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Purpose — Fare validation data from transit smart card automatic fare collection (AFC) systems have properties that align with the direction of large-scale mobility surveys and the evermore demanding data needs of the transit industry. In addition to applications in transit planning and service monitoring, travel patterns and behaviour can effectively be studied by exploiting the continuous stream of observations from the same card. The paper proposes a methodology to enrich fare validation data in order to generate information that is hard to obtain with traditional travel surveys.

Methodology/approach — The methodology aims to synthesize individual-level attributes by summarizing multi-day validation records from each card. These new dimensions are then transposed to various levels of aggregation and studied simultaneously in multivariate analysis. The methodology can also be applied to synthesize other multi-day attributes and is transferable to other modes and other travel behaviour studies.

Findings — Results show that validation data can effectively be used to measure the distribution of travel patterns in time and space as well as the variation of those phenomena over time. The paper provides several examples based on millions of validation records from the metro sub-network of Montréal, along with interpretations and some practical implications.

Research limitations/implications — Limitations and bias regarding the data and the methodology as well as the strategies to handle them are discussed within the context of passive travel survey and travel behaviour studies.

Practical implications — Practitioners in transit planning, operations, marketing and modelling can benefit from studying the increasingly accessible and massive smart card datasets through a deeper understanding of multi-day travel patterns and behaviour of transit users.

Originality/value — This paper outlines a data modelling approach and simple-to-implement methodology which exploit the multi-day property of fare validation data from a smart card AFC. The concept of multi-day attributes is introduced. The analyses show that the approach is effective for extracting information on travel behaviour and its variation which would otherwise be hard to obtain through traditional travel surveys, opening up another dimension of this data source for practitioners and transport modellers alike.

This paper aims to explore the relationship between music and learning in the mind/brain.

Taking a consilience approach, this paper briefly introduces how music affects the mind/brain, then moves through several historical highlights of the emergent understanding of the role of music in learning; for example, the much‐misunderstood Mozart effect. Then the role of music in learning is explored from a neuroscience perspective, with specific focus on its potential to achieve brain coherence. Finally, using a specific example of sound technology focused on achieving hemispheric synchronization, research findings, anecdotes and experiential interactions are integrated to touch on the potential offered by this new understanding.

Listening to music regularly (along with replaying tunes in one's brain) clearly helps keep the neurons active and alive and the synapses intact. Listening to the right music does appear to facilitate learning, and participating more fully in music making appears to provide additional cerebral advantages. Further, some music supports hemispheric synchronization, offering the opportunity to achieve brain coherence and significantly improve learning.

This paper brings together diverse research to demonstrate the potential of music to affect mind/brain learning. Further, it introduces and discusses a specific example of sound technology to achieve brain coherence.