Article

Received 30.01.2026

Revised 23.05.2026

Accepted 25.06.2026

Published 04.07.2026

Retrieved from Vol. 29, No. 1, 2026

Pages 137 -146

Korpan, Ya., Fedorov, E., & Utkina, T. (2026). Investigation of the application of LiDAR technology to optimise automatic traffic flow control. Transport Systems and Technologies, 29(1), 137-146. https://doi.org/10.32703/2617-9040-2026-47-11

Investigation of the application of LiDAR technology to optimise automatic traffic flow control

Yaroslav Korpan*, Eugene Fedorov, Tetyana Utkina

y.korpan@chdtu.edu.ua

The paper examined a topical issue arising from the need to improve the efficiency of urban traffic management through the use of advanced technologies. The purpose of the study was to investigate an approach to optimising the operating modes of traffic lights at intersections based on traffic flow data obtained using Light Detection and Ranging (LiDAR) sensors to improve traffic efficiency and reduce delays. The paper described the method of using LiDAR sensors for calculating turn signals at intersections and further optimising the operating modes of traffic lights based on the obtained data. The study found that, in order to function correctly, the method must include the following stages: placement of LiDAR sensors at key intersection points in order to obtain three-dimensional point clouds reflecting the dynamics of traffic flow; processing and analysis of data generated by the sensor (advanced data processing methods are used, in particular, algorithms for segmentation of point clouds and object recognition); analysis of information about the object’s trajectory (in particular, for highlighting and counting return flows from primary LiDAR data); development of a mathematical model of the traffic control system; application of adaptive control strategies for the formation of optimised traffic light modes; development of recommendations for improving efficiency and optimisation on the methods of statistical analysis and machine learning to identify stable patterns and time trends in the structure of traffic; implementation of traffic light objects management. The generalised results of the study showed that the described method can provide an integrated approach and support for automatic adaptive control of traffic lights due to the use of LiDAR technology to collect detailed information about turning movements at intersections and optimise the time of traffic lights, which, as a result, will help to increase the capacity of intersections and reduce congestion in urban transport networks. The results of the study can be used by traffic management bodies and transport infrastructure engineers when designing and optimising traffic light regulation at urban intersections

Light Detection and Ranging sensors; object recognition; mathematical model; traffic; real-time correction; machine learning
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https://doi.org/10.32703/2617-9040-2026-47-11