"Traffic Congestion Management Dataset"

"Traffic congestion has become a critical challenge in urban transportation systems world wide, particularly in India, where it has reached alarming levels in major cities like Kolkata, Mumbai, Delhi, and Bengaluru etc. According to the World Economic Forum, traffic conges tion in these cities costs the Indian economy approximately 22 billion dollar annually, with residents spending over 200 hours each year stuck in traffic. This leads to significant produc tivity losses, reduced quality of life, and worsens environmental issues, as increased vehicle emissions contribute to India\u2019s growing pollution crisis. The Ministry of Road Transport and Highways (MoRTH) reports that in 2021, India experienced over 150,000 road accidents , with congestion-related factors playing a key role in these incidents. Traffic fatalities in congested areas are disproportionately high, accounting for nearly 25% of all accidents, with congestion-related crashes often leading to more severe outcomes due to limited vehicle maneuverability and heightened driver stress. The outdated or insufficient infrastructure in rapidly growing cities further exacerbates the problem, making it a major public safety con cern. As the urban population continues to grow and vehicle ownership increases, the need for effective traffic management systems becomes even more urgent to alleviate congestion, prevent accidents, reduce pollution, and improve overall urban mobility. To overcome a va riety of approaches have been explored to address this issue, particularly leveraging artificial intelligence (AI) and machine learning (ML) technologies."