The Future Mobility Sensing (FMS) is a smartphone-based integrated activity-travel survey. It uses a combination of a smartphone app, available for Android and iOS, and an online prompted recall survey to collect both demographic and travel data from participants. Data collected from the smartphone app are uploaded to a central server, mapped, analyzed, and made accessible to the participant from the project website, where he or she is asked to provide detailed travel information via a prompted-recall survey. The detailed, accurate data collected by FMS can be used for transportation modeling and urban planning..
Originally piloted as part of Singapore’s 2012 household travel survey, in cooperation with the Land Transport Authority (LTA), FMS is now being deployed commercially in a range of settings in the USA, the Middle East and Africa. At the same time, we continue to advance research applications of FMS, including for public transport customer satisfacion surveys and for better understanding the energy and emissions impacts of individual activity patterns with the purpose of providing customized, context-specific user feedback for behavior change. You can view a presentation of this work here.
A brief description of FMS’ core technologies follows.
Smartphone app
The key role of the smartphone device in our project is to act as data loggers. In fact, the overall FMS platform is being implemented to allow other types of devices to upload data to the server, such as dedicated GPS loggers or On-Board Diagnostic (OBD) devices. Thus, our app is deliberately “silent” in the sense that nothing at all is expected from the user besides making sure it is running.
Download the app on Android and iOS
Data analysis
The data analysis component serves to transform the logged raw data into understandable information for the user. It is particularly focused on inferring activity locations, transportation modes and activities.
For “stop inference”, we apply a rule-based algorithm in two phases: first, it matches spatial/temporal windows to the data to obtain candidate stops; then, it uses wifi, GSM, and accelerometer data to merge stops, particularly using accelerometer information to detect “still” periods (where, although the GSM is “jumping”, the user should stay in the same place). It also uses past validation information to match user’s recurrent places (e.g., home, work) with GSM signatures and adds/removes stops based on mode detection results (e.g. there must be a stop for change mode/transfer between any two different modes). The “mode inference” step applies a machine-learning algorithm to accelerometer and GPS data to identify the mode out of the set of car, bus, subway, walk, bicycle or motorbike. Finally, the “activity inference” matches the historical data, namely the previous validations, to current stops to identify recurrent locations. We are currently developing an integrated stop/mode detection algorithm based on machine-learning techniques.
Web interface
The third component of our system is the web interface. To collect high quality validated data, it is important to make the validation process intuitive and simple for all users, however, many new users find this process challenging. To this end, we carried out several rounds of usability tests, and are continuously working on improving our interface design based on user feedback. In addition, we development a remote desktop help system based on the Firefly® technology. With this feature, the user can share his browser window with the FMS helpdesk assistant, which enables the assistant to better understand his issue and guide him through the validation process.
