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Cited by 936

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

On Stochastic Models of Traffic Assignment

Daganzo, Carlos F., Sheffi, Yosef·1977·Transportation Science

Abstract

This paper contains a quantitative evaluation of probabilistic traffic assignment models and proposes an alternate formulation. First, the concept of stochastic-user-equilibration (S-U-E) is formalized as an extension of Wardrop's user-equilibration criterion. Then, the stochastic-network-loading (S-N-L) problem (a special case of S-U-E for networks with constant link costs) is analyzed in detail and an expression for the probability of route choice which is based on two general postulates of user behavior is derived. The paper also discusses the weaknesses of existing S-N-L techniques with special attention paid to Dial's multipath method and compares them to the suggested approach. The proposed model seems reasonable and does not exhibit the inherent weaknesses of the logit model when applied to sets of routes which overlap heavily. The discussion is supported by several numerical examples on small contrived networks. The paper concludes with the discussion of two techniques that can be used to approximate the link flows resulting from the proposed model in large networks.

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Metadata

Authors
Daganzo, C. F., & Sheffi, Y.
Venue
Transportation Science
Published
1977-08
Volume
11
Issue
3
Pages
253-274
ISSN
  • 0041-1655 (ISSN-L)
  • 1526-5447
OpenAlex
W2039423859
Cited by
936
References
9
FWCI
51.27
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: On Stochastic Models of Traffic Assignment. https://app.cochat.ai/papers/10.1287/trsc.11.3.253
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Daganzo, C. F., & Sheffi, Y. (1977). On Stochastic Models of Traffic Assignment. Transportation Science. https://doi.org/10.1287/trsc.11.3.253

Data sources

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.