Some Theoretical Aspects of Road Traffic Research.¶
WARDROP, J. G. (1952). Some Theoretical Aspects of Road Traffic Research. Proceedings of the Institution of Civil Engineers, 1(3), 325-362.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Nash Equilibrium
- Traffic and transportation — user-equilibrium in route assignment is a Nash concept: each driver chooses the route minimising their own travel time, and no driver can improve by switching given the others' choices.
This sourceStates Wardrop's first principle of user-equilibrium route assignment — no driver can reduce travel time by switching routes — the traffic instance of Nash equilibrium.
- Traffic and transportation — user-equilibrium in route assignment is a Nash concept: each driver chooses the route minimising their own travel time, and no driver can improve by switching given the others' choices.
Mechanisms¶
- Traffic Assignment Model
- Iterate to user equilibrium. Travelers are shifted toward faster routes and link times updated, repeatedly, until no traveler can improve by switching — Wardrop's first principle.
This sourceDefines Wardrop's first principle as a route-use equilibrium in which no traveler can improve travel time by switching routes.
- Iterate to user equilibrium. Travelers are shifted toward faster routes and link times updated, repeatedly, until no traveler can improve by switching — Wardrop's first principle.
- Traffic Assignment or Flow Equilibrium Model
- … is that it makes the decentralized-vs-coordinated gap computable under hypothetical capacity scenarios, so decisions rest on a modeled equilibrium rather than on static max-flow arithmetic that ignores behavior — which is exactly the arithmetic that predicts improvement where selfish routing delivers the opposite.
This sourceFormulates both decentralized user equilibrium and coordinated system-optimal traffic assignments.
- … is that it makes the decentralized-vs-coordinated gap computable under hypothetical capacity scenarios, so decisions rest on a modeled equilibrium rather than on static max-flow arithmetic that ignores behavior — which is exactly the arithmetic that predicts improvement where selfish routing delivers the opposite.
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