AI-Assisted Optimized Route Finder Integrating Dijkstra’s Algorithm and Graph Theory with Predictive Traffic Analytics – Dr. S Chithra

Medium Link

https://medium.com/@chithra.kdc/ai-assisted-optimized-route-finder-integrating-dijkstras-algorithm-and-graph-theory-with-e740dc9b367b

Course: BCA V semester –Artificial Intelligence MCA II semester& BCA IV semester – Design and Analysis of Algorithm, PGDM – IV Term Machine Learning

Teaching Notes: AI-Assisted Optimized Route Finder Using Dijkstra’s Algorithm

Dijkstra’s Algorithm, a core concept in graph theory, is used to find the shortest path between locations in a network. When combined with AI and predictive traffic analytics, it enables real-time route optimization by adjusting for traffic density, congestion, and road conditions. This integration powers intelligent navigation systems like Google Maps, helping users reach destinations faster. For students, it provides practical exposure to algorithm design, data analytics, and AI applications in smart transportation systems.

Learning Objectives

This case let will enable students to:

1. Understand the basics of graph theory and Dijkstra’s Algorithm.
2. Explain how the algorithm determines the shortest path.
3. Identify the role of AI in predictive traffic optimization.
4. Apply the concept to real-world route planning scenarios.
5. Recognize the significance of AI-assisted routing in smart transportation systems.

1. Introduction

With the rise of intelligent transportation systems and navigation applications such as Google Maps, route optimization has become a critical research area in both computer science and artificial intelligence (AI).

• The effectiveness of a route finder depends on two major factors:
• The efficiency of the shortest path algorithm, and
• The ability to adapt dynamically to real-time traffic conditions.

This study explores how Dijkstra’s Algorithm, grounded in graph theory, can be combined with AI-driven predictive traffic analytics to develop an AI-Assisted Optimized Route Finder.

The objective is to not only compute the shortest path mathematically but also to predict and adapt to real-world road conditions for improved accuracy and travel efficiency.

Graph Theory and Dijkstra’s Algorithm

In graph theory, a road network is represented as a weighted graph, where:

• Nodes (vertices) represent intersections or locations,
• Edges represent roads,
• Weights represent the distance, cost, or travel time between two nodes.

Dijkstra’s Algorithm, introduced by Edsger W. Dijkstra (1956), is used to compute the shortest path between nodes in a weighted graph. It ensures that the chosen route minimizes total travel cost (distance or time).

AI and Predictive Traffic Analytics

While Dijkstra’s Algorithm provides an optimal path under static conditions, AI-based predictive models enhance the system by:

• Forecasting traffic congestion using historical and live data,
• Adjusting edge weights dynamically in real-time,
• Re-computing optimal routes as traffic conditions evolve.

Case Study: Sara’s Daily Commute

Sara, a marketing executive in Bengaluru, commutes daily from Indiranagarto Electronic City, a distance of around 19 kilometers.
Her route is affected by fluctuating traffic conditions, particularly during morning rush hours.

Problem Statement

Sara often experienced delays of up to 30 minutes due to unpredictable congestion on her usual routes.

A traditional route finder (without traffic awareness) failed to adapt to these variations.

Hence, a need arose for an AI-assisted route optimization system that could:

• Predict congestion ahead of time,
• Update travel routes in real time, and
• Guarantee the shortest possible travel duration.

Methodology

This section explains how the AI-Assisted Optimized Route Finder was developed and applied to Sara’s real-world commute between Indiranagar and Electronic City in Bengaluru. The system works in three major stages: Graph Construction, Shortest Path Calculation using Dijkstra’s Algorithm, and AI-based Dynamic Optimization.

Step 1: Graph Construction

The road network was represented as a graph, with:

• Nodes: Indiranagar, MG Road, Koramangala, BTM Layout, Electronic City.
• Edges: Roads connecting them, with weights based on distance and estimated travel time.
• Weights = Travel time or distance between them

In Sara’s route network, the main points considered were:

The estimated travel times (in minutes) between these points are as follows:

Step 2: Dijkstra’s Algorithm Implementation

The goal is to find the shortest route from A (Indiranagar) to E (Electronic City) based on total travel time.

Step 2.1 – Initialization

Start from A:

• Distance from A to A = 0
• Distance to all others = ∞ initially
  → A(0), B(∞), C(∞), D(∞), E(∞)

Step 2.2 – Visit Neighbours of A

From A:

• To B = 0 + 3 = 3
• To C = 0 + 5 = 5
  Now: A(0), B(3), C(5), D(∞), E(∞)

Mark A as visited.

 Step 2.3 – Move to the Nearest Unvisited Node (B = 3)

From B:

• To C = 3 + 4 = 7 → but C already has 5 (keep 5)
• To D = 3 + 8 = 11
  Now: A(0), B(3), C(5), D(11), E(∞)

Mark B as visited.

 Step 2.4 – Move to Next Nearest (C = 5)

From C:

• To D = 5 + 6 = 11 (equal to current D = 11, no change)
  Now: A(0), B(3), C(5), D(11), E(∞)

Mark C as visited.

 Step 2.5 – Move to D (11)

From D:

• To E = 11 + 7 = 18
  Now: A(0), B(3), C(5), D(11), E(18)

Mark D as visited.

 Result:

Shortest path = A → B → D → E
Total estimated travel time = 18 minutes

Step 3: AI Integration and Predictive Adjustment

While Dijkstra’s Algorithm provides the shortest route under static conditions, the AI module enhances the system by making it adaptive to real-world traffic.

The AI component analyzes:

Real-time traffic data from GPS, mobile apps, or road sensors

Historical traffic trends, such as average congestion at certain times of day

Predictive models (like regression or neural networks) that estimate congestion before it happens

Step 3: AI Integration and Predictive Adjustment

While Dijkstra’s Algorithm provides the shortest route under static conditions, the AI module enhances the system by making it adaptive to real-world traffic.

The AI component analyzes:

• Real-time traffic data from GPS, mobile apps, or road sensors
• Historical traffic trends, such as average congestion at certain times of day
• Predictive models (like regression or neural networks) that estimate congestion before it happens

Example of Dynamic Adjustment:

If the AI module predicts heavy congestion between B (MG Road) and D (BTM Layout) (increasing travel time from 8 → 15 minutes),

the algorithm recalculates the total travel time:

Now, a new optimal path is computed:

→ A → C → D → E, with a total time of 5 + 6 + 7 = 18 minutes,

which may now match or outperform the old route under congestion.

The integrated system continuously:

1. Computes shortest paths using graph theory (Dijkstra’s Algorithm).
2. Monitors real-time conditions using AI-based predictive traffic analytics.
3. Adapts routes dynamically, ensuring that Riya always travels via the fastest available path.

This hybrid approach effectively combines mathematical precision with AI-driven adaptability, resulting in smarter and more efficient urban commuting.

Conclusion

This case study demonstrates that combining classical graph algorithms with modern AI predictive analytics results in a highly efficient and intelligent navigation system.The AI-Assisted Optimized Route Finder not only calculates the shortest path but also learns from real-time traffic data to ensure the fastest possible route.Such hybrid systems can transform urban mobility by enabling smart, adaptive, and data-driven route planning for millions of users.

Conceptual Questions

1. Why is Dijkstra’s Algorithm not suitable for graphs with negative edge weights?
2. How is Dijkstra’s Algorithm applied in real-world navigation systems like Google Maps?
3. Differentiate between Dijkstra’s Algorithm and the A* (A-star) Algorithm.
4. What is the significance of edge weights in route-finding algorithms?
5. What does it mean when we say a node has “neighbours” in graph theory?