For static traffic assignment problems, it is well known that (1) for some users the experienced travel time in a system optimum assignment can be substantially higher than the experienced travel time in a user equilibrium assignment, and (2) the total travel time in user equilibrium can be substantially higher than the total travel time in system optimum. By seeking system optimal traffic flows subject to user constraints, a compromise assignment can be obtained that balances system and user objectives. To this aim, a linear model and an efficient heuristic algorithm are proposed in this paper. A computational study shows that the proposed model, along with the heuristic algorithm, is able to provide fair solutions with near-optimal total travel time within very short computational time.

System optimal routing of traffic flows with user constraints using linear programming

Angelelli E.;Morandi V.
;
Savelsbergh M.;Speranza M. G.
2021-01-01

Abstract

For static traffic assignment problems, it is well known that (1) for some users the experienced travel time in a system optimum assignment can be substantially higher than the experienced travel time in a user equilibrium assignment, and (2) the total travel time in user equilibrium can be substantially higher than the total travel time in system optimum. By seeking system optimal traffic flows subject to user constraints, a compromise assignment can be obtained that balances system and user objectives. To this aim, a linear model and an efficient heuristic algorithm are proposed in this paper. A computational study shows that the proposed model, along with the heuristic algorithm, is able to provide fair solutions with near-optimal total travel time within very short computational time.
2021
2021
Ateneo di appartenenza
PE1_15 Discrete mathematics and combinatorics
PE1_19 Control theory and optimization
PE1_20 Application of mathematics in sciences
Esperti anonimi
Inglese
Internazionale
STAMPA
293
3
863
879
17
Congestion; Constrained system optimum; Latency function; Linear programming; Piecewise linear approximation; Traffic
https://www.sciencedirect.com/science/article/pii/S0377221720310894
4
info:eu-repo/semantics/article
262
Angelelli, E.; Morandi, V.; Savelsbergh, M.; Speranza, M. G.
1 Contributo su Rivista::1.1 Articolo in rivista
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/547567
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