Incomplete traffic flow records and sparse trajectory data prevent accurate behavioral modeling. Resolving these data gaps allows for reliable predictive analytics in urban mobility systems.

Fragmented multi-source data and complex spatiotemporal correlations lead to unreliable predictions of population and market trends. Resolving these inaccuracies allows for precise resource allocation and risk mitigation in urban planning.

Suboptimal path selection and node placement in complex urban or transit infrastructures lead to systemic delays and resource waste. Resolving these bottlenecks improves throughput and reduces operational latency in logistical and transport networks.