Forecasting Gathering Events through Continuous Destination Prediction on Big Trajectory Data

Abstract

Urban gathering events such as social protests, sport games, and traffic congestions bring significant challenges to urban management. Identifying gathering events timely is thus an important problem for city administrators and stakeholders. Previous techniques on gathering event detection are mostly descriptive, i.e., using real-time on-site observations (e.g., taxi drop-offs, traffic volume) to detect the gathering events that have already emerged. In this paper we propose a predictive approach to identify future gathering events through destination prediction of incomplete trajectories. Our approach consists of two parts, i.e., destination prediction and event forecasting. For destination prediction, we relax the Markov property assumed in most of the related work and address the consequent high-memory-cost challenge by proposing a novel Via Location Grouping (VIGO) approach for destination prediction. For event forecasting, we design an online prediction mechanism that learns from both historical and recent trajectories to address the non-stationarity of urban trip patterns. Gathering events are forecast based on projected arrivals in each location and time. A case study on real taxi data in Shenzhen, China shows that our proposed approach can correctly and timely predict gathering events. Extensive experiments show that the proposed VIGO approach achieves higher accuracy than related work for destination prediction and saves more than 82% memory cost over a baseline approach. The event forecasting based on VIGO is effective and fast enough for continuous event forecasting at one-minute frequency.