AI-driven planning is Gati Shakti's new frontier, says DPIIT official

Over the past year and a half, the GatiShakti National Master Plan has developed into a comprehensive database of ongoing as well as future projects for various central and state-level economic and social infrastructure initiatives. Moving forward, GatiShakti will also incorporate predictive infrastructure planning through the use of artificial intelligence (AI), SUMITA DAWRA, special secretary (logistics), Department for Promotion of Industry and Internal Trade, shares in an interview with Shreya Nandi in New Delhi. Edited excerpts:

What has been the most significant achievement since the launch of the GatiShakti National Master Plan?
 

It’s challenging to pinpoint a single achievement as it has been an evolving process. One key accomplishment is the convergence of various Government of India departments and ministries, each with varying levels of engagement.

We have onboarded 39 ministries/departments regarding their geographic information system data. While some have been fully mapped, others have made commendable progress, and some have just begun. Infrastructure ministries have a strong footing, while social sector ministries are in different stages of onboarding.

Another aspect is the quality and authenticity of the data. Certain ministries have established themselves well and enhanced their data up to the field level over the past few months.

Furthermore, the network planning group has held 52 meetings over the past year and a half, marking consistent achievement. However, it’s important to appreciate the coordination, data mapping efforts of the technical partner, quality assurance protocols established by each ministry, and the usage.

The biggest achievement today is the diverse and innovative approach and scale. Over the past 18 months, the platform’s usage cases have showcased impressive variety and innovation, emphasising its extensive scope.

For instance, the Railways initially focused on optimum project alignment, then progressed to linking economic nodes with manufacturing centres, connecting hinterlands to ports, and exploring multimodal and social sector connectivity. The approach has evolved to encompass a comprehensive perspective. Whether it’s a railway freight line, an expressway, or a gas pipeline, we now analyse the economic, social, and connectivity aspects.

What is the road map for GatiShakti? What are the intended achievements?
 

In the next two to five years, the GatiShakti approach is poised to deliver greater benefits. Primarily a planning tool, many of the ongoing plans will transition into implementation within two years, yielding early advantages, while others will manifest benefits over a longer period.

Given the extensive evaluation and recommendations for the GatiShakti approach, we anticipate improved multimodal network connectivity, efficient movement to exempt gateways, and a fillip to cross-border trade.

For example, the Indo-Nepal-Haldia expressway will significantly reduce travel time from 20 to seven hours, invigorating cross-border traffic.

Moreover, we anticipate enhanced planning for schools and substantial improvements in business connectivity across states. This will encourage and support supply chains, thereby reducing logistics costs — a critical component of the overall cost of goods.

We will see a lot of improvement there because there’s real work happening. As states use this innovative approach, a lot will depend on state-level adoption of this innovative approach.

Gujarat, Uttar Pradesh, Maharashtra, and Assam have set encouraging examples in implementing GatiShakti. These states are likely to experience a positive surge in ease of living.

There’s a lot which probably is beyond my prediction, but we are also working on AI coming together with PM GatiShakti because we have a huge amount of data. We are trying to harness this data into useful information for a better life.

How do you plan to leverage AI in GatiShakti?
 

Our objective is predictive planning. Presently, we’re engaged in planning, but our vision is to project origin-destination flows for both freight and passenger traffic.

For example, there will be freight flows, which will grow today and we also have the projections. These projections will be plotted to get predictive guidance on where the infrastructure should come up.

This data-driven approach will enable more informed decisions on capital expenditure based on specific area growth patterns.

This year, our focus shifts towards predictive infrastructure planning. We intend to leverage AI to achieve this goal.

What has been the biggest challenge in coordinating and implementing GatiShakti with the Centre and states?
 

Successful case demonstrations have been instrumental in encouraging deeper adoption of the GatiShakti approach among infrastructure ministries. This approach was subsequently extended to the state level, with successful cases showcased to foster motivation and enhance state-level efforts.

Having done it within infrastructure ministries and at the state level, we are now extending this process to the social sector, allowing for more effective problem-solving.

We are also working on generating solutions for various ministries, leveraging the extensive utility benefits of this comprehensive and varied platform.

What is the plan for opening GatiShakti to the private sector?
 

Several factors need careful consideration. There’s a huge amount of data. Some ministries would like to restrict some of the layers of data.

Protocols for data use and privacy must be established in collaboration with the Ministry of Electronics and Information Technology and the Department of Science & Technology, alongside ministries like Home Affairs.

Comprehensive examination and collaboration are crucial before moving forward. A systematic approach is being developed to involve the private sector in GatiShakti.