Decision making for urban mobility: a macro, meso and micro analysis
Bibliographic record
Abstract
Urban congestion is a challenge that cities commonly suffer across the globe. Traffic congestion and longer commutes are linked with poor cardiovascular and metabolic health, along with decreased energy and increased stress among the users. This is further translated into productivity and economic loss, an increase in health service expenses and a general decrease in the quality of social wellbeing. \nTo improve this condition, the municipality administration has the role of implementing solutions to strategically address urban mobility. However, this is a complex task to achieve and normally involves limited resources, which make real-world deployments have a great inherited risk. Thus, decision-making is a task that has to be carefully addressed by different factors and scales. \nThis thesis approaches multiple tools for analytics on urban mobility using skills in SQL, R and Python, and open-source software such as QGIS for spatial analysis and SUMO (Simulation of Urban Mobility) for microsimulation. The methodology includes the analysis of urban mobility in Montreal from different levels of analysis. \nAt the macro level, the MTL Trajet dataset provides insight of mobility behaviour of participants through their trip coordinates. Using geometry datasets of quarter and boroughs of Montreal, the analysis is framed and processed via SQL and QGIS. Data visualization is presented in Choropleth maps, Flow maps and Chord diagrams using origin and destination of trips. Supporting processing task such as reverse geocoding to join attributes between datasets are used. The macro analysis helps to identify a primary area of analysis seeking the most transited region. The quarter of René-Lévesque in/and the borough of Ville-Marie are the most accessed areas in this study. \nIn the meso level, street network information from OpenStreetMap allows making relations among the elements of the area, such as universities and their proximity to pedestrian zones. Resulting maps aid decision-making from a meso perspective, choosing the area of Concordia University as a suitable space for microfocus. \nAt the micro-level, four areas of opportunity interpreted as transit policy-testing were identified. A custom micro-network and synthetic demand for this area were used to simulate the impacts of these scenarios. The measures tested to improve urban mobility in the area are the restriction of street lanes for specific vehicle types and the inclusion of pedestrian areas. Experimentations with different levels of user modal share and shift are presented. \nResults of macro, meso and micro analyses are included to provide recommendations for the administration of the city of Montreal. The inclusion of multiple restrained lanes for buses and high-occupancy vehicles around Concordia University and a pedestrian zone will allow to save time to road users, as long as single-passenger vehicle shifts towards public transit and shared–vehicles.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".