This post explores how LLM-driven systems transform simple travel predictions into something intelligent, accurate, and responsive.
Classic ETA systems rely on basic formulas: distance, average speed, maybe a historical table showing usual delays. These approaches are serviceable in predictable conditions but often fall short. Unexpected traffic jams, road closures, unusual weather, or large events can easily throw off these calculations, leaving customers and companies frustrated.
LLMs are advanced AI models originally designed to understand and generate human language. Researchers realized that the same models could process more than words—they can handle timelines, sensor logs, maps, and even text-based driver reports or news updates.
For example, an LLM-based ETA system for buses digests live GPS feeds alongside news about protests or storms, combining all these sources to update predictions on the fly.
- Structured data: GPS positions, route segments, real-time traffic feeds, weather conditions, vehicle speeds.
- Unstructured data: Live text updates, public announcements, driver feedback, social media mentions.
- Historical records: Past journeys, typical rush hour patterns, recurring events.
Feature Engineering
Feature engineering is the process of turning raw data into useful information that helps machine learning models make better predictions. For example, instead of just using raw GPS coordinates or timestamps, feature engineering adds context like whether it’s rush hour, a holiday, or rainy weather. These added “features” give the model richer clues about real-world conditions, improving the accuracy and reliability of ETA predictions.
Every ETA prediction is only as good as its features. LLM-powered systems don’t just take raw data; they transform it into context-rich inputs:
- Flags for rush hours or weekends.
- Holiday/event markers.
- Weather context (rain, fog, heatwaves).
- Traffic congestion patterns.
This process feeds the model patterns that aren’t obvious at first glance but matter a lot for real-world travel.
The Deep Learning Difference
Traditional models “crunch numbers” by working primarily with structured data using fixed formulas or statistical models. They rely on predefined rules or mathematical relationships to calculate estimates. For example, a traditional ETA model might use formulas based on distance and average speed or look up historical average times for similar trips. These models are rigid and don’t change once the trip starts, so they can’t handle unexpected events or complex patterns very well.
In contrast, LLMs and related AI models “analyze patterns, learn trends, and recognize exceptions” in a flexible, adaptive way. Because they are trained on vast amounts of diverse data—including real-time traffic, weather reports, textual driver inputs, and more—they recognize complex relationships and changing conditions. They can update their predictions dynamically as new data arrives, learning from past errors and adjusting to sudden disruptions like accidents or road closures.
So traditional models follow fixed computation steps on known data, while LLMs understand rich context, continuously learn, and adapt their ETA predictions in real time. This makes LLM-powered ETA systems smarter, more accurate, and better suited for unpredictable real-world travel scenarios.
Real-World Impact
Ride-sharing platforms like Uber, delivery apps, and public transit providers such as Chalo in India use these models to improve the reliability of arrivals across entire fleets.
For customers, this means:
- Fewer late deliveries or missed rides.
- More accurate and shorter wait times.
- Better ability to plan around real-world events and last-minute disruptions.
Although LLMs have made ETA predictions more accurate and reliable, some challenges still exist. Sometimes there isn’t enough data, unexpected events happen, or information is missing, which can cause errors or delays. But as AI models improve and get more data, they learn from their mistakes, get better quickly, and help reduce problems.
In upcoming posts, I will explore these challenges in detail, sharing how AI fixes failures and supports smoother, smarter transportation experiences for everyone.