Active control for adaptive sound zones in passenger train compartments

The acoustics in train compartments is an important part of the comfort when travelling. To improve the acoustics, active noise control (ANC) can be used to create local quiet zones at the passenger ear position. In this thesis the fundamental ANC theory is explained and the possibilities and limitations with the technique are identified. An ANC system which uses the virtual microphone technique is implemented and quiet zones are realized at a chair in a train compartment studio. The performance of the ANC system is evaluated with a Styrofoam head model with built in microphones that is used to produce contour plots of the quiet zone shape and attenuation level. The results show that the 10 dB quiet zone is relatively large covering more than a square decimeter in low frequencies and decreases with frequency. Head movements effects are also evaluated and the results show that the system is very sensitive to head movements, especially in higher frequencies. In order to find an optimal positioning of the system components, several experiments have been made. From these it is concluded that varying noise incidence is problematic to some extent. Ways of handling the problems are presented and if the problems were properly dealt with ANC could be an effective way of reducing noise in passenger train compartments

Maintaining a distributed symbiotic relationship using delegate MultiAgent systems

Online simulation of traffic can assist route guidance systems by predicting problems such as congestion. Accurate predictions require accurate status information about vehicles - the fact that the vehicles are distributed over large-scale road infrastructure makes this particularly challenging. Embedding the online simulation in the road infrastructure - by distributing it across road side computing infrastructure - is a partial solution, but also adds additional complexity to the symbiotic relationship between online simulation and physical system. In this paper we describe an approach that uses delegate MultiAgent Systems to reduce the complexity of such symbiotic relationships. Experimental results in a prototype implementation of the route guidance mechanisms show that the approach is feasible and leads to a proactive route guidance mechanisms with the potential of outperforming current state of the practice non-proactive routing mechanisms.status: publishe

Weighing communication overhead against travel time reduction in advanced traffic information systems

In this paper we develop a multi-agent based traffic simulator by considering traffic flows as emergent phenomena over two layered multi-agent systems. The first layer is composed of vehicle agents and the second layer is composed of signal agents. The system can naturally express actual road networks and can reproduce various traffic congestions induced by means of local interactions among vehicle agents. We show the system is useful for the road enhancing problem to guess the most suitable place when a new road is constructed. Further, we study a traffic control system which dissolves congestions at intersections by monitoring the traffic flow at the first layer.status: publishe

Multi-model traffic microsimulations

Microscopic simulation of traffic, while often necessary to capture the effects of interest, is a computationally expensive simulation strategy. What can be observed, however, is that the accuracy required from the simulation for post simulation analysis can depend on the simulated world and vary over simulated time - roads becoming crowded may be simulated differently than sparsely used roads. In this paper we explore multi-model simulation as an adaptive simulation strategy. Multi-model simulations are capable of selecting and switching to a suitable simulation model at runtime, based on the state of the simulated world. This simulation strategy reduces the computational complexity of traffic microsimulations while still maintaining the desired level of accuracy needed to produce meaningful results. We illustrate the approach via an experimental set-up that allows switching between a road queue model and a fully detailed road simulation model.status: publishe

A decentralized approach for anticipatory vehicle routing using delegate multiagent systems

Advanced vehicle guidance systems use real-time traffic information to route traffic and to avoid congestion. Unfortunately, these systems can only react upon the presence of traffic jams and not to prevent the creation of unnecessary congestion. Anticipatory vehicle routing is promising in that respect, because this approach allows directing vehicle routing by accounting for traffic forecast information. This paper presents a decentralized approach for anticipatory vehicle routing that is particularly useful in large-scale dynamic environments. The approach is based on delegate multiagent systems, i.e., an environment-centric coordination mechanism that is, in part, inspired by ant behavior. Antlike agents explore the environment on behalf of vehicles and detect a congestion forecast, allowing vehicles to reroute. The approach is explained in depth and is evaluated by comparison with three alternative routing strategies. The experiments are done in simulation of a real-world traffic environment. The experiments indicate a considerable performance gain compared with the most advanced strategy under test, i.e., a traffic-message-channel-based routing strategy.status: publishe

Coordination in hierarchical pickup and delivery problems using delegate multi-agent systems

Pickup and delivery problems are a generalization of the planning problem faced by transport companies. Large logistics providers often employ a hierarchical ‘hub and spoke’ overlay network to connect pickup and drop off sites. Hierar- chical pickup and delivery problems pose two major challenges: (1) determining suitable routes from the origin to the destination of packages through the logistics providers’ network, and (2) allocating the resources that are required for pickup, transporting and delivering the packages along such a route. By combining traditional resource allocation techniques with swarm algorithms, the approach in this paper offers a decen- tralized solution to pickup and delivery problems in hierarchical environments. Resources are scheduled locally at the node from which they operate, resulting in a distribution of many local resource schedules. A swarm approach called delegate multi- agent systems is used to extract information from relevant localized schedules and combine them in consistent global paths. The ant-like agents in these delegate multi-agent systems also redistribute feedback from the path planning mechanism to the decentralized resource scheduling mechanism. Results obtained by this hybrid approach show that it outperforms greedy and static alternatives.status: publishe

Anticipatory coordination of electric vehicle allocation to fast charging infrastructure

The limited range of electric vehicles (EVs) in combination with the limited capacity of current fast charging infrastructure are both causes for a limited adoption of EVs. In order to reduce the general inconvenience that EV users experience when having to wait for available fast charging stations and to lessen the danger of damaging the infra- structure by overloading it, an efficient coordination strategy is needed. This paper proposes an anticipatory, decentralised coordination strategy for on-route charging of EVs during lengthy trips in a fast-charging infra- structure. This strategy is compared to a reference strategy that uses global real-time knowledge of charging station occupation. Simulation results using a realistic scenario with real-world traffic data demonstrate that the anticipatory strategy is able to reduce the waiting times for EV users by up to 50% while at the same time decreasing the peak loads of the electricity grid caused by charging EVs by 21%. The limited range of electric vehicles (EVs) in combination with the limited capacity of current fast charging infrastructure are both causes for a limited adoption of EVs. In order to reduce the general inconvenience that EV users experience when having to wait for available fast charging stations and to lessen the danger of damaging the infra- structure by overloading it, an efficient coordination strategy is needed. This paper proposes an anticipatory, decentralised coordination strategy for on-route charging of EVs during lengthy trips in a fast-charging infra- structure. This strategy is compared to a reference strategy that uses global real-time knowledge of charging station occupation. Simulation results using a realistic scenario with real-world traffic data demonstrate that the anticipatory strategy is able to reduce the waiting times for EV users by up to 50% while at the same time decreasing the peak loads of the electricity grid caused by charging EVs by 21%.status: publishe

Anticipatory Coordination of Electric Vehicle Allocation to Fast Charging Infrastructure

The limited range of electric vehicles (EVs) in combination with the limited capacity of current fast charging infrastructure are both causes for a limited adoption of EVs. In order to reduce the general inconvenience that EV users experience when having to wait for available fast charging stations and to lessen the danger of damaging the infra- structure by overloading it, an efficient coordination strategy is needed. This paper proposes an anticipatory, decentralised coordination strategy for on-route charging of EVs during lengthy trips in a fast-charging infra- structure. This strategy is compared to a reference strategy that uses global real-time knowledge of charging station occupation. Simulation results using a realistic scenario with real-world traffic data demonstrate that the anticipatory strategy is able to reduce the waiting times for EV users by up to 50% while at the same time decreasing the peak loads of the electricity grid caused by charging EVs by 21%. The limited range of electric vehicles (EVs) in combination with the limited capacity of current fast charging infrastructure are both causes for a limited adoption of EVs. In order to reduce the general inconvenience that EV users experience when having to wait for available fast charging stations and to lessen the danger of damaging the infra- structure by overloading it, an efficient coordination strategy is needed. This paper proposes an anticipatory, decentralised coordination strategy for on-route charging of EVs during lengthy trips in a fast-charging infra- structure. This strategy is compared to a reference strategy that uses global real-time knowledge of charging station occupation. Simulation results using a realistic scenario with real-world traffic data demonstrate that the anticipatory strategy is able to reduce the waiting times for EV users by up to 50% while at the same time decreasing the peak loads of the electricity grid caused by charging EVs by 21%. ispartof: pages:74-85 ispartof: Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection vol:8473 pages:74-85 ispartof: PAAMS'14 location:Salamanca (Spain) date:4 Jun - 6 Jun 2014 status: publishe