Personalizing Mobile Travel Information Services
place - europe, mode - mass transit, technology - passenger information
Mobile, Transport Information Systems, Routing, Personalization, Natural Language Processing, Adaptive Interfaces
Research on Advanced Traveller Information Systems (ATIS) shows that travellers make better travel decisions when they are well informed. In the dynamic setting of urban public transport systems, however, the ability to be informed is not enough: travellers need to be able to quickly access and assess the information that is relevant to their own mobility. Unfortunately, most public transport ATIS are not tailored or personalised to meet individual needs. To personalise transport information services, we advocate for a multi-layered approach, integrating (1) implicit preference elicitations, (2) personalised route planning and execution, (3) natural language processing and (4) context-aware mobile interfaces. In particular, city residents use modern-day smart phones ubiquitously. Following the trend of “people as sensor”, these powerful devices can be used to sense how people travel (when, from where, to where, what mode, etc.), and, in doing so, thus elicit preferences (point 1). These preferences are more fine grained than what ATIS can now elicit from static web pages asking pre defined questions, allowing for more advanced route planning (point 2). Lastly, routes can now be requested and visualised on the go: smart interfaces, that free users from inputting requests via keyboard, and adapt based on what the user is currently doing, (e.g. if walking, running etc.) will ease user acceptance of the technology (points 3 and 4). In this paper, we discuss how state-of-the art ATIS systems can become personalised services by including one or more of the following: data mining and natural language processing can be used to learn travellers’ implicit preferences, trip planning and routing that is computed based on explicit preferences, and how smart-phone mobile phones can dynamically adapt to travellers’ surrounding environment and activities in order to maximise the relevance of the data they display.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Lathia, N., Capra, L., Magliocchetti, D., De Vigili, F., Conti, G., De Amicis, R., Arentze, T., Zhang, J., Cali, D., & Alexa, V. (2012). Personalizing Mobile Travel Information Services. Procedia - Social and Behavioral Sciences, Vol. 48, pp. 1195-1204.