Sixty-five per cent of India's population already falls in the working-age bracket, which means that a considerably large group of commuters from across the country travel long distances using personal vehicles and public transport. In doing so, they often battle with ill-maintained roads and insurmountably heavy traffic. A more specific viewpoint, focusing on commuters using company-sponsored cab services, reveals that these professionals spend hours traveling to and from work, with ill-planned routes and delayed pick-ups hampering their health and workplace productivity.
Therefore, several path-breaking companies are leveraging the potential of modern-day technologies to bridge the existing gaps in India's transport sector.
This is where the role of Internet of Things (IoT) and predictive analytics in ride-sharing comes in. The objective of every ride-sharing start-up is to find a solution to optimize travel. They use IoT and Artificial Intelligence (AI) to identify demand and supply patterns for transportation within the city, based on historical data. The final objective of commute-focused start-ups is to understand how the traffic from commercial hubs interacts with the rest of the city and identify a way to alleviate the hassle of ride-sharing within that context.
The Indian transportation industry can leverage predictive analysis and data mining to draw insights and patterns from the vast pool of big data pertaining to transport and traffic conditions in particular areas. Using these patterns, systems powered by AI can plot the fastest routes for commuters, factoring in multiple pick-ups and drops on the way. Such systems rely upon AI to create routes for corporate commutes and club the employees who plan to commute at similar times, thus ensuring that they take the shortest route possible.
Predictive analysis tools can also alert drivers and passengers about impending bottlenecks and congestion. Such systems will reduce travel time for employees, while helping corporates save the large sums of money they spend on organising company-sponsored commutes.
Technological intervention can also bring about safety and security as far as public transport and employee commute services are concerned.
Corporates with 24-hour rotational shifts are obliged to provide cab services to their employees to ensure their safety. Connected services such as transport automation systems, driven by AI and IoT, can ensure that employees travel safely by collecting and storing relevant data about drivers, including their background verification information and prior criminal records. Additionally, through robust mobile applications, passengers can provide feedback pertaining to their experience with their drivers at the end of each trip. Advanced systems that use mobile phone sensors can also identify instances of rash driving.
Beyond the workforce commute, predictive analysis can be applied to a holistic urban mobility scenario. It can improve public transport by forecasting weather conditions, determining arrival times of buses, and predicting the number of drivers travelling each day.
Further, advanced analytics will be able to provide data regarding the impact of road maintenance, signal failures, accidents, and vehicle breakdowns on the overall traffic conditions, in addition to circumnavigating mobility bottlenecks by mapping the shortest routes in real-time. This will help people in reaching their destinations as quickly as possible.
The future of transportation in India, if it continues along the technologically-enlightened path that it has embarked upon, could be smoother and less chaotic in the near future. On the back of rapid technological advancement, the transport sector has the potential to transform itself into a well-oiled machine. Commuters and travelers are advised to just sit back, fasten their seat belts, and enjoy the ride.
(This story has not been edited by Business Standard staff and is auto-generated from a syndicated feed.)