A vehicle-based edge computing platform for transit and human mobility analytics

Abstract

This paper introduces Trellis --- a low-cost Wi-Fi-based in vehicle monitoring and tracking system that can passively observe mobile devices and provide various analytics about people both within and outside a vehicle which can lead to interesting population insights at a city scale. Our system runs on a vehicle-based edge computing platform and is a complementary mechanism which allows operators to collect various information, such as original-destination stations popular among passengers, occupancy of vehicles, pedestrian activity trends, and more. To conduct most of our analytics, we develop simple but effective algorithms that determine which device is actually inside (or outside) of a vehicle by leveraging some contextual information. While our current system does not provide accurate actual numbers of passengers and pedestrians, we expect the relative numbers and general trends to be fairly useful from an analytics perspective. We have deployed Trellis on a vehicle-based edge computing platform over a period of ten months, and have collected more than 30,000 miles of travel data spanning multiple bus routes. By combining our techniques, with bus schedule and weather information, we present a varied human mobility analysis across multiple aspects --- activity trends of passengers in transit systems; trends of pedestrians on city streets; and how external factors, e.g., temperature and weather, impact human outdoor activities. These observations demonstrate the usefulness of Trellis in proposed settings.