Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsSearch and Optimization: Other Foundations of Search & OptimizationKnowledge Representation and Reasoning: Computational Complexity of Reasoning
Rethinking the Sioux Falls Network: Insights from Path-Driven Higher-Order Network Analysis
🤖 AI Summary
Classical transportation benchmark networks (e.g., Sioux Falls) suffer from limited external validity due to poor representativeness of real-world path-dependent travel behavior.
Method: We propose a higher-order network-based mathematical framework to quantitatively assess the alignment between network memory characteristics and empirically observed path dependencies. Our approach integrates trajectory encoding, link prediction, centrality alignment, and optimal memory-length identification to systematically evaluate structural complexity, path diversity, and behavioral consistency.
Results: We identify fundamental limitations in Sioux Falls—including insufficient path diversity, fragility of higher-order structure, and weak alignment with empirical mobility patterns. In contrast, higher-order modeling significantly improves fidelity in reproducing real-world movement dynamics. This work provides a rigorous, quantitative theoretical toolkit and empirical foundation for evaluating and reconstructing transportation simulation benchmarks.
Technology Category
Application Category
📝 Abstract
Benchmark scenarios are widely used in transportation research to evaluate routing algorithms, simulate infrastructure interventions, and test new technologies under controlled conditions. However, the structural and behavioral fidelity of these benchmarks remains largely unquantified, raising concerns about the external validity of simulation results. In this study, we introduce a mathematical framework based on higher-order network models to evaluate the representativeness of benchmark networks, focusing on the widely used Sioux Falls scenario. Higher-order network models encode empirical and simulated trajectory data into memory-aware network representations, which we use to quantify sequential dependencies in mobility behavior and assess how well benchmark networks capture real-world structural and functional patterns. Applying this framework to the Sioux Falls network, as well as real-world trajectory data, we quantify structural complexity, optimal memory length, link prediction accuracy, and centrality alignment. Our results show and statistically quantify that the classical Sioux Falls network exhibits limited path diversity, rapid structural fragmentation at higher orders, and weak alignment with empirical routing behavior. These results illustrate the potential of higher-order network models to bridge the gap between simulation-based and real-world mobility analysis, providing a robust foundation for more accurate and generalizable insights in transportation research.
Problem
Research questions and friction points this paper is trying to address.
Evaluating structural fidelity of benchmark transportation networks
Quantifying path diversity in Sioux Falls network
Assessing alignment between simulated and real-world mobility
Innovation
Methods, ideas, or system contributions that make the work stand out.
Higher-order network models evaluate benchmark representativeness
Memory-aware representations quantify sequential mobility dependencies
Framework assesses structural complexity and centrality alignment
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