Real-time Taxi Demand Prediction with Spark

Urban transportation systems often struggle with balancing taxi supply and demand, especially during peak hours. Traditional methods of predicting ride demand are reactive and slow. With the rise of big data and IoT, it’s possible to predict taxi demand in real-time by analyzing streaming location and ride request data. Accurate demand prediction can improve driver allocation, reduce waiting times, and enhance the efficiency of city transportation networks.

Using Apache Spark Streaming, you can process live taxi request data, aggregate it over defined windows, and apply predictive analytics. Machine learning models like Regression, Time Series Forecasting, or XGBoost can predict future demand across different city regions. Real-time dashboards can visualize demand hotspots dynamically, helping fleet operators adjust driver availability proactively based on predicted ride requests.

You start by ingesting taxi location and ride request data using Spark Streaming from Kafka, MQTT brokers, or API sources. After aggregating data in time windows (e.g., every 5 minutes), you engineer features like pickup zones, number of active taxis, time-of-day indicators, and weather conditions. ML models are trained and updated to predict demand per zone. Results are published to a real-time dashboard for visualization and action.

• Ingest real-time taxi ride request data using Spark Structured Streaming from sources like Kafka or REST APIs.
• Preprocess: perform real-time aggregations, sliding window calculations, and extract temporal and spatial features.
• Train regression models or time series predictors to estimate demand volume by region and time interval.
• Visualize predictions live using dashboards or alert fleet managers about high-demand zones proactively.
• Continuously update and retrain models using new streaming data for improved prediction accuracy over time.

• Apache Spark Structured Streaming Guide
• Taxi Demand Prediction Research Paper
• Real-time Dashboarding with Streamlit