REQUESTING YOUR FEEDBACK

 Dear Professor Hiroyasu Ando :

 

 

 

A few days ago I came across following news report about your research :

 

https://techxplore.com/news/2026-01-city-traffic-approach-ai-slash.html

 

That reminded me about following  traffic pollution related proposal that I submitted to Indian Policy Makers, sometime back :

 

https://myblogepage.blogspot.com/2025/06/project-rote-reduction-of-transport.html

 

In this context , I invite your comments on the following chat with ChatGPT

 

 

 

With regards ,

 

Hemen Parekh

 

Mumbai – India

 

www.HemenParekh.ai  /  www.IndiaAGI.ai  /  www.My-Teacher.in  /  www.3pConsultants.co.in

 

 

I  asked :

 

 

Pl compare my blog

 

> https://myblogepage.blogspot.com/2025/06/project-rote-reduction-of-transport.html

 

with this news report

 

> https://techxplore.com/news/2026-01-city-traffic-approach-ai-slash.html

 

 

Is there any SYNERGY between the two ? Can they leverage each other ?

 

 

ChatGPT  replied :

I’ve read and compared your blog “Project ROTE = Reduction of Transport Emission” (focused on simulation and behavioural-

policy solutions to cut transport emissions in cities) with the Tech Xplore news report “Turning city traffic into a computer: Novel

approach to AI could slash energy demands” (about a new AI framework that treats traffic dynamics themselves as a computational

resource).

 

Here’s a clear analysis of synergy and whether they can leverage each other:

---

 1. Core Focus — What Each One Is About

 Your Blog – Project ROTE

• Proposes a simulation framework for reducing transport emissions (targeting AQI <100 in Chennai).

 

• Uses NaVIC satellite-enabled tracking of ~1,000 vehicles, with metrics like

 

•  
• Harm Quotient (pollution footprint),
•  
• Trans-Score (behavioural eco index),
•  
• Trans-Tax (dynamic incentives/debits).
•  
• Aims to influence driver behaviour and policy to reduce emissions and congestion via data-driven modelling and feedback loops.

Essence: data + behavioural incentives → lower emissions via simulation then real-world policy.

---

 Tech Xplore News – Harvested Reservoir Computing

• Describes a novel AI framework (Harvested Reservoir Computing) that uses traffic patterns themselves as a computational
•  
• system.
•  
• Instead of running AI on separate hardware, it leverages physical traffic flow dynamics to do computation useful for prediction and adaptive control.
•  
• Can enable accurate traffic forecasting and energy-efficient AI systems.

Essence: the physical system of traffic as a compute resource for AI → smarter predictions with less energy.

---

 2. Where Synergy Exists

 A. Shared Goal: More Efficient Urban Transport

• Both aim to make urban transport more efficient and smarter.
•  
• Your model targets emission reduction and eco-behaviour change.
•  
• The AI news targets optimizing traffic prediction and control with minimal energy.

Overlap: Both treat traffic not just as a mechanical flow but as data and dynamics that can be optimized.

---

 B. Real-Time Data & Predictive Capabilities

• Your ROTE simulation depends on NaVIC tracking and real-time behavioural analytics.
•  
• The traffic-as-computer concept uses real-world traffic dynamics and sensors for prediction.

Synergy: The AI framework could provide predictive insights about traffic states—useful for dynamic Trans-Tax and Trans-Score adjustments in ROTE.

---

 C. Smart City & Urban Sustainability Context

• Both fit into the smart city/urban computing paradigm, where data and AI are used to make cities cleaner and more liveable.

This is critical: emissions reduction requires both behavioural strategies AND adaptive traffic intelligence.

---

 3. How They Can Leverage Each Other

 A. Integrating AI Traffic Prediction into ROTE

The AI traffic computing approach (Harvested Reservoir Computing) can feed real-time traffic state predictions into your ROTE

simulation and policy system.

Example:

• Predict near-congestion states where emissions spike.
•  
• Automatically adjust Trans-Tax incentives before congestion forms.

Value: More proactive emission control and smarter dynamic incentives.

---

 B. Enhanced Simulation Realism

The AI model’s ability to forecast traffic dynamics without heavy hardware could be embedded inside ROTE’s simulation engine,

improving accuracy and reducing computational cost.

Benefit: Higher-fidelity simulations that mirror actual city behaviour.

---

️ C. Feedback Loop Between Behaviour and AI

Your ROTE behavioural eco-index (TRANS-SCORE) could be refined using insights from the AI model:

• AI forecasts identify where behaviour change would be most impactful.
•  
• Policies can be prioritized by predicted congestion/emission hotspots.

Result: Better targeting of incentives and infrastructure planning.

---

 4. Limitations or Mismatches

 Different Focus Levels

• ROTE is applied policy + real-world simulation.
•  
• The AI news is theoretical/building block for new AI systems.

These aren’t competing, but not directly interchangeable without engineering integration.

 Implementation Gap

• The AI traffic system idea isn’t yet a ready-to-deploy urban solution; it’s an AI research concept that proves a principle rather
•  
• than a full urban traffic system.

---

 5. Strategic Action Suggestions

 A. Embed Predictive AI into ROTE’s Framework

Partner with urban research labs / traffic AI startups to integrate a reservoir computing-based predictor into your simulation stack.

 This could improve anticipatory emission control.

---

 B. Align ROTE Metrics with Smart Traffic Systems

Expand the ROTE simulation to incorporate smart signal and adaptive control models common in urban traffic AI research. This

tightens the link between traffic flow and emissions.

---

 C. Position as Smart City Emission-AI Pilot

Present a combined program to civic agencies: “AI-assisted emission reduction through behavioural incentives + adaptive traffic

prediction.”

This positions your project as a smart city sustainability initiative rather than just a simulation study.

---

 Summary: High-Level Synergy

Aspect	Your Project (ROTE)	City Traffic AI
Core Goal	Reduce emissions via behavioural + policy models	Efficient AI & traffic forecasting via dynamic system modeling
Data Use	Real-time vehicle tracking & eco scoring	Traffic flow dynamics used as computation
Value	Policy + behaviour impact	Prediction + adaptive control
Synergy	Predictive insights to improve emission reduction strategies	Ground to test AI models in real urban simulations

 

Conclusion:

 

Yes — there is real synergy.

 

The AI research offers powerful predictive and adaptive tools that can enhance ROTE’s simulation and policy framework.

With thoughtful integration, your project could leverage cutting-edge AI concepts to make emissions reduction strategies smarter and

more effective.