From assisting cars, trains, ships, and aeroplanes to function autonomously to making traffic flows smoother, AI is transforming the transportation sector. Apart from enriching lives, it aims to make all transport modes cleaner, smarter, safer, and more efficient. Among many other uses, AI-led autonomous transport can help reduce human errors that are involved in many traffic accidents.
With these opportunities, however, come real challenges, including unintended consequences and misuse led by cyber-attacks and biased decisions about transport. At MAG, we aim to build solutions that are mindful of all these challenges and help institutions build transportation infrastructure that improves the quality of living for ordinary citizens.
The steamboat was one of the major milestones in the history of transportation in the year 1787. Most people relied on animal-drawn carts prior to this for commuting. Following this, major breakthroughs that led to the growth of the transportation industry included the invention of bicycles (early 19th century), motor cars (in the 1890s), trains (19th century), and aircrafts (1903).
Today, vehicles can navigate and move without any human assistance. Technological advancements have helped the transportation sector progress in its journey of innovation and evolution with one such new-age technology being AI. Leveraging AI in transportation, MAG guides the sector in increasing passenger safety, reducing traffic congestion and accidents, lessening carbon emissions, and minimizing overall costs.
Implementing intelligent algorithms for transportation companies that drive customer successRead All
Real-time information about the number of parked vehicles and empty parking spaces in a parking lot can prove to be extremely beneficial for a variety of organizations that provide parking to their customers.
At MAG, we build solutions that can make accurate predictions by aggregating real-time IoT data, providing an administrative dashboard to the organization in charge and enabling reservation in advance or on the spot among other things.
Freeway congestion can be reduced by controlling the frequency at which vehicles enter the freeway using ramp metering traffic signals on freeway on-ramps, preventing large groups of vehicles from entering together. They can be installed quickly are a lot less expensive than widening a freeway.
There has been an exponential rise in traffic and consequently the risk to drivers and passengers due to increasing urbanization over the years. Appropriate action is vital for safe driving under various road conditions. Identification and notification of accident-prone zones while driving has become easier with the rise of intelligent vehicular systems. Data analytics can improve driving safety in such regions and thereby save invaluable human lives.
Many companies are struggling with the realities of AI implementation even as Industry 4.0 continues to generate media attention. The benefits of predictive maintenance such as helping determine the condition of equipment and predicting when maintenance should be performed, are extremely strategic.
The implementation of ML-based solutions can lead to higher predictability, major cost savings, and increased availability of the systems.
Traffic signal control has experienced very few fundamental improvements in the past 50 years. While tools and methods have been developed to enable traffic engineers’ better use of traffic signal control, the fundamental logic and operations of the controller have not changed.
Most systems today depend on loop detectors or video-based systems that are located at fixed locations in space to call and extend signal control phases. These detection systems provide basic information such as vehicle count, occupancy, and/or presence/passage information. This limits the use of advanced logic that can potentially be built into modern day traffic signal controllers.
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