arXiv:2606.02287v1 Announce Type: new Abstract: Urban trajectory generation is a fundamental task for transportation simulation, urban planning, and mobility analytics. However, systematic comparison across trajectory generation methods remains difficult because existing studies often rely on different datasets, preprocessing pipelines, trajectory representations, and evaluation metrics. This fragmentation makes it unclear whether reported performance differences arise from the generation mechanism itself or from inconsistent experimental protocols. To address this issue, we present CityTrajBe
The proliferation of various trajectory generation methods necessitates a standardized benchmark to accurately compare their effectiveness and accelerate research in urban mobility.
A unified benchmark will enable more robust development of AI models for transportation simulation and urban planning, leading to more efficient and adaptable city infrastructure.
The ability to systematically compare and improve urban trajectory generation models will be enhanced, leading to faster progress in mobility analytics and smart city applications.
- · AI researchers (mobility)
- · Urban planners
- · Transportation technology companies
- · Smart city solution providers
- · Fragmented research efforts
- · Inefficient development cycles
Improved accuracy and reliability of AI models for predicting and managing urban traffic.
More sophisticated and data-driven urban planning methodologies, optimizing resource allocation and reducing congestion.
Potential for dynamic, AI-driven urban infrastructure that adapts in real-time to optimize flow and consumption.
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Read at arXiv cs.LG