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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.

Purpose — Automated fare collection systems implemented in public transportation systems in the last decade have provided a massive, continuous and low-cost source of reliable travel information. A direct and useful application of these data is the estimation of highly representative, although not bias-free, origin-destination (OD) matrices.

Methodology/approach — We discuss several issues with current OD matrix estimation methodologies, such as fare evasion and group travel, and their derived biases, specifically focusing on the Santiago (Chile) case. We also propose and apply two methods of validation: endogenous and exogenous validation. We elaborate on some methodological improvements that could be implemented to upgrade the activity estimation mechanics.

Findings — Several sources of bias in the estimation of OD matrix estimation from passive data are pointed and some solutions proposed. We apply improvements to existing methodologies and increase the success rate of trip estimations.

Practical implications — The reliable estimation of public transport OD matrices from passive data results in a valuable planning tool for both transit authorities and operators, much more representative and with less errors and biases that conventional data collecting techniques.

Originality/value of paper — This paper is one of the first works to deal with the subject.

Given the complexity of societal, technological, and economic challenges encountered by schools and teachers, one may wonder whether and how teachers can still optimally motivate their students. To adopt a motivating role in today’s ever-changing, even stormy, educational landscape, teachers need more than a checklist of motivating practices. They also need a fundamental theoretical perspective that can serve as a general source of inspiration for their everyday classroom practices across various situations and in interaction with different students. Herein, we argue that self-determination theory represents such a valuable perspective. In Part I, we discuss the satisfaction of learners’ psychological needs for autonomy, competence, and relatedness as a source of student motivation, engagement, and resilience. We also present a recently developed circular model involving a broad variety of motivating (i.e., need-supportive) and demotivating (i.e., need-thwarting) teaching practices appealing to these three needs. In Part II, we discuss several implications of this circular model, thereby discussing the diverse pathways that lead to student need satisfaction, motivation, and engagement as well as highlighting teachers’ capacity for calibration to deal with uncertainty and change. We conclude that school principals and teachers do well to invest in both students’ and teachers’ psychological need experiences, such that they become skilled in flexibly adjusting themselves to diversity, uncertainty, and change.

Data fusion and the combination of multiple data sources have been part of travel survey processes for some time. In the current context, where technologies and information systems spread and become more and more diverse, the transportation community is getting more and more interested in the potential of data fusion processes to help gather more complete datasets and help give additional utility to available data sources. Research is looking for ways to enhance the available information by using both various data collection methods and data from various sources, surveys or observation systems. Survey response rates are decreasing over the world, and combining survey modes appears to be an interesting way to address this problem. Letting interviewees choose their survey mode allows increasing response rates, but survey mode could impact the data collected. This paper first discusses issues rising when combining survey modes within the same survey and presents a method to merge the data coming from different survey modes, in order to consolidate the database. Then, it defines and describes the data fusion process and discusses how it can be relevant for transportation analysis and modelling purposes. Benefiting from the availability of various datasets from the Greater Montréal Area and the Greater Lyon Area, some applications of data fusion are constructed and/or reproduced to illustrate and test some of the methods described in the literature.

Purpose — The introduction of new technology to public transport systems has provided an excellent opportunity for passive data collection. In this paper, we explore the possibility of automatically generating level of service indicators that could be used for operation planning and monitoring of Transantiago, the public transport system of Santiago, Chile.

Design/methodology/approach — After basic processing of the raw automatic vehicle location (AVL) and automatic fare collection (AFC) data, we were able to generate bus speed indicators, travel time measurements and waiting time estimates using data from 1week. The results were compared with manual measures when available.

Findings — The advantage is that these measurements and estimates are reliable because they are obtained from large samples and at nearly no cost. Moreover, they can be applied to any set of data with a selected periodicity.

Research limitations — The scope of this research is limited to what can be observed with AVL and AFC data. Additional information is required to incorporate other dimensions, such as personal characteristics and/or more detail in the origin/destination (OD) of the trips.

Practical implications — Nevertheless, these results are valuable for the planning and operation management of public transport systems because they provide large amounts of information that is difficult and expensive to obtain from direct measurements.

Originality/value — This paper proposes tools to obtain valuable information at a low cost. These tools can be implemented in many cities that have certain technological devices incorporated into their public transport systems.