Travel behaviour of Asian & European immigrants in the Greater Toronto and Hamilton area
Bibliographic record
Abstract
Without a better understanding of travel behaviour by diverse immigrant groups, the nuances of immigrant mobility needs may go unmet. Statutory transportation planning requirements focus on mobility “needs”. But social understandings of what travel is “needed” for daily life changes over time varies between different population groups. In many major immigrant settlement areas, immigration has become the primary, if not sole, reason for population growth (Thomas, 2013). Evidence continues to emerge indicating that immigrants use non-auto modes significantly more than non-immigrants but slowly assimilate in becoming more auto-oriented (Chatman, 2013). Likewise, mobility differences between ethnic groups persist (Hu et al., 2020). In the absence of an understanding of the travel behaviour of specific immigrant groups and changes over time, there is a distinct possibility for a significant disconnect between understandings of what transportation services are provided and what services are needed (Blumenberg, 2009; Chatman & Klein, 2009). Towards bridging this gap, this study explores auto ownership and daily vehicle travel among self-identified immigrants and non-immigrants of either self-identified Asian or European descent in the Greater Toronto and Hamilton area. Using data from a 2018 travel survey of residents of the Greater Toronto and Hamilton Area, this study explores travel behaviour in relation to immigrant status and the length of residence in Canada and several mobility measures, including vehicle travel and auto ownership. This study begins by using the survey data to present descriptive statistics related to differences between immigrant groups, based on length of residence in Canada and based on auto ownership and vehicle-kilometers traveled (VKT). Next, inferential models were estimated to formally test the links between travel behaviour outcomes and immigrant status. Logistic regression models of household vehicle ownership were estimated before Tobit regression models of daily VKT were estimated. Descriptive study findings support the expectation of significant differences between immigrant status (based on length of stay) and self-identified ethnicity (Asian or European) and both vehicle ownership and VKT. However, inferential models show a more complex story. Models of household vehicle ownership (yes/no) indicate that being a recent immigrant to Canada is associated with lower vehicle ownership rates, but that effects attenuate rapidly – implying rapid assimilation. Tobit models from this study of daily VKT suggest that these lower rates of auto ownership translate weakly into VKT reductions. Evidence is suggestive of a VKT rebound among Asian populations – wherein some immigrants exceed VKT expectations of non-immigrant groups. These findings beg to question why Asian immigrants use their vehicles so intensely and how mobility gaps are overcome so quickly. Key Words Asian immigrants, European immigrants, travel behaviour, assimilation, Toronto, Hamilton
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".