A Comprehensive Review on Real-Time Traffic Signal Coordination Using Machine Learning: Optimizing Urban Traffic Flow for Efficiency and Sustainability

Abstract

Cities globally grapple with escalating traffic congestion stemming from urban expansion, aging infrastructure, suboptimal traffic signal coordination, and a deficiency in real-time data. INRIX quantified the resulting toll at $305 billion for U.S. commuters in 2020, underscoring the pressing need for cities to adopt innovative strategies within the constraints of building new roads. This challenge is further complicated by disparate data collection times at various junctions, creating a mosaic of information from different periods. Some junctions provide limited or sporadic data, necessitating careful consideration for future projections. To address these challenges, cities must embrace cutting-edge approaches that integrate real-time data, efficient traffic management systems, and forward-thinking projections, enabling them to navigate the intricacies of contemporary urban mobility and pioneer sustainable solutions for the future.

Introduction

I. INTRODUCTION

The rapid urbanization and population growth have given rise to a considerable surge in vehicular traffic, presenting formidable challenges to the efficiency of transportation systems. Beyond economic ramifications, traffic congestion contributes to heightened environmental pollution and a diminished quality of life. Conventional traffic signal control systems, often constrained by fixed timing plans, grapple with the dynamic and unpredictable nature of urban traffic. In response, the integration of machine learning algorithms into traffic signal coordination emerges as a promising avenue for fortifying the responsiveness and adaptability of traffic management systems. This study endeavors to investigate and implement a real-time traffic signal coordination approach grounded in machine learning principles, with the overarching objective of alleviating congestion, curtailing travel times, and enhancing overall traffic flow. Through the utilization of reinforcement learning, the proposed system aims to autonomously optimize signal timings in response to real-time traffic conditions, thereby establishing a dynamic and adaptive traffic control infrastructure. The core premise of this research lies in the recognition that the intricate nature of urban traffic demands a more sophisticated and adaptive control mechanism. Machine learning algorithms, particularly those rooted in the principles of reinforcement learning, offer a means to empower traffic signal coordination systems with the capacity to learn and adapt to the ever-changing dynamics of the road network.

II. RELATED WORK

RasheedF. et al.,[1] have researched on the escalating challenges posed by urbanization and population growth have spurred significant interest in addressing the burgeoning issue of vehicular traffic. ZhangY. et al.,[2] focused on the consequences of traffic congestion extend beyond economic implications, with environmental pollution and a compromised quality of life becoming prominent concerns. OpreaS. et al.,[3] proposed on Conventional traffic signal control systems, often operating on fixed timing plans, find themselves ill-equipped to contend with the dynamic and unpredictable nature of urban traffic. Huo A et al.,[4] had made thesis on the integration of machine learning algorithms into traffic signal coordination has emerged as a pivotal avenue for bolstering the adaptability and responsiveness of traffic management systems. Jin Y et al.,[5] detailed the literature underscores the potential of machine learning to revolutionize traffic control by introducing real-time adaptability to changing traffic conditions.

Khan et al.,[6] has made this shift from static to dynamic signal coordination is critical for mitigating congestion, reducing travel times, and optimizing overall traffic flow. Zhang et al., [7] several studies emphasize the necessity of a more sophisticated and adaptive control mechanism to effectively manage the complexities of urban traffic. Zhang et al., [8] made a theory on Machine learning, particularly reinforcement learning, emerges as a transformative tool in imbuing traffic signal coordination systems with the capability to autonomously learn and adapt to the evolving dynamics of the road network. Liu et al.,[9] and Staab et al.,[10] as the literature converges on the potential of machine learning in the realm of traffic management, the implementation of these innovative approaches promises to reshape urban mobility and pave the way for more efficient, sustainable, and responsive transportation system.

III.OVERVIEW OF ALGORITHMS

Support Vector Machine (SVM) is a supervised learning algorithm utilized for classification tasks, seeking to determine the optimal hyperplane that separates data points into distinct classes in a high- dimensional space [11]. Its robustness in high-dimensional settings and adaptability to non-linear decision boundaries make it a versatile choice [12]. Naive Bayes, another classification algorithm, operates on probabilistic principles derived from Bayes' theorem, assuming feature independence. Simplicity and efficiency characterize Naive Bayes, making it particularly suitable for tasks like text classification. Decision Trees, an interpretable and intuitive algorithm, recursively splits the dataset based on features [13]. While prone to overfitting, they handle both numerical and categorical data effectively. Random Forest, an ensemble of decision trees, addresses overfitting by aggregating multiple trees' predictions [14]. Lastly, K-Nearest Neighbors (KNN), an instance-based learning method, classifies data points based on the majority class of their k nearest neighbors, adapting well to local patterns. Each algorithm has its strengths and weaknesses, necessitating careful consideration based on the specific characteristics of the dataset and the task at hand [15].

IV. DISCUSSION TABLE

Based on a discussion table for the topic "Machine Learning Algorithm for Coordinating Traffic Signals in Real Time" can be organized to cover various aspects of the algorithm, its implications, challenges, and potential improvements.

V. RESULTS AND DISCUSSION

The results of the machine learning classification models exhibit varying levels of performance across Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (KNN). SVC achieved an accuracy of 0.76, indicating its effectiveness in classifying instances. Decision Tree and Random Forest models demonstrated accuracies of 0.74 and 0.77, respectively, highlighting their competence in capturing complex patterns. Naive Bayes, with an accuracy of 0.53, exhibited limitations in handling certain nuances within the dataset. KNN achieved an accuracy of 0.68, showcasing its moderate performance.

Precision and recall scores further detail the models' ability to make accurate positive predictions and capture relevant instances, respectively. The classification report provides a detailed breakdown of precision, recall, and F1-score for each class, emphasizing the models' strengths and weaknesses across different traffic directions. The macro and weighted averages offer comprehensive performance metrics, with the weighted average accuracy reaching 0.76 for the overall classification task. These results provide insights into the models' effectiveness in classifying traffic directions, guiding further refinement and optimization.

Conclusion

The endeavor to optimize traffic signal coordination using machine learning algorithms marks a crucial stride in addressing the dynamic challenges posed by urban congestion. This study delves into the complexities of implementing a real-time traffic signal coordination system, harnessing advanced machine learning techniques to augment traffic flow, alleviate congestion, and enhance overall urban transportation efficiency. In conclusion, the fusion of machine learning algorithms with traffic signal coordination represents a paradigm shift towards intelligent, responsive, and data-driven urban transportation systems. As cities undergo continuous evolution, the adoption of innovative technologies in traffic management becomes imperative. This research contributes to the ongoing discourse on intelligent transportation, laying the foundation for a future where urban landscapes are characterized by seamless and efficient traffic flow.

References

[1] RasheedF. et al. Deep reinforcement learning for traffic signal control under disturbances: A case study on Sunway city, Malaysia Future Gener. Comput. Syst.(2020) [2] ZhangY. et al. Cooperative multi-agent actor–critic control of traffic network flowbased on edge computing Future Gener. Comput. Syst. (2021) [3] OpreaS. -V. et al. Edge and fog computing using IoT for direct load optimizationand control with flexibility services for citizen energy communities Know.-Based Syst.(2021) [4] Huo A, Zhang W, Li Y (2020) Traffic sign recognition based on improved SSD model. International Conference on Computer Network, Electronic and Automation [5] Jin Y, Fu Y, Wang W, Guo J, Ren C, Xiang X (2020) Multi-feature fusion and enhancement single shot detector for traffic sign recognition. IEEE Access 8:38931–38940 [6] Khan, J., Ketzel, M., Jensen, S.S., Gulliver, J., Thysell, E., Hertel, O.: Comparison ofRoad Traffic Noise prediction models: CNOSSOS-EU, Nord 2000 and TRANEX. Environ. Pollut. 270, 116240 (2021). https://doi.org/10.1016/j.envpol.2020.116240 [7] Zhang, X., Kuehnelt, H., De Roeck, W.: Traffic noise prediction applying multivariate bi- directional recurrent neural network. Appli. Sci. (Switzerland) 11(6) (2021).

Copyright

Copyright © 2024 Vivaswath S, Dr. Blessed Prince P. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.