Mobility practices of non- motorized households, the cases of Quebec City and Strasbourg
Bibliographic record
Abstract
By the simple fact of not owning a private automobile, some households are already living a sustainable mobility lifestyle. They rely on public transportation, walking, cycling and car sharing and generally travel less than car-owning households. According to Tabbone (forthcoming) non-motorized households in France consume on average 5850 kWh less per year than motorized households, representing 34% less energy consumption than the average for motorized urban and periurban households1. Even when we look only at the energy consumption footprint inside the residence, excluding mobility, non-motorized households still consume on average 9% less than their motorized counterparts. The good news is that in many European cities, in Geneva and Lausanne for example, the number of non-motorized households is rapidly increasing (Fabbo et al., 2014, p. 311). Although non-motorized households is a group showing sustainable practices in their daily mobility, current sustainable mobility policies often do not take into account these kinds of households, focussing instead on convincing car owners to shift modal behaviours (Grengs, 2005, p. 52; Kaufmann, Jemelin, Pflieger, & Pattaroni, 2008, p. 18). Sometimes non-motorized feel socially excluded, because their mobility is limited for a lack of private car (Schönfelder & Axhausen, 2003, p. 273). The situation of people deprived of motorized vehicles in a car- dependent context appears as an exemplary case to study the complex intertwining of justice issues in contemporary cities. In order to explore this phenomenon and shed light on the mobility practices of non-motorized households living in car-dependent Western cities we met with 57 non-motorized households in Quebec City, Canada and Strasbourg, France as part of our PhD dissertation. We use discourse analysis software (IRaMuTeQ) to analyze and compare the verbatim transcript of our interviews between the two cities. We would like to present our findings at the Swiss Mobility Conference in the discussions related to mobility “actors and their logics of action (residential choice, modal practices, multilocal living, etc)”. Through our lexocimetric analysis we have exposed the different discourse that non-motorized households have when reflecting on which activities or area 1 Based on a sample representative of the entire population of France in 2013 that cannot partake in due to their lack of private vehicles. Based on different variables: gender, feeling excluded or not, living in Quebec City or Strasbourg, being a member or not of the car-sharing system and revenue level, we can show that their discourse varies and discuss these differences. We generate these results by performing a specificity analysis and generate a word cloud for each variable’s modality. For example, we show that non-motorized households in Quebec City have particular problems going to a cinema, the hardware store or visiting relatives and face a lot of waiting while the households of Strasbourg have difficulty with grocery and weekend getaways to popular destinations like the Vosges mountain range or villages. During our interviews, we have also discussed the daily mobility of these households for various reasons: work, shopping, visiting family and friends and leisure. Through similar analysis, we show the difference in discourse based on our variables. For example, while women seem to rely on carpooling and discuss specific bus routes, men seem to rely on car sharing and car rentals as well as the bicycle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".