In today’s world, knowing exactly when a delivery or a bus will arrive is very important for both companies and customers. This is where ETA, or Estimated Time of Arrival, comes in.
Traditional methods for predicting ETAs are often simple and don’t always account for real-world factors such as traffic jams or bad weather. But with new advances in artificial intelligence, especially with large language models (LLMs), ETA predictions are becoming smarter and more accurate.
LLMs are advanced AI systems that learn from large amounts of data, including travel times, traffic reports, and weather conditions. By analyzing this data, they can predict how long a trip will take much better than older methods.
Unlike traditional ETA calculations that are fixed once a trip starts, LLMs update their predictions in real time, adjusting for new information as the trip progresses.
One well-known example is Uber’s DeepETA system. It combines map data with real-time traffic information and uses deep learning models to improve arrival-time predictions for rides and deliveries. This hybrid AI approach is more flexible and accurate, helping drivers, riders, and logistics teams better plan their schedules.
Similarly, public transit companies like India’s Chalo have used AI-powered ETA models to improve bus arrival accuracy across multiple cities, reducing waiting times and making public transport more reliable. These systems process data from GPS, traffic signals, and historical travel patterns to make their predictions.
The benefits of using LLMs for ETA predictions include fewer late arrivals, better dispatching decisions, and happier customers. However, these systems need lots of good-quality data and regular updating to keep working well.
Stay tuned for my upcoming posts, where I’ll share more about how AI and large language models are changing transportation engineering and making ETA predictions better.