{"id":1335,"date":"2021-12-09T06:10:15","date_gmt":"2021-12-09T06:10:15","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/12\/09\/plan-the-locations-of-green-car-charging-stations-with-an-amazon-sagemaker-built-in-algorithm\/"},"modified":"2021-12-09T06:10:15","modified_gmt":"2021-12-09T06:10:15","slug":"plan-the-locations-of-green-car-charging-stations-with-an-amazon-sagemaker-built-in-algorithm","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/12\/09\/plan-the-locations-of-green-car-charging-stations-with-an-amazon-sagemaker-built-in-algorithm\/","title":{"rendered":"Plan the locations of green car charging stations with an Amazon SageMaker built-in algorithm"},"content":{"rendered":"
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While the fuel economy of new gasoline or diesel-powered vehicles improves every year, green vehicles are considered even more environmentally friendly because they\u2019re powered by alternative fuel or electricity. Hybrid electric vehicles (HEVs), battery only electric vehicles (BEVs), fuel cell electric vehicles (FCEVs), hydrogen cars, and solar cars are all considered types of green vehicles.<\/p>\n

Charging stations for green vehicles are similar to the gas pump in a gas station. They can be fixed on the ground or wall and installed in public buildings (shopping malls, public parking lots, and so on), residential district parking lots, or charging stations. They can be based on different voltage levels and charge various types of electric vehicles.<\/p>\n

As a charging station vendor, you should consider many factors when building a charging station. The location of charging stations is a complicated problem. Customer convenience, urban setting, and other infrastructure needs are all important considerations.<\/p>\n

In this post, we use machine learning (ML) with Amazon SageMaker<\/a> and Amazon Location Service<\/a> to provide guidance for charging station vendors looking to choose optimal charging station locations.<\/p>\n

Solution overview<\/h2>\n

In this solution, we focus use SageMaker training jobs to train the cluster model and a SageMaker endpoint to deploy the model. We use an Amazon Location Service display map and cluster result.<\/p>\n

We also use Amazon Simple Storage Service<\/a> (Amazon S3) to store the training data and model artifacts.<\/p>\n

The following figure illustrates the architecture of the solution.
\"\"<\/p>\n

Data preparation<\/h2>\n

GPS data is highly sensitive information because it can be used to track historical movement of an individual. In the following post, we use the tool trip-simulator<\/a> to generate GPS data that simulates a taxi driver\u2019s driving behavior.<\/p>\n

We choose Nashville, Tennessee, as our location. The following script simulates 1,000 agents and generates 14 hours of driving data starting September 15, 2020, 8:00 AM:<\/p>\n

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trip-simulator \n  --config scooter \n  --pbf nash.osm.pbf \n  --graph nash.osrm \n  --agents 1000 \n  --start 1600128000000 \n  --seconds 50400 \n  --traces .\/traces.json \n  --probes .\/probes.json \n  --changes .\/changes.json \n  --trips .\/trips.json<\/code><\/pre>\n<\/p><\/div>\n
The preceding script generates three output files. We use changes.json. It includes car driving GPS data as well as pickup and drop off information. The file format looks like the following:<\/p>\n
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{\n\t\"vehicle_id\":\"PLC-4375\",\n\t\"event_time\":1600128001000,\n\t\"event_type\":\"available\",\n\t\"event_type_reason\":\"service_start\",\n\t\"event_location\":{\n\t\t\t\t\t\"type\":\"Feature\",\n\t\t\t\t\t\"properties\":{\n\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\"geometry\":{\n\t\t\t\t\t\"type\":\"Point\",\n\t\t\t\t\t\"coordinates\":\n\t\t\t\t\t\t\t\t[\n\t\t\t\t\t\t\t\t-86.7967066040155,\n\t\t\t\t\t\t\t\t36.17115028383999\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t}\n}<\/code><\/pre>\n<\/p><\/div>\n
The field event_reason has four main values:<\/p>\n