Fighting Traffic with Emerging Technologies: An Analysis of Austin Traffic Using Dynamic Time Warping and Bluetooth Data
MetadataShow full metadata
There is a growing field of research surrounding geography and big data. With the explosion of the internet of things, large collections of data hide unforeseen patterns waiting for future research to uncover. As technology continues to grow, new applications in geography and city planning will continue to develop. One technology currently in development for urban planning applications is Bluetooth technology. In the past decade, institutions began to analyze trends in Bluetooth data for traffic analysis. An example of Bluetooth traffic analysis is tracking how a city’s population moves over time. This research paper extracts data from a City of Austin file and performs a dynamic time warping analysis using Python. To be able to calculate the dynamic time warping analysis this research paper extracted a frequency count from a normal Saturday, a normal weekday, and Memorial Day in 2016. The data was made to compare traffic disparities between the busy Memorial Day and the other two “normal” days in Austin. Dynamic time warping is a statistical analysis which compares the similarity between two different data sets. The results of the dynamic time warping demonstrated that the center of Austin had a greater disparity when compared to the locations on the outskirts of town. After conducting the research, the Python program illustrated a larger volume of traffic during Memorial Day when compared to normal traffic conditions. Cities across the United States will be able to predict traffic volume and urban population growth with this technique. More research will bring cities into the 21st century as urban centers begin to create smart cities which could provide emergency services, alternative traffic routes, and create alerts for severe weather conditions.