Safer Teen Driving in the Age of Smartphones

I started off my week with a deviation from my main project: creating a visualization of traffic speeds using Metropia's traffic data for Austin, Texas.

In order to tell its users the fastest route, Metropia determines and predicts the speed of traffic on every road. This information allows the Metropia app to provide users with the optimal route which takes into account current and future traffic conditions. However, occasionally there have been issues with the routes provided, often caused by incorrect estimations of traffic speed resulting from GPS error. In order to allow Metropia's developers to visualize these errors, I was asked to create a map showing the traffic speeds on every street. With a visualization showing different speeds as different colors, abnormalities such as a sudden 90mph point on an average 30mph road can be more easily detected by debugging developers.

Using QGIS, an open source graphical information system, I started off with creating a visual representation of the traffic speeds based off old traffic data, shown in Figure 1. I then wrote a java program that automatically reads in changes in traffic data to an excel file with every new update from the Metropia server, and a python script to get QGIS to re-render the image with any changes in data, so that the image would update live alongside Metropia's updates.

Figure 1: Map of traffic speeds in Austin, Texas. The blue shows lower speeds, while the red shows higher speeds.

Throughout next week, I will be working on allowing configuration of the time of day for the traffic speeds shown in order to view predictions for future traffic speed levels as well as past data. In addition, I will also be looking at how to make my program more easily usable for Metropia's developers, perhaps by making a UI (if time permits).