TITAN : an interactive, web-based platform for transportation, data integration, and analytics
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State transportation agencies regularly collect and store various types of data for different uses such as planning, traffic operations, design, and construction. These large datasets contain treasure troves of information that could be fused and mined, but the size and complexity of data mining require the use of advanced tools such as big data analytics, machine learning, and cluster computing. TITAN (Transportation data InTegration and ANalytics) is an initial prototype of an interactive web-based platform that demonstrates the possibilities of such big data software. The current study succeeded in showing a user-friendly front end, graphical in nature, and a scalable back end capable of integrating multiple big databases with minimal latencies. This thesis documents how the key components of TITAN were designed. Several applications, including mobility, safety, transit performance, and predictive crash analytics, are used to explore the strengths and limitations of the platform. A comparative analysis of the current TITAN platform with traditional database systems such as Oracle and Tableau is also conducted to explain who needs to use the platform and when to use which platform. As TITAN was shown to be feasible and efficient, the future research direction should aim to add more types of data and deploy TITAN in various data-driven decision-making processes.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. Copyright held by author.