Adaptive User Interface for Personalized Transportation Guidance System
Public transportation guidance services, such as Yahoo, Jorudan and NAVITIME, are widely used nowadays and support our daily lives. Although they provide useful services, they have not fully been personalized yet. This paper presents a personalized transportation system called PATRASH: Personalized Autonomous TRAnsportation recommendation System considering user context and History. In particular, we discuss an Adaptive User Interface (AUI) of PATRASH. Before designing a personalized route recommendation function for PATRASH’s AUI, we investigated possibilities and effectiveness of the function. First, we collected and analyzed 10 subjects’ usage histories of public transportation. Through this investigation, we confirmed the possibilities and effectiveness of the personalized route recommendation function. Second, we investigated the effectiveness of the basic functions of PATRASH’s AUI by comparing with two major transportation guidance systems in Japan. We evaluated those systems from the point of view of usabilities: click costs and time costs. The experimental results illustrate the effectiveness of AUI of PATRASH.
KeywordsPublic Transportation Display Size Arrival Station Train Station User Context
This work was supported in part by NEDO under the METI of Japan, and JSPS KAKENHI Grant Number 26350357 and 26540183.
- 1.Castillejo, E., Almeida, A., López-de Ipina, D.: User, context and device modeling for adaptive user interface systems. In: Ubiquitous Computing and Ambient Intelligence. Context-Awareness and Context-Driven Interaction, pp. 94–101. Springer, Switzerland (2013)Google Scholar
- 2.Fukuta, S., Ito, M., Kawamura, T., Sugahara, K.: Context aware navigation system for using public transport on smartphone. In: International Conference on Software Engineering and Applications (ICSEA 2012), pp. 459–463 (2012)Google Scholar
- 3.Gajos, K.Z., Everitt, K., Tan, D.S., Czerwinski, M., Weld, D.S.: Predictability and accuracy in adaptive user interfaces. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1271–1274. ACM (2008)Google Scholar
- 6.Hu, R., Pu, P.: Enhancing recommendation diversity with organization interfaces. In: Proceedings of the 16th International Conference on Intelligent User Interfaces, pp. 347–350. ACM (2011)Google Scholar
- 8.Nakamura, H., Zhang, H., Gao, Y., Gao, H., Kiyohiro, A., Mine, T.: Dealing with bus delay and user history for personalized transportation recommendation. In: The 2014 International Conference on Computational Science and Computational Intelligence, vol. 1, pp. 410–415 (2014)Google Scholar
- 9.Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: An empirical study of geographic user activity patterns in foursquare. ICWSM 11, 70–573 (2011)Google Scholar
- 10.Reinecke, K., Bernstein, A.: Improving performance, perceived usability, and aesthetics with culturally adaptive user interfaces. ACM Trans. Comput.-Hum. Interact. (TOCHI) 18(2), 8 (2011)Google Scholar
- 15.Zenker, B., Ludwig, B.: Rose-an intelligent mobile assistant-discovering preferred events and finding comfortable transportation links. In: ICAART, vol. 1, pp. 365–370 (2010)Google Scholar