With over 70% of the world’s entire population expected to be living in cities by 2050, supporting citizens’ mobility within the urban environment is a priority for municipalities worldwide. Although public multi-modal transit systems are necessary to better manage mobility, they are not sufficient. Citizens must be offered personalized travel information to make their journeys more efficient and enjoyable. Notably, such information should not only be objective (e.g., bus timetable, live bus tracking), but crucially personalized – since every passenger preferences and interests differ (e.g., crowdedness of trains, heat of tube platforms, sociability of the coaches).
To enable this, a multitude of research problems need to be solved. On the one hand, efficient techniques for mobile participatory sensing are required to create robust mobile distributed systems that can provide on-demand sensing information at a large scale. The sensing has to be intelligent to allow conservation of resources as well as capture of correct sensing information in a given context without requiring user intervention all the time. This needs to then be complemented by domain-specific machine learning algorithms, which must be able to execute on resource-constrained mobile devices with heterogeneous configurations.
It is important to note that although several projects exist focusing on the use of ICT in multi-modal urban transportation in the EU (the French PI is actively involved in one such activity), the Indian context brings unique challenges to the domain. Notably, although it can be assumed that all urban commuters in India have mobile phones, even smart phones, not all might have data plans and might rely on SMS only. The extreme unreliability of the actual transport system due to the chaotic large-scale traffic (e.g., buses not following their schedule) further stresses the need of robust systems for information management in these contexts.