The rising problem of traffic congestion in cities can result in economic loss,
increased pollution, and delays. Today, the problem with these traffic management
systems is that they are not suited to cope with real-time complexities like an accident
or sudden change of road conditions because of snow or rain, etc. Dynamic, real-time
traffic management systems powered by recent advances in Artificial Intelligence (AI)
promise to address these issues. This chapter introduces a scalable hybrid AI model,
which involves Deep Learning (DL), Reinforcement Learning (RL), and anomaly
detection to forecast traffic patterns, alleviate congestion, and mitigate dynamic
disruptions. The model is introduced based on Long Short-Term Memory (LSTM) and
Graph Neural Networks (GNNs) to capture the temporal and spatial dependencies for
more accurate traffic flow prediction. Through those data streams, anomaly detection
algorithms find spikes that can be tied to incidents such as accidents or road closures
and then reinforce learning to optimize traffic signal control to reduce congestion.
There are three main features: integrating all kinds of multi-modal data, i.e., sensor
data, traffic cameras, social media, weather forecasts, etc., is the major aspect.
Automated pre-processing pipelines make sure data is efficiently processed with very
little computational latency without losing real-time performance. Virtual simulation
and testing evaluate the model's performance with varying ridership in normal and
unusual conditions to ensure the model can manage all phases of traffic. The model's
effectiveness is demonstrated through key performance metrics, such as predictive
accuracy, scalability, and real-time adaptability. This AI-based approach offers a
flexible and scalable solution for improving urban traffic management and reducing
congestion.
Keywords: Hybrid AI model, LTSM, Real-time traffic management, Reinforcement learning, Traffic congestion prediction.
