Bibliographic record
Abstract
A recurring issue is that of increased car dependence in major North American cities. Policy makers are challenged to find new and innovative solutions to counter the negative externalities of this personal vehicle dependence. For instance, the air pollution and greenhouse gas emissions resulting from private vehicular travel is of a particular concern for the health and safety of future generations. Moreover, the prevalence of sub-urban life in North American cities in the recent years has resulted in increased private vehicle usage while reducing public transportation systems usage. A well planned and efficient public transportation system can provide equitable service and accessibility to the population as well as contributing to the reduction of air pollution and GHG emissions. An effective solution lies in transit agencies and government implementing policies that maximize transit use and minimize car dependence. Not surprisingly, many urban regions are enhancing public transportation infrastructure to address the private vehicle use challenge. A number of research efforts have been focussing on understanding individual behavioral challenges in using transit while several other studies have examined the factors affecting transit operations. These studies provide important information to local agencies and transit agencies to enhance public transit services and operations. This thesis is a collection of three distinct studies, each relating to public transportation issues from different perspectives. The first study examines individual home to work/school commute patterns in Montreal, Canada with an emphasis on the transit mode of travel. The overarching theme of this research is to examine the effect of the performance of the public transportation system on commuter travel mode and transit route choice (for transit riders) in Montreal. We investigate two specific aspects of commute mode choice: (1) the factors that dissuade individuals from commuting by public transit and (2) the attributes that influence transit route choice decisions (for those individuals who commute by public transit). The second study is an effort to develop a framework for a better understanding of commuter train users' mode and station choice behavior. Typically, mode and station choice for commuter train users is modeled as a hierarchical choice with mode being considered as the first choice in the sequence. This research proposes a latent segmentation based approach to relax the hierarchy. In particular, this innovative approach simultaneously considers two segments of station and access mode choice behavior: Segment 1 - station first and mode second and Segment 2 – mode first and station second. The allocation to the two segments is achieved through a latent segmentation approach that determines the probability of assigning the individual to either of these segments as a function of socio-demographic variables, level of service (LOS) parameters, trip characteristics, land-use and built environment factors, and station characteristics. Finally, the third study draws attention to the spatial characteristics affecting transit ridership. An analysis of bus stop level boarding and alighting is undertaken by developing ordered response models of the bus stop specific boarding and alighting by time of day. The analysis quantifies the influence of various exogenous factors including public transit accessibility indices (number of bus/metro/train stops around each stop, length of bus/metro/train lines, length of exclusive bus lanes), infrastructure attributes (road length by functional classification, bike lane lengths, distance to central business district, CBD), and land use measures (number of parks and their areas, residential area, number of commerces and their area, government and institutional area, resource and industrial area, and population density).
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".