Optimizing Urban Commute Quality Through Traffic Congestion Analysis and Predictive Modeling
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| Title: | Optimizing Urban Commute Quality Through Traffic Congestion Analysis and Predictive Modeling |
|---|---|
| Authors: | Almansoori, Obaid |
| Committee Members: | Parthasarathi Gopal |
| Summary: | Urban traffic congestion imposes significant economic, environmental, and social costs on rapidly growing cities worldwide. This research investigates how predictive analytics and machine learning can be leveraged to classify and forecast traffic congestion severity in real time, enabling data-driven decision-making for transportation planning, signal optimization, and congestion management. A real-world traffic monitoring dataset comprising 5,952 observations collected over two months via computer vision sensors at an urban intersection was analysed under the CRISP-DM frame- work. The dataset records counts of four vehicle classes including cars, bikes, buses, and trucks at 15-minute intervals, alongside temporal variables such as time of day, date, and day of week. Analysis was performed using Python 3.11 with scikit-learn, scipy, pandas, and matplotlib, and cross-validated using IBM SPSS Statistics 29. Four supervised machine learning classification algorithms were trained and evaluated on a stratified 70/30 train-test split. XGBoost (Gradient Boosting Classifier) achieved the highest performance with an accuracy of 99.89%, weighted F1-score of 0.9989, precision of 0.9989, and recall of 0.9989 on the held-out test set of 1,786 observations, substantially outperforming Linear SVM (90.76% accuracy) and Logistic Regression (88.69%). Random Forest achieved 99.66% accuracy as the second-best model. Statistical analysis confirms highly significant associations between traffic situation and hour of day (Chi-Square = 2,413.211, df = 15, p < .001), PM peak period (Chi-Square = 1,138.848, df = 3, p < .001), and all vehicle count variables (Kruskal-Wallis H values ranging from 98.3 to 3,242.9, all p < .001). Exploratory analysis reveals that the PM Peak (16:00–18:00) |
| URL: | https://repository.rit.edu/theses/12565 |
| Database: | OpenDissertations |
| FullText | Text: Availability: 0 |
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| Header | DbId: ddu DbLabel: OpenDissertations An: ddu.oai.repository.rit.edu.theses.13690 AccessLevel: 6 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimizing Urban Commute Quality Through Traffic Congestion Analysis and Predictive Modeling – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Almansoori%2C+Obaid%22">Almansoori, Obaid</searchLink> – Name: Author Label: Committee Members Group: Au Data: <searchLink fieldCode="CO" term="%22Parthasarathi+Gopal%22">Parthasarathi Gopal</searchLink> – Name: Abstract Label: Summary Group: Ab Data: Urban traffic congestion imposes significant economic, environmental, and social costs on rapidly growing cities worldwide. This research investigates how predictive analytics and machine  learning can be leveraged to classify and forecast traffic congestion severity in real time,  enabling data-driven decision-making for transportation planning, signal optimization, and  congestion management. A real-world traffic monitoring dataset comprising 5,952 observations collected over two months via  computer vision sensors at an urban intersection was analysed under the CRISP-DM frame- work. The  dataset records counts of four vehicle classes including cars, bikes, buses, and trucks at  15-minute intervals, alongside temporal variables such as time of day, date, and day of week.  Analysis was performed using Python 3.11 with scikit-learn, scipy, pandas, and matplotlib, and  cross-validated using IBM SPSS Statistics 29. Four supervised machine learning classification algorithms were trained and evaluated on a  stratified 70/30 train-test split. XGBoost (Gradient Boosting Classifier) achieved the highest  performance with an accuracy of 99.89%, weighted F1-score of 0.9989, precision of 0.9989, and  recall of 0.9989 on the held-out test set of 1,786 observations, substantially outperforming Linear  SVM (90.76% accuracy) and Logistic Regression (88.69%). Random Forest achieved 99.66% accuracy as  the second-best model. Statistical analysis confirms highly significant associations between traffic situation and hour of  day (Chi-Square = 2,413.211, df = 15, p < .001), PM peak period (Chi-Square = 1,138.848, df = 3, p  < .001), and all vehicle count variables (Kruskal-Wallis H values ranging from 98.3 to 3,242.9, all  p < .001). Exploratory analysis reveals that the PM Peak (16:00–18:00) – Name: URL Label: URL Group: URL Data: <link linkTarget="URL" linkTerm="https://repository.rit.edu/theses/12565" linkWindow="_blank">https://repository.rit.edu/theses/12565</link> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ddu&AN=ddu.oai.repository.rit.edu.theses.13690 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English Subjects: – SubjectFull: XGBoost Type: general – SubjectFull: Gradient Boosting Type: general – SubjectFull: Traffic Congestion Classification Type: general – SubjectFull: Vehicle Count Analysis Type: general – SubjectFull: Urban Mobility Type: general – SubjectFull: Machine Learning Type: general – SubjectFull: Predictive Analytics Type: general – SubjectFull: Computer Vision Traffic Monitoring Type: general – SubjectFull: CRISP-DM Type: general – SubjectFull: Feature Importance Type: general Titles: – TitleFull: Optimizing Urban Commute Quality Through Traffic Congestion Analysis and Predictive Modeling Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Almansoori, Obaid IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Type: published Y: 2026 |
| ResultId | 1 |
