Section: New Results

Sarathi: A Platform for Personalized Mobility Service for Urban Travellers

Participants : Rachit Agarwal, Garvita Bajaj, Georgios Bouloukakis, Valérie Issarny, Nikolaos Georgantas.

Thanks to the increased abundance of mobile phones, the recent field of mobile participatory sensing could be leveraged towards providing a more fine-grained and up-to-date view of a city's transportation system. Thus, in order to address problems like dynamicity (unexpected faults, stoppages, etc.) and unexpected load (number of people using the transportation), etc., in different societal contexts of France and India, we aimed to produce a middleware platform called “Sarathi" that is enriched with personalized mobility services for urban travelers and is evaluated via real-life demonstrators. Towards this, the key results include:

• Identification of System Architecture [14]: We first identify requirements for our system that would satisfy the objectives. The identified requirements are then mapped to specific components that would carry out specific tasks. A client-server system architecture is then created by connecting the identified components. Some components that we identified are: UI component that would run at the client side, recommendation system and knowledgebase component that would run at the server, and a communication component that would ensure communication of the client with the server. To realise these components, we also identify tools and techniques that would ensure best runtime performance.

• Modeling Passenger convenience in Metro transit  [20]: This effort builds upon existing research in the area, studied during our joint survey of related work, and applies the work to the context of the Paris and New Delhi metro system. This work captures 'personalized' experience of passengers during a multi-leg journey and models the convenience for commuters. A leg in a journey is defined as a segment of a journey traveled on a metro line. The work proposes a mathematical model for commuter convenience and validates it using data collected from metro commuters. The convenience model uses 3 convenience measures namely seat availability, wait time and comfort. The work also aims to identify the best mobile interaction paradigm for enabling timely data collection and dissemination and outlines a middleware architecture to achieve this (aiming at acceptable response times for mobile apps).

• Mobile Application: An Android application called MetroCognition for gathering commuters convenience rating during their metro transit based on the three above described measures has been developed, deployed and made available on Google Play Store (https://play.google.com/apps/testing/edu.sarathi.metroCognition) for beta testing.