article · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering
Rapid urban population growth and rising vehicle numbers increase traffic congestion, making flow prediction a central focus for intelligent transport systems. Evaluating several machine learning algorithms, including linear regression, decision tree, gradient boosting, K-nearest neighbours, and random forest, demonstrates their capability to predict traffic flows reliably using public road traffic data from the United Kingdom. Building on these predictive models, an adaptive traffic light management system was developed using the random forest regressor. The system dynamically alters green and red signal timings by accounting for road width, traffic density, vehicle categories, and expected traffic levels. In simulation tests, this adaptive signalling system achieved a 30.8% reduction in traffic congestion at road intersections, offering an effective method to mitigate junction bottlenecks.
Urban traffic congestion leads to wasted travel time, increased vehicle emissions, and economic inefficiency. By using predictive computer models to automatically adapt traffic signals to incoming vehicles, municipal networks can keep junctions moving smoothly. Demonstrating a notable cut in congestion through intelligent signal timings offers a practical pathway towards smarter, less gridlocked urban road networks.
This technology applies directly to municipal traffic authorities, transport agencies, and smart city management vendors seeking to optimise signal timings at road junctions. Tested in simulation using national road dataset records, the concept sits at an applied research stage. Progression towards commercial deployment would require testing on physical traffic light controllers and validation within live street conditions.
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<span lang="EN-US">Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.</span>
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DOI: 10.11591/ijece.v13i5.pp5813-5823
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