The adoption of smart cards in urban public transport has fundamentally changed how transport providers manage and plan their networks. Traveller information services, in particular, have leveraged this contextual data for targeting passengers and providing relevant information. Thus, it becomes increasingly relevant for the next generation of services to obtain on-time contextual passenger information, to support the development of intelligent information services. In this paper an adaptation of the Top-K algorithm is proposed for predicting journey destination, applied to different scenarios in public transport. The performance and efficiency are analysed and compared to a decision tree classifier. Finally, the feasibility and potential of applying the proposed methods to large-scale systems in a real-world environment is discussed.
How to Predict Journey Destination for Supporting Contextual Intelligent Information Services?
2015-09-01
612008 byte
Conference paper
Electronic Resource
English
Measuring Destination Attractiveness: A Contextual Approach
Online Contents | 1993
|Information for the intelligent journey
Automotive engineering | 1989
|Information for the intelligent journey
Tema Archive | 1989
|Learning to Predict Driver Route and Destination Intent
IEEE | 2006
|