[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123506-en":3,"doc-seo-123506-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123506,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Machine Learning-Driven Strategies for Efficient Traffic Congestion Management - Doctor of Philosophy Thesis","Urban regions face traffic congestion that increases trip durations, fuel consumption, and pollution. This thesis develops a three-step, machine learning–driven framework for efficient congestion management. Step 1 performs proactive congestion prediction across Montreal zones by comparing LSTM, Decision Tree, RNN, ARIMA, and SARIMA, identifying Decision Tree as superior. Step 2 uses Enhanced Bat Algorithm (EBAT) to adaptively optimize traffic signal cycle times under predicted congestion, improving convergence speed and solution quality versus fixed-time and non-predictive baselines. Step 3 applies multilevel learning combining anomaly detection, data cleansing, and ensemble stacking/voting, with clustering (K-Means, Hierarchical) for anomaly characterization, yielding higher prediction accuracy.","Machine Learning-Driven Strategies for Efficient Traffic  \nCongestion Management  \nMohammed Khasawneh  \nA Thesis  \nin  \nThe Department  \nof  \nInformation and Systems Engineering  \nPresented in Partial Fulfillment of the Requirements  \nfor the Degree of  \nDoctor of Philosophy (Information and Systems Engineering) at Concordia University  \nMontral, Qubec, Canada  \nSeptember 2024  \n© Mohammed Khasawneh, 2025  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis  \nprepared By: Mohammed Khasawneh  \nEntitled: Machine Learning-Driven Strategies for Efficient Traffic Congestion  \nManagement  \nand submitted in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy (Information and Systems Engineering)  \ncomplies with the regulations of this University and meets the accepted standards with respect to originality and quality.  \nSigned by the Final Examining Committee:  \n  Chair  \nDr. Andrea Schiffauerova  \n  External Examiner  \nDr. Golam Kabir  \nDr. Fuzhan Nasiri  Arm’s Length Examiner  \n  Examiner  \nDr. ManarAmayri  \n  Examiner  \nDr. Farnoosh Naderkhani  \nDr. Anjali Awasthi  Thesis Supervisor  \nApproved by    \nDr. Farnoosh Naderkhani, Graduate Program Director  \n11/29/2024  Dr. Mourad Debbabi, Dean, Gina Cody School of Engineering and Computer Science  \nAbstract  \nMachine Learning-Driven Strategies for Efficient Traffic Congestion Management Mohammed Khasawneh, Ph.D.  \nConcordia University, 2025  \nUrban regions have a notable obstacle in the form of traffic congestion, which results in longer trip durations, higher fuel usage, and increased pollution levels. This study aims to tackle this issue by presenting a three step approach. The first approach uses Machine learning for Proactive Traffic Congestion Prediction. We explore multiple machine learning algorithms, such as Long Short-Term Memory (LSTM), Decision Tree (DT), Recurrent Neural Network (RNN), AutoRegressive Integrated Moving Average (ARIMA), and Seasonal ARIMA (SARIMA), to predict traffic congestion levels in different zones of the Montreal area. The results indicate that the Decision Tree approach surpasses other algorithms, attaining faster convergence, lower loss values, and a considerably higher R2 score. After predicting the congestion using one of the prediction algorithms mentioned above, metaheuristic optimization algorithms are used to find near optimal cycle time for each traffic light. In step 2 Enhanced Bat Algorithm (EBAT) is proposed to adaptively modify traffic signal timings based on expected congestion levels. The EBAT algorithm utilizes adaptive parameter adjustment and guided exploration techniques that are dependent on the expected congestion. This results in enhanced performance when compared to the conventional Bat Algorithm. We conduct a comparative analysis of EBAT with various meta-heuristics, namely Particle Swarm Optimization (PSO), Cuckoo Search (CS), JAYA, Sine Cosine Optimization (SCO), and Harris Haws Optimization (HHO) . The evaluation considers three scenarios: fixed-time traffic lights (baseline), dynamic traffic lights without prediction, and dynamic traffic lights with predicted congestion. The results demonstrate that EBAT yields substantial enhancements in both the rate at which convergence is achieved and the quality of the solutions, as compared to fixed and non-predictive scenarios. The  \nsecond approach is using Multilevel Learning for Enhanced Prediction Accuracy. The precision of  \npredicting traffic congestion depends on the ability to recognize and manage abnormal traffic patterns, especially in highly populated regions. Traditional prediction methods are vulnerable to these anomalies, as they frequently do not handle or clean the data. This can result in inaccurate forecasts, as the data may encompass anomalous occurrences such as accidents or unforeseen road closures, which can greatly distort the underlying trends. The study presents a novel and creative strategy to le","cbCairPve2Z49r1L","https://ap.wps.com/l/cbCairPve2Z49r1L","pdf",22408451,1,141,"English","en",105,"# Abstract\n## Proactive traffic congestion prediction\n## EBAT-based dynamic signal optimization\n## Multilevel learning for improved prediction accuracy\n## Anomaly detection and data cleansing\n## Applications and contributions","[{\"question\":\"What problem does the thesis address in urban transportation?\",\"answer\":\"The thesis targets traffic congestion that leads to longer trip durations, higher fuel usage, and increased pollution levels in urban areas.\"},{\"question\":\"How does the first approach predict traffic congestion?\",\"answer\":\"It uses multiple machine learning models (including LSTM, Decision Tree, RNN, ARIMA, and SARIMA) to predict congestion levels in different Montreal zones, with Decision Tree delivering the best results.\"},{\"question\":\"What is EBAT and how is it used after prediction?\",\"answer\":\"EBAT (Enhanced Bat Algorithm) adaptively modifies traffic signal timings by optimizing near-optimal cycle times for each traffic light based on expected congestion, outperforming conventional bat-based and other metaheuristic methods under tested scenarios.\"},{\"question\":\"How does multilevel learning improve prediction accuracy?\",\"answer\":\"It combines anomaly detection with data cleansing, then trains initial learners and builds an ensemble via stacking and voting. It further segments and characterizes anomalies using clustering (K-Means and hierarchical clustering) to better manage abnormal traffic patterns.\"}]","Machine Learning-Driven Strategies for Efficient Traffic Congestion Management - Doctor of Philosophy Thesis | PDF",1785816916,355,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"machine-learning-driven-strategies-for-efficient-traffic-congestion-management-doctor-of-philosophy-thesis","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-driven-strategies-for-efficient-traffic-congestion-management-doctor-of-philosophy-thesis/123506/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address in urban transportation?","Question",{"text":75,"@type":76},"The thesis targets traffic congestion that leads to longer trip durations, higher fuel usage, and increased pollution levels in urban areas.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the first approach predict traffic congestion?",{"text":80,"@type":76},"It uses multiple machine learning models (including LSTM, Decision Tree, RNN, ARIMA, and SARIMA) to predict congestion levels in different Montreal zones, with Decision Tree delivering the best results.",{"name":82,"@type":73,"acceptedAnswer":83},"What is EBAT and how is it used after prediction?",{"text":84,"@type":76},"EBAT (Enhanced Bat Algorithm) adaptively modifies traffic signal timings by optimizing near-optimal cycle times for each traffic light based on expected congestion, outperforming conventional bat-based and other metaheuristic methods under tested scenarios.",{"name":86,"@type":73,"acceptedAnswer":87},"How does multilevel learning improve prediction accuracy?",{"text":88,"@type":76},"It combines anomaly detection with data cleansing, then trains initial learners and builds an ensemble via stacking and voting. 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