Year: 2025
Type: article
Abstract: Today’s navigation systems are no longer just about getting from point A to B. They’ve evolved into intelligent traffic platforms that adapt to road conditions in real time, helping drivers avoid congestion, save time, and reduce emissions. These systems bring together live data from road sensors, mobile devices, crowdsourced reports, and city infrastructure to paint a detailed picture of what’s happening on the roads. Using distributed computing, they process this massive flow of information almost instantly, with decisions happening across a network of connected devices and edge nodes. At the core are advanced routing algorithms that go far beyond shortest-path calculations. They weigh multiple factors—like travel time, driver preferences, and even predicted traffic patterns—while trying to balance both individual efficiency and the greater good of overall traffic flow. Thanks to machine learning and AI, these systems can now predict problems before they happen, spot anomalies quickly, and adjust routes on the fly. With edge computing handling time-sensitive tasks close to the source, and AI powering smarter decisions, real-time traffic systems are helping cities become more efficient, sustainable, and commuter-friendly.
Author: Satyanandam Kotha
Institution: Jawaharlal Nehru Technological University, Hyderabad
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409446225