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Advanced Intelligent Transportation Systems Training Course
Introduction
Advanced Intelligent Transportation Systems (AITS) represent the cutting edge of modern mobility, integrating AI-driven traffic analytics, IoT-enabled sensing, connected vehicle ecosystems, and smart infrastructure technologies to optimize transportation networks. As cities move toward smart mobility and autonomous transportation, AITS provides the digital backbone for reducing congestion, enhancing safety, streamlining public transit operations, and improving environmental sustainability. Advanced Intelligent Transportation Systems Training Course equips learners with a deep understanding of the emerging innovations shaping urban mobility in the era of Industry 4.0 and smart city transformation.
Through real-world case studies, simulation exercises, and data-driven insights, participants will explore how advanced technologies such as machine learning, edge computing, vehicle-to-everything (V2X) communication, and big data transportation analytics redefine the planning and management of transportation systems. By the end of the program, learners will gain the competencies required to design, evaluate, and implement transformative AITS solutions aligned with global trends in digital mobility, intelligent infrastructure, and sustainable transportation innovation.
Programme Curriculum
Advanced Intelligent Transportation Systems Training Course
Introduction
Advanced Intelligent Transportation Systems (AITS) represent the cutting edge of modern mobility, integrating AI-driven traffic analytics, IoT-enabled sensing, connected vehicle ecosystems, and smart infrastructure technologies to optimize transportation networks. As cities move toward smart mobility and autonomous transportation, AITS provides the digital backbone for reducing congestion, enhancing safety, streamlining public transit operations, and improving environmental sustainability. Advanced Intelligent Transportation Systems Training Course equips learners with a deep understanding of the emerging innovations shaping urban mobility in the era of Industry 4.0 and smart city transformation.
Through real-world case studies, simulation exercises, and data-driven insights, participants will explore how advanced technologies such as machine learning, edge computing, vehicle-to-everything (V2X) communication, and big data transportation analytics redefine the planning and management of transportation systems. By the end of the program, learners will gain the competencies required to design, evaluate, and implement transformative AITS solutions aligned with global trends in digital mobility, intelligent infrastructure, and sustainable transportation innovation.
Course Duration
5 days
Course Objectives
Understand the foundations of AI-powered Intelligent Transportation Systems.
Apply IoT-enabled sensing and communication technologies in transportation networks.
Analyze big data mobility patterns using machine learning.
Evaluate V2X, V2I, and V2P communication frameworks in smart cities.
Develop strategies for smart traffic management and congestion mitigation.
Assess autonomous vehicle integration within existing transport systems.
Implement predictive analytics for transportation planning.
Explore edge computing and cloud-based decision platforms.
Design cybersecurity-resilient mobility systems.
Assess sustainable and green mobility solutions using AITS.
Build digital twin transportation models for scenario testing.
Examine global mobility-as-a-service (MaaS) frameworks.
Apply best practices in smart mobility policy, governance, and innovation ecosystems.
Target Audience
Transportation planners
Traffic engineers and roadway designers
Smart city specialists
Public sector transportation authorities
Data scientists and AI analysts
Civil engineering professionals
Autonomous vehicle researchers
ITS solution developers and technology integrators
Course Modules
Module 1: Foundations of Intelligent Transportation Systems
Evolution of ITS technologies
Core ITS architecture and global standards
Digital mobility ecosystems overview
Key components of smart infrastructure
Role of data and connectivity in ITS Case Study: Japan’s Smart Mobility Framework for Urban Traffic Optimization.
Module 2: IoT and Connected Vehicle Technologies
IoT sensor networks for traffic monitoring
V2V, V2I, and V2X communication protocols
Telematics and real-time data acquisition
Edge computing for low-latency decision-making
Connected vehicle cybersecurity considerations Case Study: U.S. Department of Transportation Connected Vehicle Pilot
Module 3: AI & Machine Learning in Transportation Analytics
Predictive traffic modeling
Deep learning for pattern recognition
Real-time anomaly detection
Transportation demand forecasting
Intelligent decision support systems Case Study: Google’s AI-based Traffic Signal Optimization in Urban Corridors.
Module 4: Smart Traffic Management & Control Systems
Adaptive traffic signal control systems
Dynamic lane management
Incident detection and response automation
Congestion management techniques
Integrated multimodal transportation control centers Case Study: Singapore’s AI-driven Expressway Monitoring System.
Module 5: Autonomous & Electric Mobility Ecosystems
Levels of vehicle automation
Sensor fusion and navigation systems
EV charging infrastructure planning
Safety and regulatory frameworks
Integrating autonomous vehicles into city networks Case Study: Waymo Autonomous Taxi Deployment Trials.
Module 6: Urban Mobility Planning & Digital Twins
Simulation and modeling tools
Data-driven mobility planning
Scenario analysis using digital twins
Infrastructure readiness assessments
Multimodal optimization strategies Case Study: Helsinki’s Digital Twin of Urban Transportation Systems.
Module 7: Sustainable Transportation & Green Mobility
Low-carbon mobility strategies
Intelligent public transit systems
Energy-efficient traffic management
Active mobility and micro-mobility integration
Urban environmental analytics Case Study: Copenhagen’s Smart Mobility & Carbon-Neutral Transport Plan.
Module 8: ITS Governance, Policy & Implementation
National and international ITS policy frameworks
Stakeholder engagement and project governance
Data privacy, security, and ethical considerations
Funding and procurement strategies
Roadmap for ITS deployment Case Study: European Union’s ITS Directive Implementation.
Training Methodology
This course employs a participatory and hands-on approach to ensure practical learning, including:
Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.
Register as a group from 3 participants for a Discount
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to FINESKILL TRAINING CENTER account, as indicated in the invoice so as to enable us prepare better for you.