A Big Data System for Things That Move

The world consists of many interesting things that move: people go to work, home, school, and shop in public transit buses and trains, or in cars and taxis; goods move on these networks and by trucks or by air each day; and food items travel a large distance to meet their eater. Thus, massive movement processes are underway in the world every day and it is critical to ensure their safe, timely and efficient operation. Towards this end, low-cost sensing and acquisition of the movement data is being achieved: from GPS devices, RFID and barcode scanners, to smart commuter cards and smartphones, snapshots of the movement process are becoming available. In this talk, I will present a system for stitching together these snapshots and reconstructing urban mobility at a very fine-grained level. The system, which we call the Space-Time Engine, provides an interactive dashboard and a querying engine for answering questions such as: what is the crowding at a train station? where’re packages held up and how can their delivery be sped up? how can the available supply of transport capacity be better used to address daily demand as well as the demand on exceptional days (such as rallies and severe weather events). I will describe the STE’s capabilities for operational and planning purposes, and as a learning system.