Urban traffic congestion is a major issue, with traditional traffic
management systems struggling to adapt to changing trends, leading to delays, higher
fuel usage, and pollution. Key AI technologies like Machine Learning (ML), computer
vision, Internet of Things (IoT), geospatial technology, etc used widely in improving
traffic management. Long Short-Term Memory (LSTM), Facebook Prophet,
Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA with
Exogenous Factors (SARIMAX), Gated Recurrent Units (GRU), etc., are widely used
for time-series traffic prediction, which helps in forecasting traffic congestion and flow
patterns. These models rely on historical data to predict future trends and inform realtime traffic management strategies. Computer Vision technologies like object detection
and classification try to find accurate information about vehicles from camera feeds.
IoT devices like infrared sensors, smart traffic lights, etc try to collect real-time data on
vehicle count, speed location, etc, and try to optimize traffic flow and prevent
accidents. Data analytics and big data techniques can process large datasets from GPS
and sensors to identify long-term traffic trends and optimal route planning. Geospatial
technologies like Geographic Information Systems (GIS) offer spatial mapping of
traffic congestion and incidents, while digital mapping services like Google Maps
supply live traffic data and alternate routes. Edge computing and blockchain try to
enhance AI systems by enabling localized data processing to minimize latency and
ensure secure, decentralized data exchange between traffic entities. Cloud computing
tries to enhance the scalability of these AI systems, stores computational resources, and
then analyzes large traffic datasets. Thereby it enables the traffic data to update in real
time and handle changing traffic conditions. This chapter reviews all these key
technologies and discusses the integration of machine learning, computer vision, IoT,
and other technologies, highlights the challenges of urban traffic management, and
suggests future research directions to enhance system scalability, data quality, and realtime decision-making.
Keywords: Artificial intelligence (AI), AI traffic systems, Machine learning, Smart traffic management, Traffic flow analysis.
