Bi-level programming model and algorithm of optimal toll rate for highway network

HUANG Ya-fei; LIU Tao

Volume 6 Issue 4

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HUANG Ya-fei, LIU Tao. Bi-level programming model and algorithm of optimal toll rate for highway network[J]. Journal of Traffic and Transportation Engineering, 2006, 6(4): 105-111.

Citation:

HUANG Ya-fei, LIU Tao. Bi-level programming model and algorithm of optimal toll rate for highway network[J]. Journal of Traffic and Transportation Engineering, 2006, 6(4): 105-111.

HUANG Ya-fei, LIU Tao. Bi-level programming model and algorithm of optimal toll rate for highway network[J]. Journal of Traffic and Transportation Engineering, 2006, 6(4): 105-111.

Citation:

HUANG Ya-fei, LIU Tao. Bi-level programming model and algorithm of optimal toll rate for highway network[J]. Journal of Traffic and Transportation Engineering, 2006, 6(4): 105-111.

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Bi-level programming model and algorithm of optimal toll rate for highway network

HUANG Ya-fei^1
,,
LIU Tao²

1.
School of Electric and Information Engineering, Changsha University of Science and Technology, Changsha 410076, Hunan, China
2.
School of Mathematics and Computational Science, Changsha University of Science and Technology, Changsha 410076, Hunan, China

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Author Bio:
Huang Ya-fei(1975-), male, lecturer, 86-731-8169048, xyrhyh@163.com
Received Date: 2006-07-21
Publish Date: 2006-12-25

Abstract

Abstract

In order to find system and science method to calculate the optimal toll rate of highway network, a bi-level programming model to determine the optimal toll rate was put forward, the relationship among highway network managers, toll road operators and users was described.Its upper objective function was consumer surplus which should be maximized, its lower-level problem was multi-vehicle-type stochastic user equilibrium model with elastic demand.A kind of hybrid optimization algorithm combined genetic algorithm and simulated annealing to solve it was proposed.Calculation result shows that the value of revenue for highway network influences the toll rate directly, furthermore, it influences OD traffic flows, and the influence on the vehicle types with low time value is more obvious than on the vehicle types with high time value, which indicates that the model can balance the benefits among managers, operators and users reasonably, and reflect the fact more accurately when considering vehicle types; compared with genetic algorithm and simulated annealing algorithm, the computation result of the algorithm for the model is least, the algorithm is feasible.
- traffic engineering,
- toll rate,
- stochastic user equilibrium,
- elastic demand,
- genetic algorithm,
- simulated annealing

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References(12)

References

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