How is artificial intelligence being used in transportation

AI in transportation has grown to such an extent that its impact has been barely noticed. The technology is making industries more user-centric and automated. Here, we look at the applications of AI in transportation and how it is changing the industry.

• Autonomous Vehicles

One of the most interesting uses of AI in transportation is autonomous cars. Self-driving cars from Waymo and Uber, and semi-autonomous cars from Tesla are already on the road. Using a combination of cameras, sensors, location information, and AI, cars can provide advanced driver assistance features that require little or no human intervention, even if they don’t drive themselves completely. Statista suggests that we could see up to 800,000 vehicles on the road globally between 2023 and 2030. KPMG also suggests that this technology could reduce accident rates by around 90%.

However, AI's true potential in transportation goes beyond consumer applications. It extends to the commercial and public transportation sectors. Imagine the impact on global logistics, where half of all transportation is done by truck. AI in these vehicles could revolutionise not just transportation, but global trade as well. Meanwhile, AI in public transportation can optimise scheduling to reduce waiting times, improve passenger safety, and manage passenger boarding to reduce congestion.

• Optimising traffic flow

Managing traffic or dealing with congestion is a major challenge in congested and busy cities. To address this issue, machine learning transportation applications can prevent traffic jams and recommend optimal routes for drivers. They work by processing data from sensors, cameras, and other IoT devices embedded in the road to identify hazards before specific traffic problems occur. After analysing the data, AI applications can send signals to management systems to adjust signal timing, reroute individual vehicles, or notify accidents.

AI in transportation can thereby lessen traffic, cut down on wait times, and enhance security. City authorities and logistics companies can use these solutions to improve traffic management, reduce congestion, and benefit from reduced carbon emissions.

• Monitoring road conditions

While local councils and national highways monitor major and strategic road networks, monitoring of smaller, less travelled roads and residential areas is typically based on public reports of poor conditions. Computer vision and ML algorithms in traffic AI systems can now automatically detect problems in road surfaces and surrounding structures, eliminating the need for on-site inspections. Using footage captured by drones or fixed cameras, they detect potholes and estimate the extent of road damage. With AI app development services in the USA, authorities can speed up road repairs not only on major roads with high traffic volumes but also across cities, and more importantly, improve safety.

• Traffic accident detection

Effective management of traffic accidents is crucial to maintaining a safe traffic system. And this is where computer vision systems play a key role. They continuously monitor all cameras to scan video for unusual traffic conditions, queues, and incidents. Companies like Motorola Solutions have developed AI-enabled interfaces that can detect these critical incidents immediately.

But AI doesn’t stop at text recognition. It also predicts potential traffic problems. For example, Mind Titan worked with the Estonian Road Authority to create a traffic risk assessment model. Based on data such as weather conditions, crime, police patrol locations, accidents, and other details, the system can predict the probability, severity, and cause of an accident. Thus, AI in the transportation sector will save thousands of lives.

• Driver Monitoring

Driver distraction ranks first on the list of preventable causes of car accidents, with driver fatigue coming in second. AI in logistics and transportation is working to overcome human flaws that could be lethal on the road. One example of effective AI in transportation is advanced driver monitoring systems that can detect whether a driver is paying attention to the road or is distracted.

This is possible thanks to Machine Learning algorithms that analyse camera data inside the vehicle to identify risky behaviour, drowsiness, and distraction. Most of these systems have warning features that alert drivers by vibrating the wheel or increasing the volume of the radio to get their attention as soon as possible to avoid an accident.

• Smart Parking Management

While navigating traffic and avoiding speeding tickets can be difficult, finding a parking space in a crowded parking lot or busy city centre can be just as difficult. Thankfully, transportation Artificial Intelligence is making this challenging chore a little bit simpler.

Integrating computer vision can monitor available spaces in sensor-equipped parking lots, while cameras and automatic licence plate recognition can detect parked cars and track parking times. Then, it updates a real-time map of available parking spaces and upcoming free parking spots that drivers can check on their phones.

• AI in Logistics and Supply Chain Management

Transport logistics companies and retailers are working to keep their supply chains running smoothly, and AI in logistics and transportation can help them do just that, while making it more efficient. Optimising delivery routes, reducing fuel consumption, and sequencing deliveries are just a few of the things that AI can help improve. AI can also help predict product demand, inventory requirements, and customer needs more accurately. So it’s no wonder that by 2024, 50% of all supply chain companies will invest in AI-based applications with advanced analytics capabilities, according to Gartner.

• Automatic License Plate Recognition

Automatic licence plate recognition (ALPR) is a smart upgrade to traditional transportation systems. It uses cameras to read vehicle registration plates to identify vehicles and drivers. ALPR systems operate with greater than 95% accuracy in all lighting conditions. This AI is versatile in transportation technology, including access control for parking lots, flow and incident monitoring for traffic management, and security purposes for law enforcement agencies.

Conclusion

As a result, the Cost to develop an AI Application and AI software can differ significantly based on the previously listed parameters, as you have read. When selecting a development partner, it is crucial for firms to thoroughly assess their requirements and budget. They will be able to maximise their return on investment thanks to this. Thus, keep this article in mind while you're budgeting for app development expenses.