares like Data analytics, Big data tools, Machine learning, Artificial Intelligence, etc, identifying the patterns of congestion or incident detections has become fairly promising. However, the data for analytics comes from sensors, cameras, or different sources of data feed. One of the issues of the disturbance in data quality is due to the misbehavior or improper, malfunctioning, or faulty working of the data feed sensors. The aim of the project is to develop a Real world scalable big data processing for smart transportation performance assessment. The architecture we have used here is Serverless architecture keeping the major focus in Cloud Services using AWS Cloud resources. During the process of this research and development, we have explored various technologies and arrived at the best possible design considering the functional and technical requirements. In this project, the ultimate goal is to identify the faulty sensors for the respective date and stored in a database and also perform the cost comparison and identify advantages of using Serverless architecture. This data provides faulty sensors information for either querying (SQL) or visualizing (Tableau/Dashboards). This provides the Department of Transportation with the alert flags for which they would like to act on. The entire project is developed with sincere efforts to use the Serverless project on CLoud using the services of AWS.

Committee: Ying Cai (co-major professor) and Anuj Sharma (co-major professor)

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