We realized that travel time between various places can differ drastically depending on potential departure time.
Did you know that commuting from SF to San Jose takes twice as long during rush hour than during off hours? It's a difference of 100 minutes and 45 minutes (8pm).
What it does
Commute Curve looks through a range of potential departure times and calculates which one is the most efficient for your commute.
By minimizing congestion, we can improve commuter efficiency by cutting down on overall commute time and be greener for the planet by reducing fuel consumption since a lot of oil is wasted when cars idle in traffic.
How We built it
We built an NodeJS server hosted on AWS which connected to the Here API for routing and geocoding, Google Maps for location autocomplete and HTML/CSS/JS/ReactJS/CanvasJS for the frontend.
Challenges We ran into
- API Choices; Deciding on which location services API to use was a challenge because though we were more familiar with the Google Maps API and it supported more extensions, there were certain routing features that we found more convenient in the Here API. In the end for ease of implementation, we decided to go with the Here API.
- Time; Managing multiple time stamps between server and client caused some confusion at first. Given that we were hosting in a different time zone than routing was occurring, we had to be consistent that our system used a consistent time zone convention.
Accomplishments that We're proud of
- Connecting our Node REST API to the React frontend.
- Creating location autocomplete --> geocode --> route framework.
- Rendering visual interpretable graphs.
What We've learned
We had a lot of fun applying congestion-based pricing schemes from other countries like Singapore to our idea for commuters.
What's next for Commute Curve
- Include data from other vendors besides HereAPI for more accurate time estimates.
- Increase configurations for public transport
- Mobile app
- Add other types of analyses.
- Publicize app