Adaptive traffic control systems in intelligent transportation systems: a systematic literature review
Filia Eunike Sologia1 and Yuli Adam Prasetyo2
Master’s Program in Information System,School of Industrial Engineering, Telkom University, Main Campus (Bandung Campus), Bandung 40257,West Java,Indonesia1
Information System Study Program,School of Industrial Engineering, Center of Excellence for Smart City, Telkom University, Main Campus (Bandung Campus), Bandung 40257,West Java,Indonesia2
Information System Study Program,School of Industrial Engineering, Center of Excellence for Smart City, Telkom University, Main Campus (Bandung Campus), Bandung 40257,West Java,Indonesia2
Corresponding Author : Yuli Adam Prasetyo
Recieved : 24-August-2025; Revised : 19-July-2026; Accepted : 21-July-2026
Abstract
Traffic congestion continues to pose a major challenge, particularly in urban areas where infrastructure capacity and data availability are limited. An adaptive traffic control system (ATCS), part of intelligent transportation systems (ITS), can improve road network performance by dynamically adjusting signal timing in response to real-time traffic conditions. However, research on ATCS remains scattered and diverse, making it difficult to obtain a comprehensive picture of its trends, methods, challenges, and effectiveness. This study seeks to deliver a comprehensive overview of ATCS through a systematic literature review (SLR) involving 77 full-text articles using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The analysis focuses on four main aspects: research trends, implementation barriers, methods and technologies used, and performance evaluation metrics. The study’s findings indicate a significant increase in publications after 2019, with simulation-based studies dominating and major contributions coming from China and the United States. Methodological advancements have gradually transitioned from traditional rule-based approaches toward more advanced techniques, including reinforcement learning (RL), deep RL, multi-agent RL, and hybrid approaches. Various enabling technologies, such as edge computing, computer vision, floating-car data (FCD), sensor fusion, digital twins, and vehicle-to-everything (V2X) communication, are increasingly being integrated. However, real-world implementation still faces various challenges, including data limitations, low connected vehicle (CV) penetration, gaps between simulation and field conditions, and security and infrastructure readiness issues. Performance evaluation is also evolving into a multidimensional process encompassing mobility, safety, the environment, and system sustainability. Overall, there is no single universally optimal method; the performance of ATCS is strongly influenced by the nature of the available data and the level of system readiness. Therefore, future research should focus on improving field validation, standardizing evaluation methods, developing solutions that adapt to local conditions, and strengthening system security and reliability to support real-world implementation.
Keywords
Adaptive traffic control system (ATCS), Intelligent transportation system, systematic literature review, Reinforcement learning, Traffic signal control, Smart transportation.
Cite this article
Sologia FE, Prasetyo YA. Adaptive traffic control systems in intelligent transportation systems: a systematic literature review. International Journal of Advanced Technology and Engineering Exploration. 2026;13(140):210-235. DOI : 10.19101/IJATEE.2025.121221193
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