Integrating Machine Learning and Deep Learning in Smart Cities for Enhanced Traffic Congestion Management: An Empirical Review
Journal Title: Journal of Urban Development and Management - Year 2023, Vol 2, Issue 4
Abstract
In the rapid urbanization experienced globally, traffic congestion emerges as a critical challenge, detrimentally affecting economic performance and the quality of urban life. This study delves into the deployment of machine learning (ML) and deep learning (DL) methodologies for mitigating traffic congestion within smart city frameworks. An extensive literature review coupled with empirical analysis is conducted to scrutinize the application of these advanced technologies in various transportation domains, including but not limited to traffic flow prediction, optimization of routing, adaptive control of traffic signals, dynamic management of traffic systems, implementation of smart parking solutions, enhancement of public transportation systems, anomaly detection, and seamless integration with the Internet of Things (IoT) and sensor networks. The research methodology encompasses a detailed outline of data sources, the selection of ML and DL models, along with the processes of training and evaluation. Findings from the experiments underscore the efficacy of these technological interventions in real-world settings, highlighting notable advancements in the precision of traffic predictions, the efficiency of route optimization, and the responsiveness of adaptive traffic signal controls. Moreover, the study elucidates the pivotal role of ML and DL in facilitating dynamic traffic management, anomaly detection, smart parking, and the optimization of public transportation. Through illustrative case studies and examples from cities that have embraced these technologies, practical insights into their applicability and the consequential impact on urban mobility are provided. The research also addresses challenges encountered, offering a discourse on potential avenues for future research to further refine traffic congestion management strategies in smart cities. This contribution significantly enriches the existing corpus of knowledge, presenting pragmatic solutions for urban planners and policy makers to foster more efficient and sustainable transportation infrastructures.
Authors and Affiliations
A. B. Feroz Khan, Perl Ivan
Keywords
Related Articles
- EP ID EP732657
- DOI https://doi.org/10.56578/judm020404
- Views 73
- Downloads 0
The Impact of Urban Governance on Enhancing Resilience in Informal Settlements: A Case Study from Jafarabad, Kermanshah
Informal settlements, home to a significant segment of the global population, are often overlooked by policymakers and urban planners, resulting in inadequate infrastructure, limited access to essential services, and pre...
Enhancing Urban Development with Picture Fuzzy Sets: A Strategic Decision Support Framework
In the realm of sustainable urban development, a paramount focus is placed on the amalgamation of environmental conservation, the integration of smart technology, and the promotion of social inclusivity. This approach ad...
Human Resource Dynamics in Urban Crowd Logistics: A Comprehensive Analysis
The advent of Information and Communication Technologies (ICT) has significantly revolutionized urban logistics, particularly through the emergence of crowd-sourced platforms. This evolution has engendered substantial be...
Quality of Life During COVID-19 Global Pandemic as the Implementation of Physical Distancing in Medan City - Indonesia
Quality of Life is important but often neglected, especially during the COVID-19 pandemic. Health and economic aspects are currently getting more attention. Therefore, this study tries to measure the Quality of Life of t...
Uncontrolled Urban Growth in Western Balkans Territories after the Communist Collapse—a Review from the Spatial Planning Perspective
With the fall of the Communist Regimen in the Western Balkans Region, governance politics regarding urban and spatial planning processes led to significant changes in several territorial aspects. In this regard, several...