MIT Investigator(s): Moshe BEN-AKIVA and Christopher ZEGRAS
Development of a next generation individualized mobility sensing system that leverages advanced mobile technologies and machine learning techniques to capture high resolution, multi-day human behaviour and vehicular and freight movements as well as related preferences and satisfaction information.
- Further improve quality of the travel behavior data collected via FMS. We plan to achieve this objective by reducing battery consumption on phones, using additional sensor information and other context information to improve the stop/mode detection algorithm, and designing and developing more user-friendly interfaces.
- Develop a context specific stated preferences (SP) survey system to test new transportation solutions and policies. This includes both trip-based SP experiments and long-term mobility behavior-based SP experiments.
- Develop real-time on-phone surveys based on specific events of interest being detected by the FMS app.
- Provide information and recommendations to users based on their data to (1) incentivize users to keep using the system, and (2) influence people’s mobility choices toward healthier, environmentally friendly and energy efficient options.
- Understand the capabilities of, behavioral understanding from, and data implications of implementing FMS in a city of the Global South, Dar es Salaam (Tanzania). We will achieve these objectives by analyzing FMS and other data from an FMS deployment in DES.