Self-Adaptive Predictive Passenger Flow Modeling for Large-Scale Railway Systems

Intelligent transportation system (ITS) is a crucial symbol of smart cities, which aims to provide sustainable and efficient services to residents. Playing a vital role in ITS, railway systems have been integrating with multiple Internet of Things (IoT) devices to monitor real-time inbound passenger flows to ensure pedestrian safety. But it remains difficult to consolidate real-time information from different IoT sources and accurately estimate the future flow due to the coarse-grained data, potential impacts of dynamic interchanged passengers, and real-time predictive capability, which have greatly hindered the progress of ITS transformation in smart cities. To tackle these challenges, we propose a two-stage self-adaptive model for accurately and timely predicting passenger flow in metropolitan railway systems. In the first stage, a self-attention-based prediction model is introduced to predict the next-day passenger flow based on the historical boarding records captured by IoT devices. The proposed decomposing components transferring the discrete boarding records into continuous patterns enable the module to deliver a robust minute-level prediction. In the second stage, a real-time fine-tuning model is developed to adjust the predicted passenger flow based on real-time emergencies and short-term changes in passenger flows from IoT devices. The combination of an offline deep learning mechanism and a real-time reallocation algorithm ensures the real-time response without loss of accuracy. Our end-to-end framework has been deployed to the railway system in Greater Sydney Area, Australia, which can offer accurate predictions to trip planners for timetable design and provide timely decision support for controllers when emergencies happen.