Building smart transportation services in urban cities has become a worldwide problem owing to the rapidly increasing global population and the development of Internet-of-Things applications. Traffic congestion and en...
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Building smart transportation services in urban cities has become a worldwide problem owing to the rapidly increasing global population and the development of Internet-of-Things applications. Traffic congestion and environmental concerns can be alleviated by sharing mobility, which reduces the number of vehicles on the road network. The taxi-parcel sharing problem has been considered as an efficient planning model for people and goods flows. In this paper, we enhance the functionality of a current people-parcel taxi sharing model. The adapted model analyzes the historical request data and predicts the current service demands. We then propose two novel online routing algorithms that construct optimal routes in real-time. The objectives are to maximize (as far as possible) both the parcel delivery requests and ride requests while minimizing the idle time and travel distance of the taxis. The proposed online routing algorithms are evaluated on instances adapted from real Cabspotting datasets. After implementing our routingalgorithms, the total idle travel distance per day was 9.64% to 12.76% lower than that of the existing taxi-parcel sharing method. Our online routing algorithms can be incorporated into an efficient smart shared taxi system.
作者：
Bauer, DIBM Corp
Zurich Res Lab Res CH-8803 Ruschlikon Switzerland
A neck on-line routingalgorithm based on the notion of minimum-interference is presented. The algorithm maximises the sum of residual flows of ingress-egress pairs. using a simple heuristic method. It achieves good r...
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A neck on-line routingalgorithm based on the notion of minimum-interference is presented. The algorithm maximises the sum of residual flows of ingress-egress pairs. using a simple heuristic method. It achieves good results in terms of total bandwidth routed.
In this paper, we propose a dynamic secure routing game framework to effectively combat jamming attacks in distributed cognitive radio networks. We first propose a stochastic multi-stage zero-sum game framework based ...
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ISBN:
(纸本)9781424492688
In this paper, we propose a dynamic secure routing game framework to effectively combat jamming attacks in distributed cognitive radio networks. We first propose a stochastic multi-stage zero-sum game framework based on the directional exploration of ad hoc on-demand distance vector (AODV) algorithms. The zero-sum game captures the conflicting goals between malicious attackers and honest nodes and considers packet error probability and delay as performance metrics. The game-theoretic routing protocol guarantees a performance level given by the value of the game. Distributed Boltzmann-Gibbs learning is used for an on-line routingalgorithm, in which the users do not have the knowledge of the attackers and the utility function. Instead, the users learn the payoffs based on their past observations. We use simulations to illustrate the proposed routing mechanism and compare the algorithm with fictitious-play learning. Unlike typical distributed routingalgorithms such as AODV routing, the proposed secure routingalgorithm supports a novel recovery of routing path failure against unknown attackers.
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