Describe how you would design a system to predict and prevent traffic congestion in a large city in real-time.
Predicting and Preventing Traffic Congestion
Data Collection : Gather extensive real-time data from various sources, such as GPS from mobile applications, traffic cameras, IoT sensors, and social media feeds, to monitor traffic flow, weather conditions, road closures, and events.
Data Processing : Develop a robust data pipeline using technologies like Apache Kafka and Spark to clean, preprocess, and analyze data in real-time.
Predictive Modeling : Utilize machine learning models like decision trees, neural networks, or time-series forecasting models (e.g., ARIMA, LSTM) to predict congestion. Feature engineering would involve considering temporal patterns, road segments, and external factors.
Preventive Actions : Implement adaptive traffic management systems that dynamically adjust signal timings. Propose alternate routes to drivers through navigation apps and display dynamic messages on road signs.
Evaluation : Constantly evaluate and monitor the model’s predictions against actual traffic conditions to ensure accuracy and refine the model accordingly.
