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Daily mobility is an ordinary problem that fits into the complexity of everyday household life. Mobility is also a dimension of identity, a predisposition which gives individuals a specific framework for dealing with activities and travels. Thus, households’ choices are made with a wide range of rational elements (economic, time, accessibility, etc.), but also with more sensible elements (preferences, past experiences, satisfaction, etc.). In addition, large-scale urban projects aim to improve modal split and to facilitate households’ commutes conditions. Since poles of employment generate the main concentrated flows of urban travel, those poles are at the core of strategic planning. Beyond improving mobility, changing job location may involve contrasted issues for households, positive (e.g., travel efficiency, sustainable mobilities, etc.) and negative (e.g., freedom of choice, undesirable mode change). This paper focuses on an exceptional workplace relocation (2015) in Montréal – McGill University Health Center – and explores the daily commute choices for more than 10,000 metropolitan workers. While metropolitan accessibility is sharply improved, the new workplace offers a low-quality mobility environment compared to former ones (n=5) located downtown. What mobility choices do metropolitan employees make in order to rebuild their travel routines? What is the importance of both mobility habits and mobility identities in commute changes/satisfaction? Using an internet retrospective survey (n=1 977) conducted with the concerned workers, mobility strategies have been explored. Principal component analyses and clustering are used to take into account lifestyles typology into a logistic regression model in order to explain mode choice according to identities, workers’ socioeconomic characteristics, built environment and structural urban characteristics. Beyond the importance of accessibility, attitudes towards mobility have strong explanation power for specific workers' profiles. These variables should be better embraced by transportation plans.

In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentation-based fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filter-based feature selection approach. The selected feature is further segmented by utilizing the bottom-up time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rate-of-change (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state fault-severity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the fault-severity classification is carried out by kernel-based support vector machine (SVM) classifier. Next to SVM, the k-nearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine sliding-chair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentation-based failure severity detection and SVM-based classification are promising.