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PreprintCC BY 4.0arXiv

A Link-Based Algorithm for Stochastic User Equilibrium with Bounded Choice and Local Detour Threshold

  • Takeshi Nagae
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Abstract

This study develops a novel solution method for stochastic user equilibrium (SUE) under the bounded choice model with local detour threshold (BCM-LDT SUE; Rasmussen et al., 2024), which restricts the choice set in line with observed route choice behavior. The model assigns zero probability to paths whose total cost exceeds a bound or whose local detour exceeds a threshold; thus, the path choice set varies with link costs. The proposed algorithm updates link flows while implicitly enumerating the path choice set via a depth-first search (DFS) pruned with the BCM-LDT bounds. This distinguishes it from the original algorithm, which updates path flows, and enables quasi-Newton-type acceleration. Since paths are never stored, the method can handle choice sets totaling on the order of 107 paths. On a personal computer, it reached equilibrium in a few to about fifteen minutes, not only for the Anaheim network under the original settings but also for larger networks such as Winnipeg and Barcelona.

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Cite

BibTeXRIS
BibTeX
@misc{nagae2026linkbased,
  title = {{A Link-Based Algorithm for Stochastic User Equilibrium with Bounded Choice and Local Detour Threshold}},
  author = {Takeshi Nagae},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.29151},
  eprint = {2610.29151},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.29151},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - A Link-Based Algorithm for Stochastic User Equilibrium with Bounded Choice and Local Detour Threshold
AU  - Takeshi Nagae
PY  - 2026
DA  - 2026/10/21
PB  - arXiv
DO  - 10.48550/arxiv.2610.29151
UR  - https://arxiv.org/abs/2610.29151
AB  - This study develops a novel solution method for stochastic user equilibrium (SUE) under the bounded choice model with local detour threshold (BCM-LDT SUE; Rasmussen et al., 2024), which restricts the choice set in line with observed route choice behavior. The model assigns zero probability to paths whose total cost exceeds a bound or whose local detour exceeds a threshold; thus, the path choice set varies with link costs. The proposed algorithm updates link flows while implicitly enumerating the path choice set via a depth-first search (DFS) pruned with the BCM-LDT bounds. This distinguishes it from the original algorithm, which updates path flows, and enables quasi-Newton-type acceleration. Since paths are never stored, the method can handle choice sets totaling on the order of 10^7 paths. On a personal computer, it reached equilibrium in a few to about fifteen minutes, not only for the Anaheim network under the original settings but also for larger networks such as Winnipeg and Barcelona.
ER  -