Congestion avoidance for recharging electric vehicles using smoothed particle hydrodynamics

In this paper, a novel approach for recharging electric vehicles (EVs) is proposed based on managing multiple discrete units of electric power flow, named energy demand particles (EDPs). Key similarities between EDPs and fluid particles (FPs) are established that allow the use of a smoothed particle hydrodynamics (SPH) method for scheduling the recharging times of EVs. It is shown, via simulation, that the scheduling procedure not only minimizes the variance of voltage drops in the secondary circuits, but it also can be used to implement a dynamic demand response and frequency control mechanism. The performance of the proposed scheduling procedure is also compared with alternative approaches recently published in the literature

Spatial inference of traffic transition using micro-macro traffic variables

This paper proposes an online traffic inference algorithm for road segments in which local traffic information cannot be directly observed. Using macro-micro traffic variables as inputs, the algorithm consists of three main operations. First, it uses interarrival time (time headway) statistics from upstream and downstream locations to spatially infer traffic transitions at an unsupervised piece of segment. Second, it estimates lane-level flow and occupancy at the same unsupervised target site. Third, it estimates individual lane-level shockwave propagation times on the segment. Using real-world closed-circuit television data, it is shown that the proposed algorithm outperforms previously proposed methods in the literature

Proportional differentiated admission control

Published versio

Anomaly Detection using Microscopic Traffic Variables on Freeway Segments

This paper proposes and assesses the effectiveness of monitoring vehicular traffic anomalies using microscopic traffic variables, namely relative speed and inter-vehicle spacing. We present an algorithm that detects transient changes in traffic patterns using microscopic traffic variables. In particular, we show that when applied to real-world scenarios, our algorithm can use the variance of statistics of relative speed to detect traffic anomalies and precursors to non-recurring traffic congestion. The performance of the proposed algorithm is also assessed using a microscopic traffic simulation environment, where we show that with minimum prior knowledge, the proposed algorithm has comparable performance to an ideally placed loop detector monitoring the standard deviation of speed. The algorithm also performs very well even when the microscopic traffic variables are available only from a fraction of the complete population of vehicles.Accepted versio

Utility-based scheduling disciplines for adaptive applications over the Internet

Published versio

Income maximisation using prices and QoS for multi-class telecommunication system

Accepted versio

Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction

Published versio

Structural generative descriptions for time series classification

In this paper, we formulate a novel time series representation framework that captures the inherent data dependency of time series and that can be easily incorporated into existing statistical classification algorithms. The impact of the proposed data representation stage in the solution to the generic underlying problem of time series classification is investigated. The proposed framework, which we call structural generative descriptions moves the structural time series representation to the probability domain, and hence is able to combine statistical and structural pattern recognition paradigms in a novel fashion. Two algorithm instantiations based on the proposed framework are developed. The algorithms are tested and compared using different publicly available real-world benchmark data. Results reported in this paper show the potential of the proposed representation framework, which in the experiments investigated, performs better or comparable to state-of-the-art time series description techniques