Towards Inclusive Mobility Modeling: Characterizing and Evaluating Elderly Trajectory Patterns in Urban Systems
arXiv:2606.31207v1 Announce Type: new Abstract: The rapid advance of smart cities increasingly depends on trajectory data mining, yet underrepresented demographic groups, particularly the elderly, are often sparsely represented in public mobility datasets. This underrepresentation can introduce systematic bias into mobility modeling and downstream urban planning. Using the 2016-2020 Jersey City subset of the Citi Bike System Data, this study quantitatively examines how the absence of underrepresented subgroups' mobility signatures affects mobility modeling, using synthetic trajectory generatio
The increasing reliance on AI for urban planning and smart city development necessitates robust, unbiased data, making fair representation a critical and timely challenge.
Biased mobility modeling due to underrepresented demographics can lead to inequitable urban planning and resource allocation, highlighting the need for inclusive AI development.
This research highlights a crucial gap in current mobility data and modeling, pushing for more inclusive data collection and algorithmic design to prevent systemic biases in future urban systems.
- · Elderly population advocacy groups
- · Smart city developers focusing on inclusivity
- · Data scientists specializing in bias detection
- · Urban planners relying on biased datasets
- · Models trained on unrepresentative data
- · Entities prioritizing efficiency over equity in data collection
Improved understanding and representation of diverse demographic mobility patterns will emerge.
Urban infrastructure and services will be designed to better accommodate the needs of all population segments, including the elderly.
More equitable and resilient smart cities will evolve, driven by ethically sourced and representative data.
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Read at arXiv cs.AI