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The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

This chapter provides a reflective commentary on how the transition to electric vehicles (EVs) may alter how society uses cars through an inspection of evidence from the studies which have examined the impact of EV adoption on trip patterns. A framework for evaluating trip patterns is applied which considers how the adoption of an EV could generate impacts for the spatial distribution of car trips, when these trips occur, the journey purpose these trips serve, and the driving style in which the trips are conducted. It is identified that the principal issue which is likely to motivate alterations in trip patterns following a transition to EVs is the technical and regulatory differences which distinguish them from conventional vehicles. Spatial trip patterns could become anchored to the burgeoning chargepoint infrastructures, with network coverage having implications for where EVs will be seen. Changing seasons could reduce the range of the battery packs, limiting the useability of EVs in winter months. Low operating costs of EVs may encourage their use for short distance trips due to a feeling of guilt-free travel. Eco-driving functions of EVs could promote sustainable driving practices by gamifying energy efficiency though the introduction of targets, medals, and leader boards. It is concluded that the exact manner in which trip patterns will be altered by the transition to EVs is difficult to predict with clarity, with many alternative futures being conceivable. In part, the impact on trip patterns will be contingent on whether or not EVs start to look and feel like conventional cars as the technology matures.

For a long time, telecommuting has been expected to affect the aggregated travel pattern. A number of cause‐effect relationships between telecommuting and travel have been identified in literature concerning different types of trip with both decreased and increased travel as the outcome. To explore how telecommuting affects travel and travel patterns in Sweden an empirical study was conducted. The most important cause‐effect relationship concerns three categories: work‐trips, non‐work‐related trips and combination trips. The travel pattern, which is based on the telecommuter's regularity of trips, the point in time for different types of trip and the travel mode used, is also studied. The present results are compared with international findings, with the aim to create better understanding of how telecommuting affects the telecommuter's travel pattern and approximately estimate the magnitude of the travel impact. Finally, there is a contextual discussion concerning the probable total travel effects of telecommuting.

People with autism and their families struggle with travel because of its complexity. This study aims to promote travel participation by exploring the travel needs and patterns of people with autism and their families.

A total of 28 participants were interviewed. Data were analyzed via constructivist grounded theory.

People with autism and their families displayed five travel patterns: mutual support, relatives’ visitation, independent travel, expanded socialization and package tours. These patterns were adopted in a stepwise fashion as the autistic individuals’ abilities improved. The travel challenges and support needs of Chinese autistic people and their families were identified.

The findings can inform accessibility tourism, promote an understanding of autistic people’s tourism activities among the public and industry marketers and offer strategic guidance about family travel for this population.

This effort responds to a call to investigate disability-related issues. The study evaluated the travel behavior of people with autism and their families, from a developmental perspective, presenting a new angle in research on accessibility tourism.

It has been established that values, beliefs and norms are good predictors of pro-environmental behaviour; however, it is less explored how these constructs can be influenced. The purpose of this paper is to assess whether the COVID-19 pandemic, as a shock event, was a “game changer” or it had limited impacts on deep-rooted travel patterns.

To understand whether the pandemic has influenced people’s value orientations with regards to sustainable travel, the authors have made use of the value–belief–norm (VBN)theory of environmentalism and conducted a large-scale survey in Germany, Italy, France, The Netherlands and UK (N = 1545).

The findings showed that the pandemic caused only temporary change in deep-rooted travel patterns as (self-reported) pre-pandemic behaviour of respondents align with their value orientations and proved to be a good predictor of projected travel behaviour. Based on projected travel behaviour and value orientations, four market segments were identified – Frontrunners, Laggards, Comfortable Crowd and Entitled Stewards. While all segments showed willingness to adapt, the authors found variations in the extent of adaption and in the phases of the customer journey where travellers are likely to make changes.

According to our knowledge, using the VBN theory of environmentalism to understand the impact of shock events on deep-rooted travel patterns represents a new perspective. Moreover, to the best of authors’ knowledge, this study is one of the first, written in the midst of the COVID-19 pandemic, which looks at the impact of the health crisis on deep-rooted travel patterns, instead of the commonly studied risk perception.

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.