Executive Development Programme in Smart Urban Mobility Analytics

• Introduction to Smart Urban Mobility Analytics: Understanding the concept, importance, and benefits of smart urban mobility analytics. Exploring the role of data-driven decision-making in modern urban transportation.
• Data Collection Techniques: Examining various data collection methods, including GPS tracking, sensors, IoT devices, and public transport data. Discussing data privacy and security considerations.
• Data Analysis Tools and Techniques: Introducing popular data analysis tools such as Python, R, and Tableau. Demonstrating various analytical techniques, including descriptive, diagnostic, predictive, and prescriptive analytics.
• Transportation Demand Modeling: Understanding the principles of transportation demand modeling, focusing on trip generation, trip distribution, mode choice, and traffic assignment models.
• Smart Urban Mobility Solutions: Investigating innovative smart mobility solutions, such as Mobility as a Service (MaaS), electric and autonomous vehicles, and multi-modal transportation systems.
• Performance Metrics and Evaluation: Identifying key performance indicators and evaluation metrics for smart urban mobility systems. Assessing the impact of mobility analytics on transportation system efficiency, safety, and sustainability.
• Policy and Strategy Development: Developing evidence-based policies and strategies for smart urban mobility, using data-driven insights. Exploring the role of public-private partnerships in implementing smart mobility solutions.
• Change Management and Stakeholder Engagement: Managing change in urban mobility systems and engaging stakeholders, including government agencies, transport operators, and the public.
• Ethics and Responsibility in Smart Urban Mobility Analytics: Examining the ethical implications of smart urban mobility analytics, including issues related to data privacy, cybersecurity, and social equity.