TruckIt

A data driven map centric garbage truck data visualization SPA for use by the public and registered city officials.

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Inspiration

Using the provided Garbage Truck GPS database we did some data analysis and strove to provide a community view to help community members visualize Garbage Truck data (TruckThis Metrics) and allow them to submit garbage pickup requests. In addition to the community view, we built a login protected administrative view which uses an advanced algorithm (The TruckU algorithm) to calculate wear and tear on the garbage truck fleet. An optimized requested pickup route with calculated directions is also provided via the admin page.

What it does

By providing this view to community members we hope to empower them to help the City of Schenectady reduce garbage truck emissions by reducing the amount of trips a garbage truck needs to make. In essence, if the municipality garbage collection model moves away from a pre-defined pickup model and into an 'on-demand' pickup model it will empower citizens to reduce the frequency they need garbage pickup thereby reducing the amount of time garbage trucks need to be on the road. Displaying fleet stats to the community helps engage users to reduce emissions. Leveraging GoogleMaps travelling salesman routing algorithm allows efficient routes to be plotted on demand rather than traversing the current street by street routes.

The TruckU data is mined from the existing GPS dataset based on vehicle events and miles traveled. This then is run through the sophisticated TruckU wear and tear algorithm to determine a 'Wear Score' (wear and tear on brakepads, tires, oil, etc.) relative to the whole fleet. The 'Wear Score' helps provide city officials with a view into what fleet vehicles will need to be serviced first.

How we built it

Angular 5 SPA, ASP.NET WebAPI2.0 Service Tier, Google Maps APIs

Challenges we ran into

  • We initially wasted a lot of time trying to understand all of the data sets we have access to. After setting up the requests to the Smart Cities cameras and receiving the data we decided to pivot away from this data set as it didn't immediately provide useful data that we could leverage. This cost us some setup time.
  • Most of our actual Angular experience over the past few years is using AngularJS and we have only recently begun developing in Angular. Having to hit the ground running even with the Angular CLI proved to have some initial hurdles. ## Accomplishments that we're proud of
  • TruckU Algorithm
  • TruckThis Metrics and Data Visualization
  • Optimized OnDemand Pickup routes.
  • Garbage truck 'Uber' view and historical routing ## What we learned Became much more proficient with Angular ## What's next for workspace Standing up a database to host customer data so there can be validation around submitting a pickup request. Additional dashboarding reports and data mining activities to further enhance the TruckU algorithm
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