By P Daksh Shanthram
Managing urban traffic congestion has been a daunting task, a massive issue for emergency vehicles like ambulances, fire trucks, and police vehicles that need to make their way through busy streets. The inability to create a clear path quickly is not just an inconvenience, it can be a matter of life and death. Although cities have improved their infrastructure and raised public awareness, traditional traffic management systems often remain static, struggling to adapt quickly to changing conditions. This is where advanced technology, particularly artificial intelligence (AI) and real-time data integration, can transform traffic management.
Conventional traffic lights follow fixed patterns designed to optimise flow under normal conditions. However, emergencies are anything but normal, necessitating immediate and flexible adjustments that static systems cannot offer. For example, an ambulance heading to a hospital might have to stop at multiple intersections just because the system cannot recognise its urgency. This issue is even more pronounced in cities like Delhi, Mumbai, or Bengaluru, where it seems to be faster to carry the patient to the hospital running than to rely on emergency services.
By incorporating AI into traffic systems, a new system can be developed, one where signals are adaptive rather than static. By equipping emergency vehicles with GPS technology, these systems can send real-time location and route information to a central traffic management centre. With this data, AI algorithms can assess the surrounding traffic conditions and prioritise a clear route for the emergency vehicle. This involves adjusting signal timings not just at one intersection but along an entire corridor, creating a coordinated flow that minimises delays.
Take Singapore as an example. The city-state’s smart traffic management system employs AI to monitor real-time conditions, optimising signal timings to give priority to ambulances and fire trucks. In India, a similar system adapted to local conditions could yield transformative results. However, implementing such a solution requires more than just technology, it necessitates a complete overhaul of current infrastructure, collaboration among government entities, and public compliance.
Beyond the immediate need to prioritise emergency vehicles, these systems can also lay the groundwork for smarter cities. By integrating AI-driven traffic management with public transport systems, ride-sharing platforms, and pedestrian safety measures, we can create a systematic approach to urban mobility. In such a city, traffic lights would synchronise with transport schedules, reducing congestion and enhancing efficiency for all commuters.
Despite the potential of these technologies, challenges persist. Retrofitting existing traffic systems with AI and IoT capabilities demands substantial investment, both financially and logistically. Policy changes are also essential. Governments must implement regulations that facilitate data sharing between emergency services and traffic management systems while safeguarding the privacy and security of this information. Additionally, public behaviour must adapt. Drivers need to follow emergency lane protocols and other guidelines designed to ensure smooth traffic flow during critical situations.
While these challenges are significant, the advantages greatly surpass the drawbacks. The adoption of intelligent traffic systems signifies not only a technological leap but also a societal change in emphasising what truly matters: human life. Every moment gained in clearing a route for emergency vehicles can mean lives saved and families kept together.
The tools to tackle this problem are already easily accessible. AI, real-time data, and IoT technologies are no longer just concepts of the future, they are practical tools ready for use. The steps ahead are collaborative, requiring the efforts of governments, technologists, and citizens to bring these systems into action. By tapping into the potential of these innovations, we can reshape our cities into environments where traffic does not obstruct emergencies but aids in their resolution.
(The writer is a class 11 student of Kendriya Vidyalaya NAL campus)