Ideaz

Did you find yourself juggling between apps to find a Swiggy coupon that you got a few weeks ago? Of course, you wouldn't find the right one without having to wait longer for your order or potentially see the restaurant become unserviceable.

Most of us have given up; we've decided to spend a few extra pennies rather than look for a long-lost coupon which probably wouldn't bring the bill down by any means. Anyway, what do we lose? ₹50? Haha, that's insignificant. The coupon seems redundant, doesn't it? That's where we all went wrong.

Here’s the one-stop solution:

Order Assist – Your Coupon Wallet

A single place for all the coupons you get on your phone — across all apps. With just one click, Order Assist reviews your wallet and suggests the best way to order food, book flight tickets, trains, or buses.

Key Features:

• Structured Coupon Wallet: Keeps your coupons neatly organized in folders, one for each app.

• Smart Suggestions: While booking (food, flights, trains, or buses), it analyzes options on the app along with your coupon drawer to identify the best possible deal.

• AI Enabled: Automatically collects and updates coupons in the background, ensuring you don't lose any coupons.

                 *It only adds legitimate coupons*

• Expiry Alerts: Sends notifications before coupons expire, so you never miss the chance to use an offer.

• One-Click Convenience: You can attain help in just one click. 

You won’t even miss expiry dates. With Order Assist, saving becomes effortless—no more hunting through apps or losing out on offers. Every coupon will be organized, and your life will be a breeze. 

Who is it for?

Order Assist is designed for everyday users who frequently juggle between apps for food delivery, travel bookings, and shopping. Whether you’re a student trying to save on takeout, a professional booking flights, or a family planning a trip, this tool ensures you never waste time or money on missed coupons.

 In a world full of apps and scattered offers, Order Assist brings clarity and value by turning every coupon into a real saving opportunity. Are you ready for it?

PROBLEM STATEMENT

• Hearing-impaired individuals
• People with physical disabilities
• Elderly populations
• Families and caregivers
• Volunteers and communities

THE PROBLEM:

1)Traffic Congestion People in cities waste hours stuck in traffic → reduced productivity, higher stress. Fuel consumption & air pollution rise drastically.

2)Inefficient Logistics Delivery vehicles get delayed. Fuel + time wasted due to poor route planning.

3)Public Transport Challenges Buses, trains often don’t sync well with real demand. Low utilization in some areas, overcrowding in others.

THE SOLUTION:

AI Traffic Signal Control

Smart signals that adjust timings based on real-time traffic flow.

Example: Reduce red light waiting when no vehicles are coming from one direction.

Startups can provide AI-powered traffic systems for municipalities.

Smart Route Optimization for Logistic

Platforms for delivery companies (Swiggy, Zomato, Amazon, etc.) that:

Use AI + live traffic + weather data.

Suggest shortest & safest routes.

Saves fuel and increases delivery efficiency.

Dynamic Carpooling & Ride-Sharing

AI that matches people traveling in the same direction in real time.

Reduces cars on the road, cheaper rides, greener cities.

Public Transport Demand Prediction

AI models to predict passenger demand by location & time.

Helps buses/metros plan capacity and routes better.

Entrepreneurs can build SaaS for transport corporations.

Smart Parking Solutions

AI + IoT sensors show real-time empty parking spots.

Reduces unnecessary driving while searching for parking.

Business Models for Entrepreneurs
B2G (Business-to-Government): Sell AI traffic optimization systems to city municipalities.

B2B (Business-to-Business): Logistics optimization tools for e-commerce, delivery companies, taxi services.

B2C (Business-to-Consumer): Mobile app for ride-sharing, smart parking, or commute optimization

TEACHNICAL DETAILS :

System Architecture
1)Data Collection

Sensors: CCTV cameras, LiDAR, IoT sensors, GPS data from vehicles.

Data includes: vehicle count, speed, queue length, accidents, weather conditions.

2)Data Processing Pipeline

Edge devices (mini-computers at traffic junctions) process video feeds.

Cloud/central servers aggregate data across multiple junctions.

3)AI Algorithms

Computer Vision (CV): Detect and count vehicles using CNNs (YOLO, Faster R-CNN).

Reinforcement Learning (RL):

Model traffic signals as an environment.

The RL agent learns signal timings that minimize waiting time & congestion.

Algorithms: Deep Q-Learning, Multi-Agent RL (since multiple signals interact).

4)Control System

AI model outputs green/red light duration.

Communicates with traffic light controllers via IoT protocols (MQTT, ZigBee).

🔹 Tech Stack
Hardware: CCTV/IP cameras, NVIDIA Jetson Nano/TX2 (edge AI), IoT controllers.

Software:

Python (OpenCV, PyTorch/TensorFlow for CV + RL).

Apache Kafka / MQTT for real-time streaming.

Cloud platforms (AWS IoT, Azure IoT, or GCP).

Example Flow: Smart Traffic Signal
Camera detects 30 cars waiting → sends frame to edge device.

Edge device runs YOLO model → counts cars, estimates queue length.

RL agent checks current state:

Road A: 30 cars, Road B: 5 cars.

AI decides → Give Road A green light for 40s, Road B for 15s.

Controller updates lights accordingly.

System keeps learning to minimize average waiting time.

Traffic congestion is one of the most persistent urban challenges, costing billions in lost productivity, wasted fuel, and increased emissions. Current solutions—like static traffic signals, Google Maps rerouting, or government-built flyovers—are either reactive, limited in scope, or expensive to scale. Navigation apps provide route guidance for individuals, but they lack a holistic view of city-wide traffic flow. Similarly, government initiatives like adaptive signals are siloed, often failing to integrate diverse real-world data such as weather, school timings, road events, or public transport schedules. This fragmented approach leaves a massive gap: the absence of a unified, intelligent, and dynamic traffic management system.

AITO fills this gap by functioning as a full-stack platform that integrates real-time data from multiple sources—GPS, weather APIs, city infrastructure sensors, and even public event schedules. Using AI and machine learning, AITO predicts congestion before it happens and proactively reroutes vehicles. Unlike existing tools that optimize for a single driver, AITO optimizes traffic flow at the ecosystem level, ensuring smoother city mobility, reduced emissions, and higher road safety.

The beneficiaries are wide-ranging: commuters save time, logistics companies cut fuel costs, cities reduce pollution, and governments avoid massive infrastructure spending. Communities benefit from safer, more breathable, and less chaotic urban spaces.

This problem matters to me because traffic inefficiency directly impacts everyday life in Indian cities. I’ve seen first-hand how a 20-minute commute can become 90 minutes due to poor coordination and lack of predictive systems. Solving this means not just easing individual frustration, but transforming urban mobility for millions