data science project
Budget: €250 – €750 EUR
you are provided with a dataset containing mobility traces of ~500 taxi cabs in San Francisco collected over ~30 days. The format of each mobility trace file is the following - each line contains [latitude, longitude, occupancy, time], e.g.: [37.75134 -122.39488 0 1213084687], where latitude and longitude are in decimal degrees, occupancy shows if a cab has a fare (1 = occupied, 0 = free) and time is in UNIX epoch format.
The goal of this data science challenge is twofold:
1. To calculate the potential for a yearly reduction in CO2 emissions, caused by the taxi cabs roaming without passengers. In your calculation please assume that the taxicab fleet is changing at the rate of 15% per month (from combustion engine-powered vehicles to electric vehicles). Assume also that the average passenger vehicle emits about 404 grams of CO2 per mile.
2. To build a predictor for taxi drivers, predicting the next place a passenger will hail a cab.
3. (Bonus question) Identify clusters of taxi cabs that you find being relevant from the taxi cab company point of view.
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Instructions:
Please prepare the presentation with your results. In the final presentation, you'll have to justify and explain:
1. Explanatory data analysis that you performed.
The goal of this data science challenge is twofold:
1. To calculate the potential for a yearly reduction in CO2 emissions, caused by the taxi cabs roaming without passengers. In your calculation please assume that the taxicab fleet is changing at the rate of 15% per month (from combustion engine-powered vehicles to electric vehicles). Assume also that the average passenger vehicle emits about 404 grams of CO2 per mile.
2. To build a predictor for taxi drivers, predicting the next place a passenger will hail a cab.
3. (Bonus question) Identify clusters of taxi cabs that you find being relevant from the taxi cab company point of view.
===
Instructions:
Please prepare the presentation with your results. In the final presentation, you'll have to justify and explain:
1. Explanatory data analysis that you performed.