1969
DOI: 10.6028/jres.073b.010
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Traffic assignment problem for a general network

Abstract: A tra ns portatio n network is co ns id e red . The traffic de mands assoc iat ed wit h pairs of nodes and th e (co nvex) trav e ling cos t functions associat ed with th e link s a re assumed give n. Th e twu prob le ms of findin g th e traffi c patt e rn s whi c h e ith er minimize th e total cos t or e quilibrate th e use rs' c os ts are fo rmulat e d , and a lgo rithm s are co ns tru c ted for th e so lutio n of th ese prob lems.

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Cited by 487 publications
(365 citation statements)
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“…It is well known ( [2], [8], [15]; see also [4], especially Proposition 3.1) that, for homogeneous networks with differentiable latency functions l e , one can use the marginal costs 3f e l e (f e ) as b e in Theorem 1 to achieve the following classical result: Theorem 3. If functions l e are differentiable, thenf is an equilibrium for the selfish routing game with T P (f ) := e∈P (l e (f e ) +f e l e (f e )).…”
Section: Comparison To the Original Latenciesmentioning
confidence: 99%
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“…It is well known ( [2], [8], [15]; see also [4], especially Proposition 3.1) that, for homogeneous networks with differentiable latency functions l e , one can use the marginal costs 3f e l e (f e ) as b e in Theorem 1 to achieve the following classical result: Theorem 3. If functions l e are differentiable, thenf is an equilibrium for the selfish routing game with T P (f ) := e∈P (l e (f e ) +f e l e (f e )).…”
Section: Comparison To the Original Latenciesmentioning
confidence: 99%
“…This technique has been studied by the traffic community for a long time (cf. [5] and the references therein), especially in the context of marginal costs (see, for example, [2], [8], [15]). Each selfish user of class i using path P will experience the following path cost: path cost(P ) := latency(P ) + a(i) · taxation(P ).…”
Section: Introductionmentioning
confidence: 99%
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“…The elegance of this computational procedure in the context of variational inequality (11) lies in that it allows one to utilize algorithms for the solution of the uncapacitated systemoptimization problem (for which numerous algorithms exist in the transportation science literature) with straightforward update procedures at each iteration to obtain the link capacities and the Lagrange multipliers. To solve the former problem we utilize in Section 3 the well-known equilibration algorithm (system-optimization version) of Dafermos and Sparrow (1969), which has been widely applied (see also, e.g., Nagurney (1999Nagurney ( , 2006). Recall that the modified projection method (cf.…”
Section: Theoremmentioning
confidence: 99%
“…The quadratic programming problem in product flows corresponds to the classical system -optimization problem (cf. Dafermos and Sparrow (1969) and Nagurney (1999)), for which numerous efficient algorithms exist since transportation network problems are widely solved in practice. The solutions to the link capacity subproblems, as well as the Lagrange multiplier subproblems, in turn, can be obtained via closed form expressions, since the underlying feasible sets are very simple.…”
Section: Theorem 4: Convergencementioning
confidence: 99%