A Generic Future Mobility Sensing System for Travel Data Collection, Management, Fusion, and Visualization

摘要

In studies of human mobility, there is a need for a holistic system for collection of sensing data, management of data flows, fusion of multiple data sources, and visualization of integrated data to better understand travel behavior. We have designed and implemented a generic Future Mobility Sensing (FMS) system to serve these purposes. FMS harnesses various sensing technologies, heterogeneous multi-source data and analytical functionalities with three dedicated platforms, namely 1) the FMS Data Collection Platform, which intertwines sensing objects, machine learning algorithms and user verifications to collect high resolution, multi-day travel data; 2) the FMS Data Management Platform, which provides standardized APIs to access data stored in an interconnected data model; and 3) the FMS Data Fusion and Visualization Platform, which consolidates multi-source data to be interpreted and presented. Together, the three platforms form a mobility sensing flow to facilitate data-driven analysis and decision-making. With FMS, heterogeneous multi-source data are suitably integrated for analysis, and multi-dimensional knowledge is extracted and presented in intuitive and interactive analytical dashboards. The system is intended to be generic to support different requirements of mobility studies, such as travel surveys, as its function modules are reusable and can be customized to support a unified data collection, management, fusion and visualization process. This paper introduces the overall architecture of the FMS system and summarizes various applications that it can support. Specifically, we present a study of commercial vehicle parking in Singapore to demonstrate the capability of the system.