{"id":148993,"date":"2025-09-17T05:06:10","date_gmt":"2025-09-17T05:06:10","guid":{"rendered":"https:\/\/www.newsbeep.com\/au\/148993\/"},"modified":"2025-09-17T05:06:10","modified_gmt":"2025-09-17T05:06:10","slug":"traffic-algorithm-to-cut-down-on-crazy-commutes","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/au\/148993\/","title":{"rendered":"Traffic algorithm to cut down on crazy commutes"},"content":{"rendered":"

\"ATraffic in Istanbul, Turkey. Credit: Omar Ramadan via Pexels.<\/p>\n

In welcome news for commuters, researchers have developed a forecasting algorithm which could help city planners improve traffic congestion<\/a>.<\/p>\n

The new \u2018data-driven macroscopic mobility model\u2019 (D3M), presented in Chaos: An Interdisciplinary Journal of Nonlinear Science<\/a>, has faster simulation speeds and easier data requirements than existing models.<\/p>\n

\u201cImagine a system that doesn\u2019t just react to traffic locally, but simulates how congestion can spread in complex, often unexpected ways across an entire city,\u201d says co-author Deniz Ero\u011flu from Kadir Has University (KHAS), Turkey.<\/p>\n

\u201cA jam in one part of the network might trigger bottlenecks kilometres away \u2013 not because of local crowding, but due to the ripple effects of shifting flows.<\/p>\n

\u201cOur model captures these dynamics, offering system-level foresight instead of piecemeal reaction.\u201d<\/p>\n

Existing traffic flow algorithms often require detailed trip information and determine how vehicles move through intersections using inflexible rules.<\/p>\n

\u201cRather than using fixed equations for flow dynamics, we calibrate the model parameters directly from real-world traffic data,\u201d says co-author Toprak Firat also from KHAS.<\/p>\n

D3M instead relies on data which are routinely collected by city planners, such as level of road congestion.<\/p>\n


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\u201cThis allows D3M to adapt its behaviour to the observed conditions in each city, making it more flexible and realistic than models with hard-coded assumptions,\u201d says Firat.<\/p>\n

They found D3M performed more accurately, and up to 3 times faster, than a conventional model in synthetic benchmark tests.<\/p>\n

It could also accurately represent the diverse traffic conditions of Istanbul, London, UK and New York City, USA in tests using real-world data.<\/p>\n

\u201cThe key breakthrough is that cities can now run sophisticated traffic simulations without needing expensive data collection,\u201d says Ero\u011flu.<\/p>\n

\u201cUrban planners could test \u2018what-if\u2019 scenarios \u2013 like temporary closures due to accidents or maintenance \u2013 and see the predicted traffic impact before spending millions on construction.\u201d<\/p>\n

The researchers now plan to test the model in the real world, with the goal of bringing it to real cities soon.<\/p>\n

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