We are group of transit-passionate individuals who want to help improve equity and access within the bike sharing space. We chose Washington DC as our study area, as many of us are familiar with the area.
What It Does
TransitHero provides data analysis of the bike sharing ecosystem within a city using past, present, and predictive data. The goal of the application is to communicate a set of recommended priorities for maintaining and upgrading the bike share infrastructure to the transit planners. The tools we have developed are easily configurable and allow for analysis in any bike sharing system that provides real time and historic ridership data.
Brenden: (Designer/GIS User) Worked on the final ArcGIS Dashboard, visualization, and designing proposals/presentations.
Stella: (GIS User) Worked on the predicted analytics section of the project. Used ArcGIS Pro, ArcGIS Online, R, machine learning models and many geoprocessing tools.
Justin: (GIS User/Project Management) Worked on the historic ridership data analysis, organizing the team, and editing the presentation videos. Used ArcGIS Pro and ArcGIS Online.
Sean: (Developer / GIS User) Configured and set up an ArcGIS Enterprise deployment, managed a GeoEvent Server to utilize real-time data, and developed geoprocessing tools with Python to help automate analysis tasks.
How We Built It
The tool can essentially be divided into four sub-components.
Past Ridership Data and Analysis
We used custom geoprocessing tools and ArcGIS Pro to provide analysis for past ridership data. The tool produces indices derived from statistical methods that can be used to best determine where to locate new bike docking stations.
Real-Time Data and Analysis
We used GeoEvent Server for real-time analytics. Data was fetched from the Capital Bike Share's website using JSON endpoints. This data follows the GBFS standard and can easily be configured to another bike sharing system. The status of each bike docking station in the system was monitored and updated every minute.
Predictive Data and Analysis
We used ArcGIS Pro/Online for predictive data (spatial analysis, machine learning model training).
Bringing it All Together
We consolidated all of our data and maps using an ArcGIS Dashboard and added some additional analysis in the form of interactive charts.
Challenges We Ran Into
Some problems we had that we thankfully overcame included choosing the best analysis methods and initially configuring GeoEvent Server.
Accomplishments That We're Proud Of
Overall, we were happy to produce an easy to use, informative dashboard that can be applied to any bike sharing system.
Sean: I was proud to get GeoEvent running after only ever having used it one day before the Hackathon. I was also happy to create a geoprocessing tool, utilizing what I've learned so far during my internship.
What We Learned
We all learned something new about a wide array of Esri products.
Sean: I learned more about GeoEvent Server and configuring and deploying ArcGIS Enterprise.
What's Next for TransitHero - Hacking the Way to Transit Equity
We want to focus on deeper real time analytics and further advancing data visualization methods.