Placement Optimization of Public EV Charging Stations
— Cluster Feature Engineering —

The telematics API, contains a wealth of information and if a problem can be formulated with discipline and logic, then it can behave as a surrogate for some rich insights.

From the output of the previous stage, we were now able to merge a vector of computed cluster labels into the data set of observations. This provides a meaningful grouping variable to then aggregate over and define additional features that help differentiate between the quality, and ultimately, the utility of the identified candidate locations.

The M/M/∞ Model

Along with the destinations, we were also able to extract timestamps for vehicle trips from the API. This allowed us to evaluate a trip not only where it ended, but also what time the trip started and ended. By comparing sequential trips for a given vehicle we were able to calculate how long a vehicle stayed at a destination by the timestamps.

The developer portal telematics API protects the anonymity of vehicles by encrypting the vehicle identification number (VIN), however, it sustains this field as a valuable research attribute by preserving its uniqueness.

With this level of granularity we were able to precisely calculate, for every identified location, what the frequency of arrivals, and the dwell time was at each location.

Borrowing from queuing theory, we leveraged the M/M/∞ model and were able to use the arrival behavior and the dwell time behavior to generate an expected number of vehicles that would be at any of the candidate locations at any given time.

Diversity

The second engineered feature was Diversity. It simply represented the ratio of unique visitors to total visitors at a cluster. The primary purpose of incorporating this term as a factor was that it cleansed irregular data, where an instance of a cluster was formed by a small number of vehicles and large number of observations, such that it qualified as a cluster. There were a few instances that behaved like this, such as apartment blocks and neighborhoods. The diversity factor helped dampen such irregularities.

The secondary purpose was that our goal was to improve EV adoption, and selecting locations that represented a wider audience, meant that any potential EV infrastructure would get more visibility at those locations.
['Yusuf J Khan']
['Telematics', 'Machine Learning', 'Clustering', 'Optimization', 'Ev Charging']