Authors: Dr.C.K Gomathy, Ananth Lakshmi ss, Lakshmi A
Abstract: Urban areas are becoming increasingly congested as populations grow and public spaces experience unpredictable fluctuations in foot traffic. This constant movement creates challenges for city planners, traffic authorities, and public safety teams who require reliable, real-time information to manage crowds efficiently. This research investigates the use of Big Data Analytics for predicting urban crowd flow by analyzing digital footprint signals generated through everyday human interaction with technology. These signals include smartphone GPS activity, Wi-Fi hotspot connections, public sensor logs, transport card swipes, and metadata from CCTV systems. By integrating these diverse and continuous data streams, the study proposes a multi-layered predictive framework capable of detecting mobility patterns, forecasting future crowd density, and supporting city-level decision-making. Through machine learning and deep learning models, the framework processes large-scale movement data and produces highly accurate predictions. The findings demonstrate that Big Data-driven analysis significantly enhances crowd-flow forecasting accuracy, improves safety management, supports effective traffic control, and strengthens urban planning strategies for smart cities.
DOI:
