Lead presenter: Brian Staes, Center for Urban Transportation Research
Biography:
Brian Staes, E.I., is a graduate research assistant in the Bertini Group at the Center for Urban Transportation Research (CUTR). He is currently working on large scale evacuation highway performance standards and campus mobility modeling. He graduated from Florida Gulf Coast University (FGCU) with a bachelor’s in civil engineering in 2018. While attending FGCU he was an engineering teacher’s assistant and an active member in the American Society of Civil Engineers (ASCE). His area of focus for his master’s is transportation engineering.
Visualizing Transit Network Performance by Leveraging Big Data
Description
Abstract:
The onset of big data in the transportation profession has given stakeholders access to fine granulated data for all modes of transportation. Quantitatively archived spatial and temporal datasets, combined with other mobility data available can be leveraged to identify mobility patterns of every transportation mode available in an urban setting. In addition to this, the collection and storage of real-time data for transportation agencies creates the possibility to investigate real-time and historic modal trends. These intelligent transport investigations can be used to forecast future needs and detect existing strains within a transportation network [1]. An existing problem however, is the processing and visualization of these vast volumes of data that is created daily [2]. For this reason, a case study of archived real-time data for the University of South Florida’s Bull Runner transit system will be analyzed to demonstrate how archived real-time transit data can be visualized to display mobility trends on the existing networks of the University of South Florida. The Bull Runner transit system will be analyzed from an aggregate to dis-aggregate level, depicting granulated system-wide trends to individually magnified stop-level trends using various performance metrics generated by the research team. Following this, the selected visualizations will provide a reference for future observations of big data feeds that simultaneously encompass multiple system performance measures. The applicability of continuous real-time data for urban movements will create opportunities for agencies to identify activity zones in their network, to which they can anticipate operational volumes in real time, which will improve overall mobility in their network. These techniques will then be upscaled to the Hillsborough Area Regional Transit (HART) transit system and visualize their network performance in the same manner.