Ez Nav

An sustainable and optimal route processor

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Inspiration

After hearing about both the trucking industry and sustainability in general, our team knew that we wanted to make a project that would implement current technologies to improve the way people interact with the world around them.. With over 5.7 Million truck drivers and 270 million registered personal vehicles, we realized that in producing an application that would allow people to make quicker and more informed decisions we would identify and improve four major metrics:

  • Time spent idling and driving around aimlessly or off-track, searching for the ideal restaurant, gas station, ATM, or other service.
  • Fuel emissions directly produced by the wasted time spent driving and idling, attempting to make an informed decision
  • Waste produced, mainly in food waste, as a by-product of choosing sub-par and/or undesirable restaurants and supermarkets, the produce of which one does not enjoy the quality of
  • User comfort and ease of wise decision-making metrics

The waste produced, in addition to filling up landfills, requires greenhouse gases to transport and dispose of. The first three of our metrics directly tackle the waste of the invaluable and finite human resource known as time, as well as the environmental impacts caused by a culture of waste and environmental unsustainability. The fourth metric directly deals with human comfort and productivity, benefiting all individuals and lowering the need to interact with a phone while on the road, indirectly improving road safety.

As such, we created a Google cloud-integrated web app that takes into account the Google reviews of local websites alongside verifiable user reviews, local temporal traffic data and road closures, to find find desirable establishments and stores along a user's route. Furthermore, the highest-rated establishment of the desired type, within a desired radius of the route, and at the desired completion of the trip, is automatically selected as a waypoint.

What it does

This application takes into account a user's current or chosen starting location, and their desired destination. A time or distance throughout the trip is chosen at which the user would like to stop, and all nearby establishments within a selected radius of that point in the trip are found.

Google reviews are automatically pulled, and the highest-rated establishment in said area is selected and highlighted as the "ideal pitstop". This pitstop is then added automatically as a waypoint at the user's permission, all at the touch of a single button past selecting the route, time of departure, and desired time/distance of pitstop.

At the same time, there is also a rating system that uses firebase to register user accounts and store reviews verified by Google accounts, alongside ratings and comments. A few of these sample ratings are shown on the map, and they are to be updated live and integrated with the pulled Google reviews to create a sense of local community and trust.

How we built it

Throughout the duration of the project, our group segmented into multiple teams and worked solid throughout a majority of the available timeframe. The roles are shown below: Robbie - working on firebase, for cloud store and user review authentication and implementation Mitch - web application, Google cloud APIs, and navigation logic (waypoints, pathfinding, pitstops) Josh and Parmir - coding Android application in Android Development Studio, integrating firebase and web application data alongside Google cloud data for mobile use

Challenges we ran into

We found that the largest challenge throughout this project was the lack of support for the many Google Cloud and other related APIs. Some had been discontinued, and some functions had to be substituted for more basic and less-accurate equivalencies. Additionally, the use of these APIs was not straight-forward and there would often be no explanation on how to implement a function for automation as opposed to manually inputting data. Additionally, combining the data of each person proved to be difficult and there were some aspects which did not come together as intended. There were many small errors which we were not able to account for at the end of the project, due to the limited time frame, including the Android application which did not seem to be compatible with the Google cloud APIs and no sufficient documentation was found online which could account for said issue.

Accomplishments that we're proud of

We are proud to announce that we have produced a fully-functional Minimum-Viable Product (MVP), with many of the features we would have intended for a full release of said product. All of the features mentioned in this description were completed either fully or partially, and the base navigation, waypoint selection, and pitstop finding algorithms were completed alongside the firebase review authentication and implementation. We discovered some new restaurants in each of our areas that we are interested to try, courtesy of none other than our beautiful product which can find the best establishment of any area.

What we learned

We learned a great deal about API implementation, general coding practices, firebase and cloud hosting, Android and in general mobile app development, and other such computer science principles. We learned a lot about how and why things may go wrong, and how to fix them, and when to give up. Additionally, we learned that some programming languages do not process from top to bottom and may completely skip or call variables before they are declared, leading to frustrating critical errors. Web design and web hosting were topics also covered and learned, and although the Android app did not come together we learned that there are significant barriers in cross-platform development which will need to be studied further.

What's next for Ez Nav

Although we will likely not be working together as a full team in the future, there is a great deal of potential with this application. There is no other application on the market which allows one to not only select their starting and ending points for navigation, but to tell their phone exactly when (lunch at 12:00?) or where (65km of gas left in the tank?) they need to make a pit stop and to let their phone find them the optimal option along their route with very minimal input. Next steps would be to implement cost-finding capabilities, and to bring the app to a fully-functioning state in which it can be brought to a mobile platform and brought onto the app store.

We will see!

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