Machine Learning-Driven Strategies for Efficient Traffic Congestion Management

Urban regions have a notable obstacle in the form of traffic congestion, which results in longer trip durations, higher fuel usage, and increased pollution levels. This study aims to tackle this issue by presenting a three step approach. The first approach uses Machine learning for Proactive Traffic Congestion Prediction. We explore multiple machine learning algorithms, such as Long Short-Term Memory (LSTM), Decision Tree (DT), Recurrent Neural Network (RNN), AutoRegressive Integrated Moving Average (ARIMA), and Seasonal ARIMA (SARIMA), to predict traffic congestion levels in different zones of the Montreal area. The results indicate that the Decision Tree approach surpasses other algorithms, attaining faster convergence, lower loss values, and a considerably higher R2 score. After predicting the congestion using one of the prediction algorithms mentioned above, metaheuristic optimization algorithms are used to find near optimal cycle time for each traffic light. In step 2 Enhanced Bat Algorithm (EBAT) is proposed to adaptively modify traffic signal timings based on expected congestion levels. The EBAT algorithm utilizes adaptive parameter adjustment and guided exploration techniques that are dependent on the expected congestion. This results in enhanced performance when compared to the conventional Bat Algorithm. We conduct a comparative analysis of EBAT with various meta-heuristics, namely Particle Swarm Optimization (PSO), Cuckoo Search (CS), JAYA, Sine Cosine Optimization (SCO), and Harris Haws Optimization (HHO). The evaluation considers three scenarios: fixed-time traffic lights (baseline), dynamic traffic lights without prediction, and dynamic traffic lights with predicted congestion. The results demonstrate that EBAT yields substantial enhancements in both the rate at which convergence is achieved and the quality of the solutions, as compared to fixed and non-predictive scenarios. The second approach is using Multilevel Learning for Enhanced Prediction Accuracy. The precision of predicting traffic congestion depends on the ability to recognize and manage abnormal traffic patterns, especially in highly populated regions. Traditional prediction methods are vulnerable to these anomalies, as they frequently do not handle or clean the data. This can result in inaccurate forecasts, as the data may encompass anomalous occurrences such as accidents or unforeseen road closures, which can greatly distort the underlying trends. The study presents a novel and creative strategy to learning at several levels, which combines anomaly detection and ensemble learning approaches to tackle this problem. Anomaly detection techniques are used to find abnormal patterns within the data, which is then followed by the process of data cleansing. First, a set of initial learner models are trained. The top-performing models are then selected for an ensemble procedure, which involves combining their predictions through stacking and voting. Evaluated using a real-world Montreal traffic dataset, this multilevel methodology demonstrates higher prediction accuracy when compared to traditional approaches. The dataset is subjected to preprocessing techniques, such as windowing, to transform time-series data into frequency patterns in order to create a more generalized model. To leverage the detected anomalies, we utilized clustering algorithms, specifically K-Means and Hierarchical Clustering, to segment these anomalies. Each clustering algorithm was used to determine the optimal number of clusters. Subsequently, we characterized these clusters through detailed visualization and mapped them according to their unique characteristics. This approach not only identifies traffic anomalies effectively but also provides a comprehensive understanding of their spatial and temporal distributions, which is crucial for traffic management and urban planning. In summary, this study showcases the efficacy of a synergistic method that combines machine learning for proactive prediction of traffic congestion with metaheuristics for dynamic regulation of traffic lights. This method has the capacity to mitigate urban traffic congestion and enhance traffic flow efficiency. In addition, the use of a multilevel learning strategy to improve forecast accuracy is a noteworthy contribution to intelligent transportation systems. An application for city of Montreal is provided.

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