The effects of climate change are being felt all across the globe, evident in everything from California’s string of wildfires to Paris’ record-setting summer temperatures. A modern problem calls for a modern solution. With this in mind, a group of the most respected researchers in the field published a paper called “Tackling Climate Change with Machine Learning.” The paper looks at ways in which artificial intelligence can help us address the climate crisis.
- The paper examines 13 areas where machine learning could reap environmental benefits.
- These include energy production, education and solar geoengineering.
- The research shows that artificial intelligence could be used to make climate predictions and raise awareness about climate change.
Notably, this paper discusses the impact machine learning could have on our transportation systems. It looks at strategies for reducing emissions in both passenger and freight transportation. According to the report’s researchers, artificial intelligence could be used to improve efficiency in the following transportation-related areas:
Freight routing and consolidation
When moving products from one place to the next, companies can reduce the number of trips made by consolidating shipments. Doing so can reduce emissions. Companies can also reduce emissions by adjusting routing so that trucks don’t return empty. Machine learning can be used to optimize performance in both of these areas.
There are many ways of improving a vehicle’s efficiency. The most obvious approach concerns electrification. You can also make a vehicle more efficient by reducing its weight and tire resistance, and by making its exterior design more aerodynamic. Machine learning can be used to improve efficiency in all these areas.
As more people buy electric cars, it brings a new challenge into focus: How can we power these vehicles without putting undue stress on our grids? Understanding the use patterns of people who drive EVs can provide vital information that grid operators can utilize for predicting electric load. Artificial intelligence can be used to forecast charging behavior, offering vital insight that grid operators can use to make informed decisions.
Placement of charging stations
There’s a need for more charging stations to support the growing number of EVs. Many of today’s EVs offer in-vehicle sensors and communication data that can shed light on driving and usage patterns. Machine learning can be used to analyze these patterns in ways that can help us optimize the placement of EV charging stations.
Bike and e-scooter sharing
Bike sharing and e-scooter services offer low-carbon solutions for getting from one place to the next. They don’t require ownership, and they can be used to support public transportation systems. The companies that provide these services can use machine learning to improve forecasts concerning demand and inventory. Machine learning can also be used to generate accurate travel-time estimates so that today’s micro-mobility solutions can be effectively integrated with other types of transportation.
WHY THIS MATTERS
The list of emission-free transportation options has never been longer, and it includes everything from EVs to e-scooters. All this proves we’re on the right path when it comes to eco-friendly transportation. This research paper shows that artificial intelligence can help us refine our approach.