Customising real time traveller information

Charles A Karl
National Team Leader for congestion, freight and productivity
Australian Road Research Board
Australia
and
Dr Neil E Béchervaise
Australian Graduate School of Entrepreneurship
Swinburne University of Technology


Keywords: Traffic, traveller information, knowledge management, adult learning, active learning, interactive learning.

Abstract

Despite repeated driver identification of the need for increased information, the difference between a good traveller information service and a poor one depends on the driver rather than the level of information. Some drivers learn quickly and want more specific detail, others are overwhelmed, lack the processing ability to utilise given information or, misinterpret based on previous patterning.

Design of an effective traveller information system necessitates the targeted provision of information sensitive to evolving driver capacity. This paper proposes that future work must proceed on twin fronts: developing effective techniques for categorising and profiling driver experience, preferred learning style and cultural personality type at entry and in evolution; and, developing procedures for matching content to the driver's processing capacity.

Customised traveller information will become effective when it meets the current understanding and needs of the driver as an active learner whose information requirements change over time and from time to time. Further work in these areas will lead to better customisation of information for the driver.