Automatic prediction of visitations to specified points of interest
Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.
1. A computer-implemented method comprising:
training a machine learning model to predict at least one visitation metric that reflects visitation of points of interest (POIs) for which visitation metrics are not currently available;
wherein training the machine learning model comprises training the machine learning model using a training dataset comprising:
visitation data for one or more points of interest for which visitation metrics are currently available, and
visitation metrics for the one or more points of interest;
using the trained machine learning model, determining a particular visitation metric for a particular POI for which visitation metrics are not currently available based on one or more inputs associated with the particular POI;
selecting the particular POI for placement of an electric vehicle charging station (EVCS) based, at least in part, on the particular visitation metric satisfying one or more criteria;
calculating a deficiency value and a served value for a specific type of charger, wherein the deficiency value and the served value are calculated based on an essential population of electric vehicle drivers in a same category as the particular POI that exist in an area around the particular POI and one or more charging recommendations that indicate an amount of chargers of the specific type that the area around the particular POI can support;
calculating a first partial visitation lift value based on the deficiency value;
calculating a second partial visitation lift value based on the served value;
calculating a total visitation lift value for the particular POI based on the first partial visitation lift value and the second partial visitation lift value;
wherein selecting the particular POI for placement of an EVCS is additionally based on the total visitation lift value for the particular POI.
2. The method of claim 1 , wherein the particular visitation metric comprises a percentage of visit count where a visit duration falls between non-overlapping bounds for the particular point of interest.
3. The method of claim 1 , wherein the one or more inputs includes one or more of: a road classification of a nearest road, a population density in a surrounding area, and a number of other POIs of the same category as the particular POI within a vicinity.
4. The method of claim 1 , furthering comprising:
generating, based on the particular visitation metric, instructions that specify to deliver charge to electric vehicles over a specific time frame at a specific rate;
transmitting the instructions to the EVCS to cause the EVCS to deliver charge to electric vehicles over the specific time frame at the specific rate.
5. The method of claim 1 , wherein the visitation metrics for the one or more points of interest include demographic profiles of visitors to the one or more points of interest.
6. The method of claim 1 , wherein the visitation metrics include visitation distributions over time for the one or more points of interest.
7. The method of claim 1 , further comprising: determining whether a proposed change for the particular POI results in a net positive outcome based on the particular visitation metric determined for the particular POI for which visitation metrics are not currently available.
8. A system comprising:
one or more processors;
one or more storage devices operatively coupled to the processor;
instructions, stored on the one or more storage devices, which, when executed by the one or more processors, cause:
training a machine learning model to predict at least one visitation metric that reflects visitation of points of interest (POIs) for which visitation metrics are not currently available;
wherein training the machine learning model comprises training the machine learning model using a training dataset comprising:
visitation data for one or more points of interest for which visitation metrics are currently available, and
visitation metrics for the one or more points of interest:
using the trained machine learning model, determining a particular visitation metric for a particular POI for which visitation metrics are not currently available based on one or more inputs associated with the particular POI;
selecting the particular POI for placement of an electric vehicle charging station (EVCS) based, at least in part, on the particular visitation metric satisfying one or more criteria;
calculating a deficiency value and a served value for a specific type of charger, wherein the deficiency value and the served value are calculated based on an essential population of electric vehicle drivers in a same category as the particular POI that exist in an area around the particular POI and one or more charging recommendations that indicate an amount of chargers of the specific type that the area around the particular POI can support;
calculating a first partial visitation lift value based on the deficiency value;
calculating a second partial visitation lift value based on the served value;
calculating a total visitation lift value for the particular POI based on the first partial visitation lift value and the second partial visitation lift value;
wherein selecting the particular POI for placement of an EVCS is additionally based on the total visitation lift value for the particular POI.
9. The system of claim 8 , wherein the particular visitation metric comprises a percentage of visit count where a visit duration falls between non-overlapping bounds for the particular point of interest.
10. The system of claim 8 , wherein the one or more inputs includes one or more of: a road classification of a nearest road, a population density in a surrounding area, and a number of other POIs of the same category as the particular POI within a vicinity.
11. The system of claim 8 , wherein the instructions further comprise instructions for: generating, based on the particular visitation metric, instructions that specify to deliver charge to electric vehicles over a specific time frame at a specific rate;
transmitting the instructions to the EVCS to cause the EVCS to deliver charge to electric vehicles over the specific time frame at the specific rate.
12. The system of claim 8 , wherein the visitation metrics for the one or more points of interest include demographic profiles of visitors to the one or more points of interest.
13. The system of claim 8 , wherein the visitation metrics include visitation distributions over time for the one or more points of interest.
14. The system of claim 8 , further comprising: determining whether a proposed change for the particular POI results in a net positive outcome based on the particular visitation metric.