Separated Cycling Infrastructure and Bike Share Ridership: Furthering Causality through GPS Data
Notice bibliographique
Résumé
Cycling, and micromobility tools like bike share, have increasingly been recognized for their health, economic and environmental benefits, and municipalities have recently made encouraging the use of these modes of urban transportation both a policy and a financial priority. Many studies, using varying methods, have identified and confirmed an association between an increased presence and connectivity of cycling infrastructure (bike lanes, cycle tracks, etc.) and cycling or bike share ridership. Determining a more explicit causal link between infrastructure and ridership, however, often proves challenging to researchers, due to data limitations and a variety of simultaneous, exogenous, factors that abound within complex urban transportation systems. Given the financial impacts of capital investment in infrastructure, more closely establishing this causal link, and identifying infrastructure’s ability to generate cycling and bike share traffic, is of growing importance to municipal governments and taxpayers. Using Hamilton Bike Share (HBS) trip logs and GPS trajectories occurring between January, 2019 and August, 2022 (n = 741,369 and 609,746, respectively), this thesis constructs individual shapefiles of each HBS trip for GIS analysis through Dalumpines and Scott’s (2011) GIS-Based Map-Matching Algorithm. It investigates the impact of ten separated cycling infrastructure projects in Hamilton, constructed between 2019 and 2022, on HBS ridership along the respective intervention segments. The thesis also holistically analyzes the spatial and ridership impacts of one infrastructure intervention, the Victoria Avenue cycle track, on the distribution of riders using the segment of interest, a more precise classification of post-intervention trip natures (‘induced’ or ‘diverted’) using a novel categorization process, and maps the impact of the iv segment on trip diversion to use the cycle track. Results indicate that five of the ten interventions have had significant, positive, impacts on monthly HBS ridership along their respective segments, with others having nearly statistically significant results as well. Moreover, the Victoria Avenue cycle track lessened the cost of distance associated with using Victoria Avenue, and 46.9% of trips along the cycle track post-intervention, were determined to be ‘induced’ trips. Finally, of the streets in the Victoria Avenue cycle track’s neighborhood, the cycle track segments were the only segments to experience ridership increases post-intervention, which indicates a significant level of trip diversion and funneling of trips to use the cycle track. These results enhance findings from the literature and more concretely quantify the direct impacts of infrastructure investments. Investments in infrastructure appear to make a significant difference in increasing ridership and serve to benefit more than just existing riders. This thesis can have an important impact on municipal active transportation planning, policy, and financing, through its results and by providing a methodological foundation for future research into infrastructure’s impacts on a variety of users.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».