An important issue for providing better guarantees of Quality of Service (QoS) to applications is QoS rout-ing. The task of QoS routing is to determine a feasible path that satisfies a set of constraints while maintai...An important issue for providing better guarantees of Quality of Service (QoS) to applications is QoS rout-ing. The task of QoS routing is to determine a feasible path that satisfies a set of constraints while maintaining high u-tilization of network resources. For the purpose of achieving the latter objective additional optimality requirementsneed to be imposed. In general, multi-constrained path selection problem is NP-hard so it cannot be exactly solved inpolynomial time. Accordingly heuristics and approximation algorithms with polynomial or pseudo-polynomial timecomplexity are often used to deal with this problem. However, many of these algorithms suffer from either excessivecomputational complexity that cannot be used for online network operation or low performance. Moreover, they gen-erally deal with special cases of the problem (e. g. , two constraints without optimization, one constraint with opti-mization, etc. ). In this paper, the authors propose a new efficient algorithm (EAMCOP) for the problem. Makinguse of efficient pruning policy, the algorithm reduces greatly the size of search space and improves the computationalperformance. Although the proposed algorithm has exponential time complexity in the worst case, it can get verygood performance in real networks. The reason is that when the scale of network increases, EAMCOP controls effi-ciently the size of search space by constraint conditions and prior queue that improves computational efficiency. Theresults of simulation show that the algorithm has good performance and can solve effectively multi-constrained opti-mal path (MCOP) problem.展开更多
The cumulative prospect theory(CPT) is applied to study travelers' route choice behavior in a degradable transport network. A cumulative prospect theory-based user equilibrium(CPT-UE) model considering stochastic ...The cumulative prospect theory(CPT) is applied to study travelers' route choice behavior in a degradable transport network. A cumulative prospect theory-based user equilibrium(CPT-UE) model considering stochastic perception error(SPE) within travelers' route choice decision process is developed. The SPE is conditionally dependent on the actual travel time distribution, which is different from the deterministic perception error used in the traditional logit-based stochastic user equilibrium. The CPT-UE model is formulated as a variational inequality problem and solved by a heuristic solution algorithm. Numerical examples are provided to illustrate the application of the proposed model and efficiency of the solution algorithm. The effects of SPE on the reference point determination, cumulative prospect value estimation, route choice decision and network performance evaluation are investigated.展开更多
文摘An important issue for providing better guarantees of Quality of Service (QoS) to applications is QoS rout-ing. The task of QoS routing is to determine a feasible path that satisfies a set of constraints while maintaining high u-tilization of network resources. For the purpose of achieving the latter objective additional optimality requirementsneed to be imposed. In general, multi-constrained path selection problem is NP-hard so it cannot be exactly solved inpolynomial time. Accordingly heuristics and approximation algorithms with polynomial or pseudo-polynomial timecomplexity are often used to deal with this problem. However, many of these algorithms suffer from either excessivecomputational complexity that cannot be used for online network operation or low performance. Moreover, they gen-erally deal with special cases of the problem (e. g. , two constraints without optimization, one constraint with opti-mization, etc. ). In this paper, the authors propose a new efficient algorithm (EAMCOP) for the problem. Makinguse of efficient pruning policy, the algorithm reduces greatly the size of search space and improves the computationalperformance. Although the proposed algorithm has exponential time complexity in the worst case, it can get verygood performance in real networks. The reason is that when the scale of network increases, EAMCOP controls effi-ciently the size of search space by constraint conditions and prior queue that improves computational efficiency. Theresults of simulation show that the algorithm has good performance and can solve effectively multi-constrained opti-mal path (MCOP) problem.
基金Project(2012CB725400)supported by the National Basic Research Program of ChinaProjects(71271023,71322102)supported by the National Science Foundation of ChinaProject(2015JBM053)supported by the Fundamental Research Funds for the Central Universities,China
文摘The cumulative prospect theory(CPT) is applied to study travelers' route choice behavior in a degradable transport network. A cumulative prospect theory-based user equilibrium(CPT-UE) model considering stochastic perception error(SPE) within travelers' route choice decision process is developed. The SPE is conditionally dependent on the actual travel time distribution, which is different from the deterministic perception error used in the traditional logit-based stochastic user equilibrium. The CPT-UE model is formulated as a variational inequality problem and solved by a heuristic solution algorithm. Numerical examples are provided to illustrate the application of the proposed model and efficiency of the solution algorithm. The effects of SPE on the reference point determination, cumulative prospect value estimation, route choice decision and network performance evaluation are investigated.
