Seeing Like a Bike: Cycling into the Sunset

The main focus of our project is to provide valid data improve the cycling conditions of the City of Atlanta. Our targets were the bikers who may not feel comfortable driving during the peak rush hour traffic.

To do this we had to measure the stress of cyclists, in different situations, and tag them based on a Level of Traffic Stress (LTS) model. To measure the stress of a cyclist we needed to take into consideration certain environmental factors like traffic, infrastructure and pollution.

Traditionally, information about these factors would be gathered through surveys and reporting incidences as they occur. The Seeing like a Bike Team though, chose to use a sensor based
approach to data collection.

The data provided would help us determine the LTS level of a certain road segment. An LTS of 1 would be a path where a not so confident person would be comfortable riding. While an LTS of 4 would be just for well-seasoned riders.

While the overarching problem is an urban and social one, when we talk about sensors it can be reduced to five main engineering problems. The problems, their solution and the system design we came up with can be seen below:

 

The front Master box was 3D printed, while the back Slave box was a laser cut ABS box. The sensors were screwed into place as can be seen in the video below:

Next came the data collection for which we thank Jeremy, Mariam, and Jihwan for helping out by taking the boxes out to collect data. The data visualizations can be seen below:

The above shows the proximity sensors during a ride from TSRB to Home Park. Here we can see the value deflection while passing cars and other obstacles.

The above shows the PM sensors along the vertical axis during a ride from Midtown to Downtown. Here the PM sensors show really high peaks at the exact same time when the rider crossed large clouds of smoke and dust.

Along with the data we also collected videos with a GoPro mounted on the bikes. This video was gathered to help tag different instances in the video and data. In this way several 5 second signatures were marked and saved. The next step was to make a classifier for the right and left side sensors. This was done by going through the video and tagging every obstacle that was apparent. One such video can be seen below:

The reason this was done was to train the machine learning model that was built. The time based data was first translated into a distance domain so as to remove the bias due to different speeds of different objects with respect to the bike. Data interpolation and Smoothing was done to finally arrive at the Proximity pattern features.

Two machine learning algorithms, Support Vector Machine (SVM) and Random Forest were used to predict the classifier of each data segment. To compare the performance of the prediction models to the baseline classification power (when randomly predicting classifiers), we plot them together with varying train-test sets.Accuracy was seen to be around 50%, which was good but not enough. This was simply due to the fact that we needed more data, as the model was learning and improving quickly, and was way better than the base score of around 18%.

In the future we would like to see the Prediction Power of the model improve significantly by the collection of more data for learning. A bit of Feature engineering would also significantly improve the accuracy. The model would then be used to set well-defined, data-driven boundaries for the LTS model which would help the policy makers make the city safer for cyclists of all comfort levels.

Actually, our overarching goal is to identify environmental factors that give rise to bike riders’ stress level. In order for this, the identification of environment should be achieved first as sensors cannot detect semantic-level objects. Once we can tune and refine the prediction model for detecting environmental fators through feature engineering and modeling, it would be possible to advance to answering the real question — how bicycle infrastructures and environmental factors affect bike riders’ stress level? and how these relationships can be used for constructing the Level of Traffic Stress (LTS) model?

 

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