A Big Data Science Solution for Transportation Analytics with Meteorological Data

In the current era of big data, very large amounts of data are generating at a rapid rate from a wide variety of rich data sources. Embedded in these big data are valuable information and knowledge that can be discovered by big data science techniques. Transportation data and meteorological data are examples of big data. In this paper, we present a big data science solution for transportation analytics with meteorological data. In particular, we analyze the meteorological data to examine impact of different meteorological conditions (e.g., fog, rain, snow) on the on-time performance of public transit. Evaluation on real-life data collected from the Canadian city of Winnipeg demonstrates the practicality of our big data science solution for transportation analytics on bus delay caused by various meteorological conditions.