Assessing the Impact of Technology on Aircraft Systems using Technology Impact Forecasting

In today’s atmosphere of constrained defense spending and reduced research budgets, determining how to allocate resources for research and design has become a critical and challenging task. In the area of aircraft design there are many promising technologies to be explored, yet limited funds with which to explore them. In addition, issues concerning uncertainty in technology readiness as well as the quantification of the impact of a technology (or combinations of technologies), are of key importance during the design process. This paper presents a methodology that details a comprehensive and structured process in which to quantitatively explore the effects of technology for a given baseline aircraft. This process, called Technology Impact Forecasting (TIF), involves the creation of a assessment environment for use in conjunction with defined technology scenarios, and will have a significant impact on resource allocation strategies for defense acquisition. The advantages and limitations of the method are discussed. In addition, an example TIF application, that of an Uninhabited Combat Aerial Vehicle, is presented and serves to illustrate the applicability of this methodology to a military system.

Modelling road traffic accidents using macroscopic second-order models of traffic flow

In this paper, we introduce a macroscopic model for road traffic accidents along highway sections. We discuss the motivation and the derivation of such a model, and we present its mathematical properties. The results are presented by means of examples where a section of a crowded one-way highway contains in the middle a cluster of drivers whose dynamics are prone to road traffic accidents. We discuss the coupling conditions and present some existence results of weak solutions to the associated Riemann Problems. Furthermore, we illustrate some features of the proposed model through some numerical simulations. © The authors 2012.

Road traffic congestion and public information - An experimental investigation

This paper reports laboratory experiments designed to study the impact of public information about past departure rates on congestion levels and travel costs. Our design is based on a discrete version of Arnott et al.'s (1990) bottleneck model. In all treatments, congestion occurs and the observed travel costs are quite similar to the predicted ones. Subjects' capacity to coordinate is not affected by the availability of public information on past departure rates, by the number of drivers or by the relative cost of delay. This seemingly absence of treatment effects is confirmed by our finding that a parameter-free reinforcement learning model best characterises individual behaviour.

Novel Radio Link Buffer Management Schemes for End-User Multi-class Traffic in High Speed Packet Access Networks

The requirement to provide multimedia services with QoS support in mobile networks has led to standardization and deployment of high speed data access technologies such as the High Speed Downlink Packet Access (HSDPA) system. HSDPA improves downlink packet data and multimedia services support in WCDMA-based cellular networks. As is the trend in emerging wireless access technologies, HSDPA supports end-user multi-class sessions comprising parallel flows with diverse Quality of Service (QoS) requirements, such as real-time (RT) voice or video streaming concurrent with non real-time (NRT) data service being transmitted to the same user, with differentiated queuing at the radio link interface. Hence, in this paper we present and evaluate novel radio link buffer management schemes for QoS control of multimedia traffic comprising concurrent RT and NRT flows in the same HSDPA end-user session. The new buffer management schemes—Enhanced Time Space Priority (E-TSP) and Dynamic Time Space Priority (D-TSP)—are designed to improve radio link and network resource utilization as well as optimize end-to-end QoS performance of both RT and NRT flows in the end-user session. Both schemes are based on a Time-Space Priority (TSP) queuing system, which provides joint delay and loss differentiation between the flows by queuing (partially) loss tolerant RT flow packets for higher transmission priority but with restricted access to the buffer space, whilst allowing unlimited access to the buffer space for delay-tolerant NRT flow but with queuing for lower transmission priority. Experiments by means of extensive system-level HSDPA simulations demonstrates that with the proposed TSP-based radio link buffer management schemes, significant end-to-end QoS performance gains accrue to end-user traffic with simultaneous RT and NRT flows, in addition to improved resource utilization in the radio access network.

End-to-End QoS Enhancement ofHSDPA End-User Multi-flow Traffic Using RAN Buffer Management

High speed downlink packet access (HSDPA) was introduced to UMTS radio access segment to provide higher capacity for new packet switched services. As a result, packet switched sessions with multiple diverse traffic flows such as concurrent voice and data, or video and data being transmitted to the same user are a likely commonplace cellular packet data scenario. In HSDPA, radio access network (RAN) buffer management schemes are essential to support the end-to-end QoS of such sessions. Hence in this paper we present the end-to-end performance study of a proposed RAN buffer management scheme for multi-flow sessions via dynamic system-level HSDPA simulations. The scheme is an enhancement of a time-space priority (TSP) queuing strategy applied to the node B MAC-hs buffer allocated to an end user with concurrent real-time (RT) and non-real-time (NRT) flows during a multi-flow session. The experimental multi- flow scenario is a packet voice call with concurrent TCP-based file download to the same user. Results show that with the proposed enhancements to the TSP-based RAN buffer management, end-to-end QoS performance gains accrue to the NRT flow without compromising RT flow QoS of the same end user session

Stated choices and benefit estimates in the context of traffic calming schemes:Utility maximization, regret minimization, or both?

This paper proposes a discrete mixture model which assigns individuals, up to a probability, to either a class of random utility (RU) maximizers or a class of random regret (RR) minimizers, on the basis of their sequence of observed choices. Our proposed model advances the state of the art of RU-RR mixture models by (i) adding and simultaneously estimating a membership model which predicts the probability of belonging to a RU or RR class; (ii) adding a layer of random taste heterogeneity within each behavioural class; and (iii) deriving a welfare measure associated with the RU-RR mixture model and consistent with referendum-voting, which is the adequate mechanism of provision for such local public goods. The context of our empirical application is a stated choice experiment concerning traffic calming schemes. We find that the random parameter RU-RR mixture model not only outperforms its fixed coefficient counterpart in terms of fit-as expected-but also in terms of plausibility of membership determinants of behavioural class. In line with psychological theories of regret, we find that, compared to respondents who are familiar with the choice context (i.e. the traffic calming scheme), unfamiliar respondents are more likely to be regret minimizers than utility maximizers. © 2014 Elsevier Ltd.