AI-Based Mode of Transportation and Destination Classification and Prediction in Origin-Destination Surveys
Notice bibliographique
Résumé
Travel patterns and mode choice depend on individual socio-economic attributes that need better understanding. As a result, deciding which features to investigate is a challenge in data analysis. \nThis study investigates people's activities and trips to explore the correlation between individual and household socio-economic attributes, neighbourhood socioeconomic level and land use, and the choice of mode of transportation to access destinations in the city of Montreal. The study found that the land-use characteristics of Montreal and the shapes of its residents' travel patterns impact the design and implementation of public transportation projects throughout the census agglomeration of Montreal. These transportation infrastructure influences people's commuting behaviour patterns. How to predict these patterns using historical data and existing master plans is a major goal of this work. Machine learning and deep learning algorithms were used to predict trip destination and mode of transportation. \nNumerous factors influence a person's travel pattern, including their age, residence location, and purpose of the trip. The most critical attributes were detected based on feature extraction methods and correlations between features were analyzed using a correlation heat map. This allowed to determine the most significant features to predict the trip's destination and mode of transportation. \nThree most recent versions (2008-2013-2018) of the Montreal Origin-Destination (OD) data were used. Furthermore, a comparison between the accuracy of several well-known algorithms, such as decision trees, random forests, SVMs, and feedforward neural networks, was conducted. Comparing different results yielded from different algorithms shows that neural networks outperform all the other algorithms in terms of accuracy in predicting both modes of transportation and destination (78 percent in mode choice and 68.7 percent in destination). Therefore, it was used to predict the future trip pattern of the year 2023. \nMoreover, this study proposes a Bayesian network to forecast the entire trip patterns for Montreal in 2023. This network is used to create a scaled-down version of OD2023. For this purpose, both OD and census data were used for the past 15 years. Different characteristics of trip patterns in each year were plotted. The Bayesian network captured and modelled how the trips changed over time. \nThis study provides a baseline for developing an application to extract critical statistical information about trip patterns on a neighbourhood scale in Montreal. Finally, the foundations for an application to extract critical statistical information about various trip patterns in various Montreal neighbourhoods were created. \nThis section combined various datasets from different years, including Census, land \nuse, and OD survey data. This application displays the extracted data in various plots and tables. \n \nThis research is meant to serve as a summary of previous studies as well as a reference for future research.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».