A better understanding of mobility behaviors is relevant to many applications in public transportation, from more accurate travel demand models to improved supply adjustment, customized services and integrated pricing. In line with this context, this study mined 51 weeks of smart card (SC) data from Montréal, Canada to analyze interpersonal and intrapersonal variability in the weekly use of public transit. Passengers who used only one type of product (AP − annual pass, MP − monthly pass, or TB − ticket book) over 12 months were selected, amounting to some 200,000 cards. Data was first preprocessed and summarized into card-week vectors to generate a typology of weeks. The most popular weekly patterns were identified for each type of product and further studied at the individual level. Sequences of week clusters were constructed to represent the weekly travel behavior of each user over 51 weeks. They were then segmented by type of product according to an original distance, therefore highlighting the heterogeneity between passengers. Two indicators were also proposed to quantify intrapersonal regularity as the repetition of weekly clusters throughout the weeks. The results revealed MP owners have a more regular and diversified use of public transit. AP users are mainly commuters whereas TB users tend to be more occasional transit users. However, some atypical groups were found for each type of product, for instance users with 4-day work weeks and loyal TB users.

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Despite the desired transition toward sustainable and multimodal mobility, few tools have been developed either to quantify mode use diversity or to assess the effects of transportation system enhancements on multimodal travel behaviors. This paper attempts to fill this gap by proposing a methodology to appraise the causal impact of transport supply improvement on the evolution of multimodality levels between 2013 and 2018 in Montreal (Quebec, Canada). First, the participants of two household travel surveys were clustered into types of people (PeTys) to overcome the cross-sectional nature of the data. This allowed changes in travel behavior per type over a five-year period to be evaluated. A variant of the Dalton index was then applied on a series of aggregated (weighted) intensities of use of several modes to measure multimodality. Various sensitivity analyses were carried out to determine the parameters of this indicator (sensitivity to the least used modes, intensity metric, and mode independency). Finally, a difference-in-differences causal inference approach was explored to model the influence of the improvement of three alternative transport services (transit, bikesharing, and station-based carsharing) on the evolution of modal variability by type of people. The results revealed that, after controlling for different socio-demographic and spatial attributes, increasing transport supply had a significant and positive impact on multimodality. This outcome is therefore good news for the mobility of the future as alternative modes of transport emerge.

The COVID-19 pandemic has led governments to implement restrictive policies which have caused unprecedented effects on transportation systems. This paper assesses which measures had more impacts on subway daily ridership in Montreal (Quebec, Canada) and on the interactions between modes using time series approaches. Change point detection methods, based on regression structure and Bayesian posterior probabilities, are first applied to automatic fare collection (AFC) data available from January 2019 to December 2021. Nine breakpoints (or ten phases) are found and linked to the COVID-19 timeline of the city. The impacts are then quantified by phase, and their variability is analyzed by day type and period. The evolution of the daily and weekly patterns in subway usage is also examined using time-frequency wavelet analysis. Finally, changes in correlations between the subway ridership and the use of three other transportation modes (cycling, private car, and carsharing) are modeled using interrupted time series models with autoregressive errors. The results reveal that lockdown implementations had a negative, immediate but decreasing impact on subway use, while release measures combined with transit-specific policies (such as free distribution of masks) led to a gradual recovery. The impacts varied in time, but traditional 5-day-a-week peak hour travel declined the most. The use of the other modes, as well as their system-level interactions with the subway, were also affected. Potential modal shifts were highlighted. Such findings provide practitioners and planners with useful insights into the COVID-19 pandemic impacts on mobility.

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