In order to investigate the effect of the use of battery electric vehicles on traffic dynamics,the valid paths of electric battery vehicles are defined and a check-based method is proposed to obtain them.Then,assuming...In order to investigate the effect of the use of battery electric vehicles on traffic dynamics,the valid paths of electric battery vehicles are defined and a check-based method is proposed to obtain them.Then,assuming that travelers only focus on their past travel experience,a day-to-day traffic assignment model is established based on reinforcement learning and bounded rationality.In the proposed model,the Bush-Mosteller model,a reinforcement learning model,is modified to calculate path choice probability according to bounded rationality.The modified model updates the path choice probability only if the gap between expected travel time and perceived travel time is beyond the cognitive threshold.Numerical experiments validate the effectiveness of the model and show that traffic flows can converge to the equilibrium in any case of cognitive thresholds and penetration rates of battery electric vehicles.The cognitive threshold has a positive influence on the variation of traffic flows while it has a negative influence on the differences between traffic flows.The adaptation of battery electric vehicles leads to the poor performance of the traffic system.展开更多
The A＇Prune quality of service （QoS） routing algorithm was proposed to compute K-shortest paths satisfying multiple QoS constraints, The A＇Prune is considered to be one of the practical routing algorithms for inte...The A＇Prune quality of service （QoS） routing algorithm was proposed to compute K-shortest paths satisfying multiple QoS constraints, The A＇Prune is considered to be one of the practical routing algorithms for intelligent optical networks because of its flexibility in handling many practical constraints, This article gives detailed performance studies of the algorithm through extensive simulation experiments. We found that both the running time and the memory space requirements of the algorithm are large, especially when the network size increases, in this article, we also propose an approach to improving the performance of the A＇Prune algorithm. The improvements should make the A＇Prune algorithm more attractive for practical use in intelligent optical networks.展开更多
基金The National Natural Science Foundation of China(No.51478110)Postgraduate Research & Practice Innovation Program of Jiangsu Province(No.KYCX18_0139)
文摘In order to investigate the effect of the use of battery electric vehicles on traffic dynamics,the valid paths of electric battery vehicles are defined and a check-based method is proposed to obtain them.Then,assuming that travelers only focus on their past travel experience,a day-to-day traffic assignment model is established based on reinforcement learning and bounded rationality.In the proposed model,the Bush-Mosteller model,a reinforcement learning model,is modified to calculate path choice probability according to bounded rationality.The modified model updates the path choice probability only if the gap between expected travel time and perceived travel time is beyond the cognitive threshold.Numerical experiments validate the effectiveness of the model and show that traffic flows can converge to the equilibrium in any case of cognitive thresholds and penetration rates of battery electric vehicles.The cognitive threshold has a positive influence on the variation of traffic flows while it has a negative influence on the differences between traffic flows.The adaptation of battery electric vehicles leads to the poor performance of the traffic system.
文摘The A＇Prune quality of service （QoS） routing algorithm was proposed to compute K-shortest paths satisfying multiple QoS constraints, The A＇Prune is considered to be one of the practical routing algorithms for intelligent optical networks because of its flexibility in handling many practical constraints, This article gives detailed performance studies of the algorithm through extensive simulation experiments. We found that both the running time and the memory space requirements of the algorithm are large, especially when the network size increases, in this article, we also propose an approach to improving the performance of the A＇Prune algorithm. The improvements should make the A＇Prune algorithm more attractive for practical use in intelligent optical networks.
