The transportation industry is ripe for some major technological transformations, especially motorcycle accidents on the rise. The transportation industry comes along with injuries, high maintenance costs, loss of lives, and disaster. Up to 4.4million people were injured, and 38,300 lost their lives on U.S roads alone in 2015 according to the National Safety Council.
Generally, hundreds of thousands of people across the world were killed as a result of road accidents, car accidents, and especially motorcycle accidents every year. These accidents bring outrageous costs including property damage, medical expenses, wage, and productivity losses. Costs estimated at $152billion every year. This estimated cost does not even include repairs for damaged roads and highway systems or general maintenance. And even with all the money being spent every year, it is still underfunded.
However, the situation is most likely to get better in the hands of technology, specifically the Internet of Things (IoT) and Machine Learning, the two cutting edge technologies that will undoubtedly become a very important part of every aspect of our lives in the years to come. With a touch of IoT technologies in our transportation industry, we can be able to achieve cost reduction, prevent damage, and mitigate risks. The implementation of connected sensors, with the backing of machine-learning-powered analytics tools, can help us to collect information, analyze, make predictions and decisions that will make our roadways safe. Here’s how IoT and Machine Learning can help make our roads safe again.
IoT is providing Real-Time data to drivers
One important factor that must be considered when addressing the issue of road safety is how IoT can enhance driver safety, especially during unfavorable driving conditions. Drivers are most often likely to experience bad weather conditions that reduce roadway visibility like snow, heavy rain, and sun glare. One of the most common driving conditions that effects drivers are fog; it is experienced more in mountainous areas. Fog develops rapidly without warning and reduces roadway visibility within minutes which renders it a very dangerous driving condition for drivers. In light of this, researchers in Tennessee are working with Cisco to test and develop a network of fog sensors that have been placed along specific roadways across the state to detect gray areas. Data that will be obtained by this roadway sensor during the exercise will be transmitted to the Central Regional Traffic Center.
The IoT and Machine Learning will take account of several factors to find out how severe these local weather conditions are and then produce an up-to-date reading that would enable officials to take appropriate steps appropriate to ensure safety on roadways for drivers in the area.
Drivers’ behavior is the driving cause of auto-related deaths
A lot of factors can be considered when looking at ways that roadway injuries and deaths can be reduced, such as weather conditions, time of day, road construction, etc., etc these factors contribute to accidents on our public roads that lead to fatalities and injuries. Of all the factors, the human element remains the top contributing factor to roadway accidents. No matter how innovative we get on our roadways, we will continue to be drawn back by reckless driving, distracted driving, drunk driving and speeding among these. When it comes to roadway accidents, motorcycle accidents seem to be top of the food chain, with the most gruesome and fatal scenes. We can also use advanced IoT to collect and analyze data to be able to predict high-risk sections of our public roads to encourage safe driving habits on our roads.
IoT can be designed to play a more proactive role in helping drivers practice safe habits on the road. Geotab is a telematics company that uses IoT to reduce accidents significantly. Geotab CEO Neil Cawse said, “Until autonomous vehicles are fully rolled out, we have to employ technology to help manage the human factor in driving, “Data collection is the first step. With telematics, you can know an infinite number of things about the vehicle and what the driver is doing.”
Onboard Diagnostics (OBD), as well as telematics, are helping insurance firms and fleet management companies to gather information about drivers, vehicles and measurable events like over acceleration, seatbelt usage, speeding, etc, which helps encourage safe driving by rating drivers based on their driving data in the form of a scorecard.
Cawse also went on to say, “The second step is driver coaching ” using the data to help the driver learn to drive safely.” In-vehicle driver feedback tools are an effective way to change driver behavior. Geotab users leverage vehicle-in-reverse detection, collision avoidance systems, mobile cameras, and video and spoken word notifications to detect risks and receive live in-vehicle feedback and warnings. Not only can it keep people safe, but it is a major benefit to companies looking to manage risk and control costs related to accidents,.
