Machine learning is one of the most advanced forms of AI currently available. Its ability to register, correlate, and predict data makes it the ideal solution for many of today’s most pressing issues. Recently, the technology has been given a new use case scenario, helping plan and incentivize sustainable transport networks.
Machine Learning Algorithms
Machine learning algorithms come in many forms today and are in use all around you. These systems continue to improve in their particular skill set, with there now being multiple ML algorithms to use based on your requirements. Some popular examples include artificial neural networks, decision trees, and support vector machines.
When you scroll through your recommended Netflix suggestions and social media feeds, interact with company chatbots, or ride in autonomous vehicles, you’re relying on ML algorithms. Three systems are fast, low cost to operate, effective, and provide new levels of insight. All of these factors make ML algorithms ideal for driving sustainable transport networks forward.
Sustainable Transport Goals
As pollution rises and environmental conditions continue to worsen, the demand for sustainable transport options has steadily increased alongside the availability. Now, there are a multitude of ways for someone to get to their destination without using fossil fuels. From EVs to pedaling your bike down the road, sustainable transport is on the rise.
Source Market Watch – Top Sustainable Transport Adoption Rates in the US
Vital Change is Needed
There is no time to waste in terms of improving sustainable transportation networks. There are already too many cars on the road in cities globally. This scenario has led to congestion, frustration, and environmental damage. Machine learning could help to reduce the world’s addiction to fossil fuels and help usher in a profitable and greener existence.
To accomplish this task, ML developers will need to create algorithms that take into account a huge variety of factors. Using machine learning to drive sustainable transport is more than self-driving cars saving gas, it’s about creating a city that encourages and incentivizes people to take all forms of green transport.
The Green Drive
You can already see this drive for sustainability in many cities, where bike trails and lanes have become more common. These lanes, if placed properly, help drive the economy, improve the local environment, and keep citizens healthy. In cities where bike lanes have been integrated, the results have varied due to key factors such as lane placement, accessibility, and other logistical issues.
In the cities where bike lanes have been introduced successfully, the results have been inspiring. Cities like Toronto have integrated bike lanes and paths that extend throughout the city. The trails have resulted in fewer bike accidents, more people cycling, and a general upkeep in the use of e-bikes to travel around town.
Problems Faced by City Planners that Machine Learning Systems Could Solve
One of the main issues that city planners struggle with is the lack of infrastructure data. Your city may want to integrate a bike lane soon. However, they need to conduct a lot of research to see how it would affect traffic and other key factors. Planners must optimize the location of the bike pathways to ensure cohesion rather than scattered and non-connected routes that serve no real purpose.
Machine Learning can take massive amounts of data and cross-reference it with location-specific information such as the city layout. This strategy allows the AI to run simulations using actual data from locations with similar conditions to determine the optimal route. Notably, determining the optimal route requires a lot more consideration than whether a pathway fits and if it travels through crucial community hubs.
Utilitarian vs. Equity
One study that could provide valuable data to ML systems in the future examined the utilitarian vs. equity-driven pathway routing methods. The researchers reviewed three models, a city-wide utilitarian model, a region-based utilitarian model, and an equity-driven model. The researchers determined that planning optimal bike pathways is far more complex than first expected as in many instances, the biggest benefits to a region are often outside the area. As such, machine learning algorithms could be used to determine how to design reliable access to sustainable transportation networks that benefit the entire community.
Lack of Incentivization for Sustainable Transport
Machine learning could also play a vital role in driving the adoption of sustainable transportation. For the last 2 decades, the drive toward EVs in the West has been slow. For years, interest in this tech would dwindle if gas prices fell.
It wasn’t until recently that the Western countries of the world began developing quantitative approaches to better prioritize infrastructure to drive adoption. Recently, developers have turned towards another budding technology that could work hand in hand with the ML system to encourage passengers to take sustainable transportation options, blockchain.
Blockchain technology enables these systems to process massive amounts of data in real-time and provide accurate rewards using custom-created tokens to incentivize users. This model is already in use across multiple industries, including the budding move-to-earn sector. Similarly, M2E apps reward users for staying active. These systems allow users to track their steps, bike activities, and more, securing rewards based on their actions.
How Will AI Enhance City Planning for Personal Sustainable Transport?
Machine Learning combines the best of both worlds in that it can take historical data and cross-reference it with real-time information to make well-informed decisions. City planners can use this information to formulate, test, assimilate, and even construct useful transportation options.
Machine Learning algorithms enable city planners to integrate sustainable options into the core infrastructure of the community on a new level. They can take into account outside factors such as huge amounts of historical and environmental data. This capability enables planners to predict traffic flow more accurately.
Does Increasing the Versatility of Routes Incentivize Sustainable Transport Practices?
Increasing the versatility of sustainable transport routes will surely drive incentivization. Passengers and riders benefit from healthier lifestyles, less noise pollution, more gas independence, and other positives. Additionally, new routes could be constructed using ML guidance that saves time and provides inspiring views, all while giving access to the city’s activity centers.
Charging Infrastructure
Machine learning systems will play a crucial role in deterring the layout of the charging infrastructure for EVs. EVs have momentum, however, there are still many communities that lack a single charging station. To improve adoption, a viable and easily accessible charging infrastructure must be created.
Machine Learning systems will also assist in driving charging efficiency. EV charging efficiency is a major concern as more people switch to fully electric vehicles. Charging EV batteries can take time. ML algorithms can help to decrease the time while improving the overall life expectancy of your vehicle’s battery.
Decrease Air Pollution
The air you breathe has a direct impact on your health. As such, it’s common for those who live next to major highways to experience some form of lung-related health issue as they age. This was the driving factor for the Chinese market, which is now, by far, the largest and most active EV economy in the world.
EV systems don’t emit emissions, which makes them ideal for retaining air purity and preventing a dangerous situation where citizens are exposed to deadly chemicals all day without even noticing. As roadways slowly convert to a majority of EV vehicles, air quality should increase proportionally.
Support for EV Communities
Machine learning systems are already helping to answer questions clients have for manufacturers during the sales process. In the future, the same concept could make owning a scooter even easier. Machine learning algorithms could help connect you to other EV owners to share information, concepts, and stories.
Companies that could Benefit from Machine Learning Suitable Transportation Incentivization Systems
It only takes a moment to see that the EV market is booming. From electric cars to e-bikes, there are a lot of manufacturers entering this sector, and product options have hit record numbers. Here is one firm that could integrate Machine Learning algorithms to drive revenue shortly.
Vista Outdoor
Vista Outdoor launched in 2015 as an American-based outdoor gear manufacturer and designer. The company has grown into a massive operation that now controls many subsidiaries across the industry. One such project that has brought the firm lots of success is QuietKat e-bikes.
Vista Outdoor Inc. (VSTO +0.18%)
Vista Outdoor Inc. (VSTO +0.18%)
QuietKat e-bikes offer top-notch performance using 750w – 1000w mid-drive motors and premium materials. These units are built to handle off-road conditions and are classified as all-terrain bicycles. Additionally, they include the company’s proprietary VPO technology that monitors your pedals and supplements power when needed.
Quietkat suffered initially as COVID-19 spread, and nobody was able to travel. This lack of travel resulted in the firm losing massive profits. However, demand for e-bikes has surged since the pandemic ended, and today, Quietkat e-bikes are the premier provider of professional-grade off-roading bikes. This growth has resulted in a direct boost to Vista Outdoor’s performance.
Recently, Vista Outdoor signed a sales agreement that split the company into two parts for $3.35 billion, including debt. News of the sale was met with investor confidence as the VSTO stock hit a $2.58B market cap. This news follows an announcement that the firm will release its latest financial statement shortly. These factors make VTSO a “buy” for traders seeking access to the EV market.
A New Era of Sustainable Transport
The era of sustainable transport is here. Now, it’s time for city planners and manufacturers to work together to make the transition as easy and accessible as possible. As such, you can expect to see further integration of this tech with the transport sector, which could result in a much cleaner and safer travel route for the majority of people soon.
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