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Given the recent price increases of fuels in France and the protest movement of the Yellow Vests, we analyze the price and income elasticities according to income level and place of residence in France during the period 1984-2018 from data of the ParcAuto panel survey. We show that the households most affected by fuel price increases are the least affluent and those living in low-density areas, who are likely to be more captive of car-use. The very low short-run elasticities, that are higher in the longer term show the difficulty of adaptation of these populations and the importance of taking into account income disparities when introducing a carbon tax by introducing compensatory measures and privileging gradual increases for better acceptability.

The global positioning system (GPS) has motivated rapid advances in mobility data collection. A massive amount of spatio-temporal information has made it possible to know where a person was and when, but not how and why (s)he travelled, creating the need for inference models. Compared with mode detection, purpose imputation has been insufficiently studied. However, the relative lack of attention to purpose identification does not mean that this field has not emerged. For this paper, which is the first review dedicated to inferring trip purposes from GPS data, 1162 non-duplicate papers from four databases (Scopus, Web of Science, ScienceDirect and TRID) were screened, and a corpus of 25 publications was selected for examination. Based on these papers, the purpose imputation problem is defined in the contexts of the evolution of GPS-based travel surveys and two research domains, transportation science (TS) and human geography (HG). Subsequently, three steps of the purpose detection process, namely trip end detection, input feature selection and main algorithms and validation, are surveyed. During these procedures, the differences between studies in TS and those in HG are highlighted. Finally, unresolved issues related to data and feature selection, algorithms and assessment are discussed substantially to provide potential research directions. This review may be an informative reference for those newly accessing the GPS-based purpose imputation field and/or intending to develop solutions to this problem.

Recent trends in France and other developed countries such as a reduction in drivers’ license ownership and stagnation of daily urban mobility suggest that « peak travel » within urban areas may have been reached. Are we assisting to cyclical or structural changes ? We tried to answer to this question by applying a prospective demographic Age-Cohort model to the Metropolitan Area of Lille, calibrated from household travel surveys conducted in 1987, 1998 and 2006. The model permits to isolate age and generation effects linked to the life cycle. Population ageing explains only partially the decline of daily traveled distance per person. Indeed there are significant changes in behavior. According to the model the proportion of adults with a driver’s license should remain almost constant till 2030. Forecasts derived from the Age-Cohort model show a slowdown of travel distance increase, when the model is calibrated on 1987 and 1998 surveys, while it shows a slow decline from 2000 to 2030 when calibrated on the two most recent surveys. Concerning the revival of public transport observed around the year 2000, calibrated with the 1987-1998 surveys, the model shows an increase by 18 % between 2000 and 2030, and it could reach +38 % when calibrated with the 1998 and 2006 surveys.The Age-Cohort model, based on the case study of Lille, shows that population ageing explains only partially the saturation in daily mobility observed in the 2000’s. However, ageing, which gives less weight to the younger generations, amplifies the phenomenon. The significant differences observed in the behaviors until the 90’s and in the 2000’s, explain well the reversal of tendency observed which leads to diverging projections.