By Anoop G Prabhu, Co-Founder & CTO- Vehant Technologies
The impact of COVID-19 has been seriously felt in the transportation sector. The pandemic has changed the way in which people are commuting now. Undoubtedly, with people’s safety in mind, the public transport operators are revamping their operational procedures to prioritize commuter safety and regain their trust. Safety measures such as sanitization, proper implementation of social distancing are a must now. With the resumption of services, the transport system may also witness a sudden peak in demand. Similarly, professional and occupational travel will also see a spurt. The transportation system will have to meet this demand while adhering to the guidelines.
Applications of Artificial Intelligence (AI), Internet of Things (IoT) and Intelligent Transportation System (ITS) can tackle many such problems of the transportation system. It can help to reduce human-to-human transmission and prevent cluster outbreaks in public transportation systems by monitoring adherence to COVID-specific norms, while improving efficiency and ease of monitoring.
Few possible use-case scenarios of AI in transportation is given below. Some of the listed measures can be easily rolled out in a modular fashion, and can be targeted for quick deployments. Others may need across the board implementation with longer deployment cycles: for these, the key will be to look at long term benefits, with an eye on the post-pandemic future.
1) Public transport with social distancing using AI solutions
Camera based AI modules can monitor transport infrastructure to detect adherence of wearing Masks and Social Distancing norms. These AI systems powered by sophisticated neural networks can also detect instances of crowd formation and generate alerts. Innovative solutions like thermal temperature screening, face mask detection of passengers before boarding a ride would also help instill confidence among commuters to ensure safety.
2) Dynamic Route management and planning
In the changed scenario, many commuters now prefer private transport over public transport, thereby leading to an increase in the traffic on the roads. Additionally, staggered shifts and work-from-home policies followed by many offices also acts to change the traffic and commute patterns established previously. All of the above necessitates changes in Route planning and revised schedules for public transport. AI working with the camera/IoT sensor data can sense the current transit demand, and also make predictions on the demand trend. It can also take over significant burden from the human operators of the Route planning and scheduling changes necessary, suggesting dynamic schedules adjusted dynamically on a weekly, or even daily frequency.
3) Area Traffic Control Systems
Traffic control systems in most cities are outdated, and many are horribly inefficient leading to traffic jams, congestion and longer transit times. The new class of traffic management systems powered by AI can provide significant improvements in all the above aspects and reducing overall end-to-end travel time. Additionally, optimizing for public transit efficiency that reduces travel time leads to a virtuous cycle that increases the number of trips per day for buses, resulting in lesser crowding and lessens overall commuter exposure time to large scale infection vectors.
4) Efficient Resource Monitoring and Deployment
Whereas the pandemic has accelerated digital transformations including touchless payments and information serving chatbots, there are still many scenarios where physical user services are needed. AI based queue detection and counter management systems, including projected wait time can help reduce crowding apart from improved customer experience. Additionally, in manpower constrained scenarios like sustained lock-downs, AI can help in effective manpower deployment and smart emergency management of grid systems, thereby reducing burden on both operators and users alike.
Like almost all other industries, the pandemic has provided an impetus to rethink and calibrate the transportation systems and implement technologies which can be used as effective buffers against future pandemics and human disasters. It is clear that people-to-people connectivity and transportation systems should be more robust and technologically adaptable in the future.
AI is not a silver bullet for all problems; but when accompanied with complementary changes in infrastructure and process changes, it can be transformational for many industries, including transportation.
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