Towards Inclusive Mobility Modeling: Characterizing and Evaluating Elderly Trajectory Patterns in Urban Systems

1. 1Collect and preprocess trajectory data from public sources, such as bike-sharing systems
2. 2Apply data mining techniques to identify patterns in elderly mobility
3. 3Evaluate the impact of underrepresentation on mobility modeling using statistical methods
4. 4Develop and test inclusive mobility models that account for elderly trajectory patterns
5. 5Integrate the models into urban planning decision-making processes

Data scientists and urban planners on a team can benefit from this knowledge to create more inclusive and accurate mobility models, which can inform policy decisions and improve the quality of life for elderly citizens

💡 Underrepresentation of elderly demographic groups in public mobility datasets can introduce systematic bias into mobility modeling and downstream urban planning

🚴‍♀️ Inclusive mobility modeling matters! 📊 Learn how to reduce bias and improve urban planning for elderly citizens #urbanplanning #mobilitymodeling