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AI IN TRANSPORT

                  In transport, AI is used to make travel more efficient, safe and comfortable. It helps manage
                  traffic, reduce accidents, improve navigation and even supports the development of self-driving
                  vehicles. With the help of AI, transport systems can analyse large amounts of data quickly and
                  make smart decisions in real time.

























                  The Role of AI in Transport Sector

                  AI helps traffic control systems keep busy roads safe. Sensors and cameras track the number of
                  cars and AI uses this data to control traffic lights, reduce congestion and prevent sudden braking.
                  AI also helps navigation apps find the quickest route. If there's traffic or an accident, the app

                  quickly suggests a new path, saving time and fuel. Self-driving cars use AI to detect obstacles,
                  read traffic signs and assist drivers, making travel safer and more efficient.

                  Improving Safety and Efficiency

                  AI technology helps prevent accidents by reducing human error. For example, cars can alert
                  drivers and slow down if they are distracted. Public transport systems also use AI to keep
                  buses and trains on time. AI helps plan routes and track delays, making travel smoother.
                  This technology also reduces traffic on crowded roads, allowing people to travel peacefully to
                  their destinations.

                  Case Study: Smarter Traffic During Monsoon


                  During the monsoon season, Mumbai often faces heavy traffic congestion due to rains. An AI-
                  powered traffic system was introduced to tackle this issue by analysing both weather information
                  and traffic patterns. Drivers, using navigation devices, were able to receive real-time suggestions
                  for alternate routes before the rain began. AI also anticipated the upcoming traffic congestion by
                  adjusting traffic patterns in advance, using historical and real-time data.

                  This proactive approach helped significantly improve the flow of traffic during heavy rains, reducing
                  delays and making travel more efficient. The system optimised the entire traffic management
                  process, resulting in smoother commutes despite challenging weather conditions.




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