FluxAI

Predictive Highway & Enforcement Intelligence

Prepared for

Ador Traffic & Enforcement

From reactive maintenance & static speed limits to a predictive, self-learning highway brain

About Trinesis

Gen AI-driven process automation & software engineering for manufacturing, industrial & utility leaders

2018

Established · 6+ yrs trusted

100+

Engineers: ML, domain & tech

99%+

Client retention

200%

Annual growth

Global Footprint

HQ & Innovation Hub
Pune, India
Sales Offices
Germany & Austin, TX, USA
Around-the-Clock Support
24×7 global availability - your network never stops

Recognition & Partnerships

NASSCOM Member Clutch - "We Deliver" ISO 27001 ISO 9001:2015 AWS Partner Google Partner Microsoft Solutions Partner Odoo Ready Partner

Process-automation specialists in ML, data analytics, agentic & generative AI - streamlining operations across industrial & utility customers.

Our Expertise

The same engineering depth that builds FluxAI - from Gen AI to ERP

The AI core that powers FluxAI

GenAI & Agentic AI

Autonomous agents and LLM applications that decide and act - not just answer.

Machine Learning & Analytics

Forecasting, predictive & anomaly-detection models that turn telemetry into foresight.

Computer Vision

Image & video models for ANPR, road-defect and roadside-asset inspection at scale.

Backed by full-stack delivery & assurance

Custom Software

Cloud & On-Prem

Web Apps

Mobile Apps

Quality Assurance

ERP & Integration

Our Solution

Meet FluxAI - an autonomous intelligence layer for traffic, enforcement & field operations

FluxAI

FluxAI connects your NMS, ATMS controllers, ANPR & radar, VMS, field-service, ERP and traffic/weather feeds to answer three operations-critical questions:

01

What is likely to occur?

Which asset failure, network fault or traffic/safety risk is likely to occur across your corridors.

02

When will it happen?

When it will hit uptime, enforcement coverage, SLAs and road safety - days to weeks ahead.

03

What action to take?

Which engineer & spare to dispatch, which speed to set, which junction to re-engineer - proactively.

Sits on top of your existing ATMS & software stack - no rip-and-replace, on-premises, your data stays yours.

How FluxAI Works

Sits on top of your stack - then discovers, decides, builds & deploys, autonomously

Your Existing Infrastructure - untouched, on-premises

NMS
Asset telemetry

ATMS
Controllers

ANPR + Radar
Enforcement

VMS
Signs & speed

Field Service
Tickets & spares

Weather + Traffic
Live feeds

ERP
SAP / Tally

read-only taps · zero disruption to your ATMS & operations

FluxAI Autonomous Intelligence Engine

Connect & Unify

Taps every distributed source and fuses them into one operational truth.

1 unified data fabric

Discover Patterns

Mines history for failure signatures, congestion & weather patterns.

Decision Intelligence

Autonomously designs the ML pipeline - no data scientist required.

planning approach…

AutoML: Build & Train

Generates, tests & trains candidates - picks the best model.

ARIMA Prophet XGBoost LSTM ✓ 93% CNN-Vision

Deploy & Serve

Pushes trained models live to power every use case below.

models in production

Continuously re-learns as new telemetry arrives - the loop never stops

Predictive Asset Health

Field-Service Optimization

Dynamic Speed Intelligence

Enforcement Analytics

Where We Left Off

Quick recap of our first conversation, Rahul

What You Told Us

Your ATMS software is already in place
A strong in-house software team, own ANPR engines, and Acusensus-derived "Ador Harmony" enforcement on the Mumbai-Pune Expressway since 2018-19. The platform isn't the gap.
You want dynamic, condition-aware speeds
Predict congestion windows & vehicle mix, set speeds by time-of-day, and warn "rain ahead in the ghats - slow down" - to take to NHAI.
Predictive analytics on your NMS-connected assets
You see what's up/down today - but want to predict chronic-fault junctions, network vs power vs hardware causes, before they fail.
"Narrow it down to one or two things"
Show a POC on asset management & predictive analytics - then bring your software team in to take it forward.

What We Aligned On

Two focused POCs - exactly what you named
(1) Predictive asset health on the hardware/NMS side, and (2) dynamic speed & road-condition prediction.
We add the ML layer - not replace your team
FluxAI complements your existing ATMS suite. Your team keeps owning the platform; we bring predictive intelligence.
On-premises, on your data, under your control
Read-only taps on NMS, ATMS & enforcement data - nothing leaves your environment.
Prove on one corridor → scale across the network
Start on the Mumbai-Pune Expressway, then roll out to other corridors & NHAI projects - with your team alongside.

Our Vision for Ador

One adaptive operating brain across assets, enforcement & traffic flow

Predictive

Failures, not surprises

Real-Time

Health across every asset

Dynamic

Speed & weather aware

Ask Anything

Plain-language answers

01

Predictive Asset Health & Maintenance

Learns the failure signatures behind every NMS alarm to predict which cameras, radars, VMS & controllers will fail - and flags chronic network/power/hardware junctions before they take coverage down. POC #1.

02

Dynamic Speed & Road-Condition Intelligence

Forecasts congestion windows and vehicle mix, recommends time-of-day variable speed limits, and pushes weather advisories ("rain ahead, slow down") to your VMS - ready for NHAI. POC #2.

03

AI Field-Service & Spares Optimization

Turns predictions into action: which engineer and which spare to dispatch to which junction, before a citizen-facing outage - cutting truck-rolls and mean-time-to-repair.

04

Enforcement & Vehicle-Pattern Analytics

Repeat-offender & vulnerable-vehicle detection from ANPR history, plus dashcam-based road-defect anomaly scoring - extending the value of data you already capture.

Use Case 1: Predictive Asset Health & Maintenance

Your hardware / NMS ask - predict failures & chronic-fault junctions before they take coverage down

FluxAI Asset Cockpit - Mumbai-Pune Expressway Corridor

Synced with NMS

23

Assets at risk (30d)

96.1→99.3%

Network uptime (target)

26h→7h

Mean-time-to-repair

−35%

Field-service cost

Predicted Failures & Root Cause - live from NMS telemetry

AI recommending dispatch

Asset · Location	Health	Predicted Failure	Likely Cause	Status	FluxAI Action

Use Case 2: Dynamic Speed & Road-Condition Intelligence

Your road-conditions ask - congestion & weather-aware variable speed limits for NHAI

FluxAI Speed Studio - Mumbai-Pune Expressway, Ghat Section (Km 60-72)

Live

Predicted Traffic vs Recommended Speed (24h)

Re-forecast every 15 min

Multi-axle peak 02:00-05:00 4-wheeler peaks 07-09 & 17-19

Recommended VSL schedule

07:00-09:00 · AM peak60

10:00-16:00 · off-peak100 / 80

17:00-19:00 · PM peak60

23:00-05:00 · freight80

Weather override - rain in the ghats

Rain + reduced visibility detected Km 60-72. FluxAI drops advisory to 60 km/h and auto-pushes "RAIN AHEAD · SLOW DOWN" to upstream VMS gantries.

Use Case 3: AI Operations Assistant

Ask in plain English. Get instant, sourced answers across assets, traffic & enforcement.

FluxAI Assistant - Connected to NMS, ATMS, Weather & ANPR

Live

Which roadside assets are likely to fail in the next 30 days?

23 of 64 assets are at elevated failure risk (6 critical). Top 3 by urgency:

Speed Radar · Km 63 Lonavala - down now, hardware fault
ANPR Camera · Km 42 Khalapur - fail ~12 Jul, power fluctuation
ESP Power Supply · Km 50 - fail ~18 Jul, capacitor aging

Recommended: dispatch engineer EN-04 to Km 63 today with a spare radar unit; pre-stage a UPS for Km 42 on the same run.

NMS

Power logs

Maintenance

It's raining in the ghats and it's 6 PM - what speed should we set?

Recommend 60 km/h on Km 60-72 (ghat section). Reasoning:

18:00 is a PM-peak window - predicted density is already high
Rain + low visibility detected on the descent - stopping distance up ~40%
Multi-axle share rising - downhill braking risk elevated

Drafted VMS message: "RAIN AHEAD · SLOW TO 60" - ready to push to gantries at Km 55 & Km 58.

Weather feed

Traffic counts

VMS

Which junctions have chronic network issues?

Show repeat speed violators this month

Multi-axle peak hours on the expressway

Executive Dashboard

360° visibility for leadership - live, across corridors

99.3%

Network Uptime

23

Assets at Risk (30d)

−28%

Projected Crash Risk

−35%

Field-Service Cost

Predicted Traffic vs Capacity (24h)

Asset Health by Type

Open the live interactive dashboard

Drive it live - Asset Health, Dynamic Speed & the AI Assistant. Ships on-prem, wired to your NMS & ATMS feeds.

The POC Behind the Demo

Not slideware - a real, reproducible ML pipeline run on data that mirrors your NMS & ATMS feeds

1 · Synthetic Data

NMS telemetry + corridor traffic, schema-matched to Ador exports

2 · Feature Eng.

Rolling means & slopes, lags, cyclical & weather features

3 · AutoML Leaderboard

Baseline + candidates, time-split evaluation

4 · Best Model

Promoted on held-out future data - no leakage

5 · Results & Serve

Metrics, figures & the live cockpit you just saw

What we actually ran

POC #1 - Predictive Asset Health
64 assets × 180 days = 11,520 telemetry rows · failure classification + RUL regression
POC #2 - Dynamic Speed
120 days hourly traffic · short-term forecast + congestion + VSL policy
Deterministic & reproducible
Fixed seed · one command (python run_all.py) regenerates every number in this deck

Tech stack

Python 3.13 pandas NumPy scikit-learn statsmodels Matplotlib

In the full pilot the leaderboard also screens XGBoost, Prophet, ARIMA & LSTM (GPU/OpenMP deps), with a model registry, monitoring and on-prem serving.

Runs entirely on-prem · your data never leaves your walls

Synthetic Data, Built to Match Your Feeds

Schema- and behaviour-matched to an Ador NMS / ATMS export - so the pilot swaps real data in 1:1

Asset NMS telemetry

11,520 rows

64 assets (ANPR, Radar, VMS, Controller, ESP Power) × 180 daily records, from a latent health curve with root-cause signatures + sensor noise.

temperature_cvoltage_stabilitypacket_loss_pctsignal_strength_dbmerror_count_24hreboot_count_7duptime_pctcalibration_driftage_monthsdays_since_maint

Labels: will_fail_30d (5.3% positive) · rul_days (remaining useful life)

Corridor traffic

2,880 rows

120 days of hourly volume on the dashboard's 24-hour profile, with weekday/weekend seasonality, holidays, weather & vehicle mix.

volume_vphhourdayofweekis_weekendis_holidayweathervisibility_kmpct_multi_axlecapacity_vph

The 12 assets & the 24h profile on the live cockpit are drawn from exactly this dataset.

Generator - latent health → noisy sensor readings src/generate_synthetic_data.py

# health (0-100) declines per asset; sensors degrade as a function of it
def telemetry_from_health(h, cause, age, quality):
    deg = (100 - h)                      # degradation magnitude
    c_net = 1.0 if cause == "Network" else 0.0
    return pd.DataFrame({
        "temperature_c":       35 + deg*0.22 + rng.normal(0,1.6,n),
        "packet_loss_pct":     0.2 + deg*0.05 + c_net*deg*0.12 + rng.normal(0,0.6,n),
        "uptime_pct":  np.clip(100 - deg*0.09 - rng.gamma(1,0.4,n), 70, 100),
        "error_count_24h": rng.poisson(0.5 + deg*0.12), ... })   # predictive, but noisy

POC #1 Results: Predictive Asset Health

Failure classification + remaining-useful-life regression · time-split evaluation, no future leakage

Will-fail-in-30-days - classification

Model	ROC-AUC	PR-AUC	Recall	F1
Gradient Boosting (HistGBM) ✓	0.976	0.623	0.590	0.588
Random Forest	0.974	0.567	0.876	0.665
Logistic Regression (baseline)	0.728	0.103	0.783	0.253

Remaining useful life - regression

Model	MAE (days)	RMSE	R²
Gradient Boosting (HistGBM) ✓	5.73	14.73	0.923
Neural Net (MLP)	6.60	14.73	0.923
Random Forest	6.26	15.37	0.917
Ridge (baseline)	13.12	20.81	0.847

Best model predicts failure ~0.98 ROC-AUC and pinpoints the failure date to ±5.7 days. Top features are 7-day trends in voltage, uptime & temperature - trajectories, not snapshots.

POC #2 Results: Traffic Forecast & Dynamic Speed

Next-hour volume forecast → congestion windows → weather-aware variable speed limits

Short-term forecast - leaderboard

Model	MAPE	RMSE	R²
Random Forest ✓	7.36%	275	0.939
Neural Net (MLP)	7.74%	283	0.935
Gradient Boosting (HistGBM)	7.82%	289	0.933
Ridge	8.07%	298	0.929
Seasonal-naive (baseline)	10.05%	367	0.892

7.4% MAPE sits squarely in the 5-10% band reported for state-of-the-art short-term highway forecasting - and beats the naive baseline by 27%. Congestion-window detection F1 = 0.91.

The forecast feeds a deterministic VSL policy (density × weather × ghat). Replacing one static limit with a dynamic one cuts speed-limit variance - the mechanism behind the ~20-35% crash reduction reported by mature VSL corridors.

Code, Repository & Reproducibility

A clean, hand-over-ready repo - your software team can read, run and extend it

Repository structure

poc/
├── README.md            # methodology + results
├── requirements.txt
├── run_all.py           # one command, end-to-end
├── data/raw/
│   ├── asset_master.csv     # 64 assets
│   ├── asset_telemetry.csv  # 11,520 rows
│   └── traffic_hourly.csv   # 2,880 rows
├── src/
│   ├── generate_synthetic_data.py
│   ├── asset_health/train.py     # POC #1
│   └── dynamic_speed/train.py    # POC #2
└── reports/
    ├── metrics/         # leaderboards .json/.csv
    └── figures/         # the charts in this deck

python run_all.py seed = 42 · fully reproducible

Model leaderboard & time-split src/asset_health/train.py

# strict time-based split - never score on the future of training
tr = df["day"].values < SPLIT_DAY        # days 0-139
te = ~tr                                # days 140-179

candidates = {
  "Logistic Regression": LogisticRegression(class_weight="balanced"),
  "Random Forest":       RandomForestClassifier(n_estimators=300),
  "Gradient Boosting":   HistGradientBoostingClassifier(max_iter=400),
}
for name, model in candidates.items():
    model.fit(X[tr], y[tr])
    p = model.predict_proba(X[te])[:, 1]
    board.append(roc_auc_score(y[te], p))   # promote best → 0.976

From POC to pilot: swap these CSVs for live NMS/ATMS/ANPR/weather feeds - the same features, split & leaderboard apply. Add XGBoost/Prophet/LSTM, a model registry, monitoring & on-prem serving.

From Synthetic to Live: Data, Weather & Scope

What's built today, how it connects to live feeds, and what comes next

Weather: consumed, not forecast

We don't predict weather - that's a solved commodity. FluxAI consumes a weather feed and fuses it with the traffic forecast into the speed decision and VMS message. Three time-horizons feed the model:

Now / ground truth
Roadside RWIS sensors + API 'current' + IMD radar nowcast → the immediate "RAIN AHEAD" VMS advisory
Short-term forecast (1-6 h)
Weather API - IMD / OpenWeatherMap / Tomorrow.io / Visual Crossing → proactive speed planning before rain hits
Historical archive
Used to train the models - what this POC ran on. Going live swaps in the API + sensors with no model change.

POC scope: built vs roadmap

Built & running today

Predictive Asset Health
Tabular ML on NMS telemetry - failure classification + remaining-useful-life
Traffic Forecast + Dynamic Speed
Tabular time-series forecast + weather-aware variable-speed-limit policy

Roadmap - next module

Road-Condition Computer Vision
Dashcam defect detection - cracks, potholes, faded markings, missing signs, encroachment - via CNN/YOLO, aligned to NHAI Rajmarg Saathi / Data Lake. Not in this POC.
Enforcement & ANPR analytics
Repeat-offender / vulnerable-vehicle detection from ANPR history

Why Now

NHAI tenders & global ITS leaders are moving from reactive ops to predictive AI

Reactive Today vs FluxAI

Capability	Today	With FluxAI
Maintenance	Fix after failure	Predict weeks ahead
Field dispatch	Reactive truck-rolls	Right engineer + part
Speed limits	Static signs	Dynamic, weather-aware
Network faults	Investigated ad-hoc	Chronic zones flagged
Cross-asset view	Siloed NMS alarms	One live truth
"Why did it fail?"	Hours in logs	Instant, plain English

The competitive signal

Acusensus - co-founded by Ador Powertron's own Executive Chairman and deployed in India as Ador Harmony - went fully AI-first and is now ASX-listed, scaling across the US, UK & Australia. The AI DNA is already in the Ador family. NHAI's newest programs - the Data Lake, Rajmarg Saathi AI dashcams and Delhi-NCR ATMS - increasingly demand predictive analytics & condition monitoring, not just hardware.

Ador's years of NMS, ANPR & enforcement data is a moat - once it's turned into models. FluxAI extends your family's AI-first philosophy from detection to prediction, on top of your existing stack.

Be the bid that wins

NHAI is rolling out ATMS across 1,205 km in Delhi-NCR and weekly AI dashcam surveys into its Data Lake. With overspeeding behind 71% of India's road deaths (MoRTH 2022), a working predictive & dynamic-speed demo on the Mumbai-Pune Expressway turns "we can build it" into "here it is running" in your next tender.

What We're Proposing

Prove it on one corridor & two use cases - before scaling across the network

Start Focused

One corridor - the Mumbai-Pune Expressway. Two POCs: predictive asset health (NMS) + dynamic speed & road-condition. We tap your existing NMS/ATMS data, on-prem.

See the Proof

FluxAI predictions run live against your NMS/ATMS reality. Your software team judges accuracy, lead-time and the dynamic-speed logic with your own data.

Scale the Network

Convinced? Roll out to other corridors & NHAI projects, and add enforcement analytics & dashcam anomaly - at your pace, with your team.

Recommended Pilot

Best Starting Point

Asset Health + Dynamic Speed on 1 corridor

Predictive failure & chronic-junction model on NMS assets
Congestion + weather-aware dynamic-speed recommendations
Plain-language AI assistant for your ops & software team

Duration:6-8 weeks

What we need from you

1-2 years of NMS / asset-fault & maintenance history (export is fine)
Traffic-count / ANPR & weather data for one expressway stretch
One point of contact from your software team to validate outputs

Your data never leaves your premises

Next Steps

Let's move forward together

From reactive ops to a predictive highway brain

01

🤝

Working Session with Your Team

Bring in your software team - align on data access & lock pilot scope on one corridor

02

🚀

Pilot Kickoff & Success

Live in 6-8 weeks - predictive asset health & dynamic speed, proven on your data

03

📈

Scale & Win Bids

Roll out across corridors & new use cases - and take a working demo into NHAI tenders

Thank You - Ador Traffic & Enforcement × FluxAI

avinash@trinesis.com trinesis.com +91 7030999223