AI in Transportation
Drive. Plan. Navigate.
Course Objective
This course provides a comprehensive foundation in how AI technologies are revolutionizing transportation systems across the globe. From self-driving cars to smart traffic systems and AI-optimized logistics, learners will explore how data-driven intelligence is transforming the future of movement. With interactive tools and real-world case studies, students will gain practical skills and build projects aligned to industry needs.
Module 1:
AI Foundations in Mobility
- • Role of AI in intelligent transport systems (ITS)
- • Key technologies: Machine Learning, Computer Vision, Deep Reinforcement Learning
- • Case study: Tesla’s Autopilot vs Waymo’s AI stack
- • Introduction to relevant datasets: NGSIM, Berkeley DeepDrive
Module 2:
Smart Cities & AI Traffic Management
- Traffic signal optimization using reinforcement learning (Q-Learning, DQN)
- Predictive congestion analytics using LSTM models
- GIS & geospatial data for urban planning
- Hands-on: Simulate a smart traffic light using SUMO + Python
Module 3:
Autonomous Vehicles & Perception Systems
- Sensor fusion: LIDAR, radar, cameras, GPS
- Neural nets for lane detection and obstacle avoidance
- YOLOv7/8 for real-time object detection
- Hands-on: Implement perception system using OpenCV and YOLO with training data
Module 4:
AI in Supply Chain & Logistics
- Vehicle routing problem (VRP) with dynamic constraints
- Real-time delivery estimation with Kalman filtering
- Cold-chain logistics AI: IoT + cloud integration
- Project: Build route planner using Google OR-Tools
Module 5:
Predictive Maintenance in Fleets
- Anomaly detection in vehicle telemetry using autoencoders
- Fault prediction with sensor time-series analysis
- Tools: Azure Machine Learning, AWS IoT Greengrass
- Dashboard project: Visualize faults before failures
Module 6:
Aviation, Maritime & Rail AI Applications
- Predicting flight delays using historical and weather data
- Maritime AI: route optimization + cargo detection via satellite imaging
- Rail maintenance AI: Indian Railways case study
- Hands-on: Work with aviation delay dataset on Kaggle
Module 7:
Green Mobility and Sustainable AI
- EV battery health prediction using supervised models
- AI-based placement of EV charging stations
- Carbon emissions tracker: build with Pandas and Matplotlib
- Case: Delhi’s electrification of public buses
Module 8:
Ethics, Safety & Regulatory Compliance
- AV decision-making ethics: edge cases and policy
- Explainability in transport AI (XAI)
- GDPR, India’s Digital Personal Data Protection Act, and mobility data
- Industry guest speaker: Legal expert on transport AI regulation
Learning Tools & Platforms Used
Azure IoT + Power BI | Fleet maintenance dashboards |
CARLA Simulator | Autonomous vehicle environment |
Google OR-Tools | Vehicle routing and logistics |
SUMO + TraCI | Traffic and mobility simulation |
YOLOv8, OpenCV, Python, TensorFlow | Computer vision projects |
Kaggle + BigQuery | Aviation & delivery data analytics |
| Transport data exploratio |
| GIS visualizations |
📈 Learning Outcomes
By the end of this course, learners will:
- ✅ Build smart traffic control simulation
- ✅ Apply CV and sensor data to vehicle detection
- ✅ Solve real-world logistics problems using AI
- ✅ Deploy dashboards for predictive fleet maintenance
- ✅ Analyze and forecast air/rail/maritime schedules
- ✅ Understand how AI can reduce urban transport emissions
- ✅ Evaluate safety, fairness, and policy in AI-driven systems
Duration:
| Section | Details |
| Total Duration | 6 Weeks (Flexible) |
| Weekly Commitment | 5–6 hrs |
| Delivery Mode: | Online (Video + Live sessions + Labs) |
| Capstone Project: | Real-world mobility solution using AI |