The integration of advanced technologies has led to drastic changes in the field of urban planning and management. When using large amounts of data from numerous sources, machine learning models can mimic past scenarios which predict future events. Urban planners use these predictions when making infrastructure or administrative decisions geared towards a highly efficient and sustainable urban environment. Machine learning algorithms can reduce the wait times at intersections, stop-and-go traffic, and overall congestion by adjusting signal timings on a real-time basis according to live density of vehicles. Conventional bottlenecks are identified and possible route changes proposed to generate improved traffic flow across regions. Further research may continue encouraging urban planning and management innovation.
Machine Learning and Robotics in Urban Planning and Management explores the integration of machine learning and robotics technology in urban and regional development. It examines solutions for traffic management, infrastructure improvements, and prediction models using intelligent technology. This book covers topics such as neural networks, smart cities, and transportation systems, and is a useful resource for urban developers, business owners, scientists, civil engineers, academicians, and researchers.