Andrei Iu. Bejan

Statistical modelling and analysis of sparse bus probe data in urban areas

Congestion in urban areas causes financial loss to business and increased use of energy compared with free-flowing traffic. Providing citizens with accurate information on traffic conditions can encourage journeys at times of low congestion and uptake of public transport. Installing the measurement infrastructure in a city to provide this information is expensive and potentially invades privacy. Increasingly, public transport vehicles are equipped with sensors to provide real-time arrival time estimates, but these data are sparse. Our work shows how these data can be used to estimate journey times experienced by road users generally. In this paper we describe (i) what a typical data set from a fleet of over 100 buses looks like; (ii) describe an algorithm to extract bus journeys and estimate their duration along a single route; (iii) show how to visualise journey times and the influence of contextual factors; (iv) validate our approach for recovering speed information from the sparse movement data.

Evaluation of velocity fields via sparse bus probe data in urban areas

The goal of this paper is to investigate how sparse public transport data such as bus data might be used in evaluating congestion in urban areas and in providing better information for road users and traffic managers. With this aim we use the well known concept of a velocity field and, building on our previous work, we specifically show how velocity fields can be usefully reconstructed from sparse bus data. Buses provide good coverage of cities and, increasingly, are being equipped with satellite navigation devices and monitored in order to display their predicted arrival times to bus passengers. We have had access to a wealth of bus data for the city of Cambridge, England for some years. We describe these bus data and show how its specifics may be used in conjunction with other sources of data, such as OpenStreetMap, in order to reconstruct information on transport traffic flow dynamics. Finally, we consider examples in which we compare our knowledge of the traffic regimes with the outputs of the technique presented.

Fisher Information-based Experiment Design for Network Tomography

Network tomography aims to infer the individual performance of networked elements (e.g., links) using aggregate measurements on end-to-end paths. Previous work on network tomography focuses primarily on developing estimators using the given measurements, while the design of measurements is often neglected. We fill this gap by proposing a framework to design probing experiments with focus on probe allocation, and applying it to two concrete problems: packet loss tomography and packet delay variation (PDV) tomography. Based on the Fisher Information Matrix (FIM), we design the distribution of probes across paths to maximize the best accuracy of unbiased estimators, asymptotically achievable by the maximum likelihood estimator. We consider two widely-adopted objective functions: determinant of the inverse FIM (D-optimality) and trace of the inverse FIM (A-optimality). We also extend the A-optimal criterion to incorporate heterogeneity in link weights. Under certain conditions on the FIM, satisfied by both loss and PDV tomography, we derive explicit expressions for both objective functions. When the number of probing paths equals the number of links, these lead to closed-form solutions for the optimal design; when there are more paths, we develop a heuristic to select a subset of paths and optimally allocate probes within the subset. Observing the dependency of the optimal design on unknown parameters, we further propose an algorithm that iteratively updates the design based on parameter estimates, which converges to the design based on true parameters as the number of probes increases. Using packet-level simulations on real datasets, we verify that the proposed design effectively reduces estimation error compared with the common approach of uniformly distributing probes.

Efficient multicast in hybrid wireless networks

The increasing popularity of smartphones and other similar multi-modal wireless devices has created an opportunity for the realization of large-scale hybrid (or heterogeneous) networks. Typically, modern mobile devices are likely to support a short range communication interface (e.g. IEEE 802.11/WiFi) and/or a longer range communication interface (e.g. cellular data link wireless technology). Multi-hop wireless networking over WiFi can help to extend the range of cellular networks in low SINR regions as well as to alleviate network congestion. Conversely, equipping a few nodes in a mobile ad hoc network (MANET) with cellular radios can help to heal wireless network partitions and, thus, to improve the overall network connectivity. One can envision large scale group communication (or multicast) applications including real-time video conferencing (e.g., iPhone FaceTime), P2P video and file sharing, and “voice call groups” in disaster relief and military hybrid networks. In this paper, the problem of resource-efficient multicast in hybrid wireless networks which include both point-to-point (cellular) and broadcast (MANET) links is considered. The underlying optimization problem is a hybrid of two well-known NP-hard graph optimization problems-the Minimum Steiner Tree problem (for point-to-point links) and the Minimum Steiner Connected Dominating Set problem (for broadcast links). We consider both edge- and node-weighted versions of this problem and use distinctly different methodologies to give three algorithms with guaranteed approximation factors. We further demonstrate by means of simulation modeling of standard deployment scenarios that while one algorithm outperforms another in terms of the tree cost, the latter outperforms the former in terms of complexity and other practical considerations.

A performance analysis study of multipath routing in a hybrid network with mobile users

Mobile communication platforms of individual agents and ground vehicles, ships and aircrafts of both civil and military services often operate in highly dynamic conditions with constantly changing infrastructure and access to communication resources. Efficient techniques for rapid and yet stable communication of such fleets with their control centres and between cooperating vehicles within the fleet is a challenging but important area of study with the potential to facilitate the analysis and design of efficient and robust communication systems. Multipath extensions of data transmission protocols aim to take advantage of path diversity to achieve efficient and robust bandwidth allocation while maintaining stability. Such multipath resource pooling extensions of routing and congestion control intrinsically implement decentralisation with implicit resource sharing. In this paper, we build on the recent theoretical work on fluid model approximations of multipath TCP and study their application to the scenarios in which a convoy with two communication nodes (representing the convoys head and tail) establishes channels with a set of radio/WiFi towers and a satellite relaying information to a remote destination; these channels have time-varying capacities which depend on the position and dynamics of the convoy. The paper studies the performance of a multipath TCP controller and demonstrates how path diversity can be implicitly utilised to spread flows across available paths. Furthermore, we study the patterns of sub-flows governed by dynamic control according to the motion of the convoy and investigate the trade-offs between resource utilisation and the speed of response by the sub-flows.

Capacity bounds and robustness in multipath networks

The recent developments of multipath data transport protocols such as Multipath TCP allow end-systems to explore and share available resources within networks. Through dynamic load balancing over subflows these protocols ensure high levels of robustness to network failures and traffic overloads. In this paper we use fluid models to study the benefits that accrue when load is shared across subflows. We combine insights gained from the fluid models with a precise description of the capacity region for the network and show that our models of multipath protocols approach the boundary of the capacity region as the intensity of the offered traffic approaches a critical value. We quantify the extent to which multipath protocols will make a network robust to unforeseen traffic mismatches and link failures and illustrate our results with parameterised models for random fluctuations in the offered traffic.

Multicast vs. unicast for loss tomography on tree topologies

Loss tomography using multicast measurements and using unicast measurements have been investigated separately. In this paper we compare the performance of the two methods on tree structures. We prove identifiability of unicast measurements on tree structures with no degree-2 nodes. To theoretically compare multicast and unicast, we develop an observation model for multicast on trees and derive expressions for calculating the Fisher Information Matrix. We apply optimal experiment design for unicast on trees and develop a simple and insightful solution. Using a packet level simulator, we evaluated and compared the per-link MSE of multicast and unicast under varying parameter settings including link weights, link success rates and tree size. The results show that in contrast to the general belief that multicast always outperforms unicast, unicast can outperform multicast under tight constraint on the probing budget, especially in terms of a weighted average of per-link MSEs. On the other hand, multicast achieves more consistent performance with respect to varying link success rates or tree size.

Application of inflow control devices to heterogeneous reservoirs

The rate of inflow to a long well can vary along its completion length, e.g. due to frictional pressure losses or reservoir heterogeneity. These variations often negatively affect the oil sweep efficiency and the ultimate oil recovery. Inflow Control Devices (ICDs) represent a mature well completion technology which provides uniformity of the inflow profile by restricting high specific inflow segments while increasing inflow from low productivity segments. This paper introduces a mathematical model for effective reduction of the inflow imbalance caused by reservoir heterogeneity. The model addresses one of the key aspects of the ICD technology application - the trade-off between well productivity and inflow equalisation. Our analytical model relates the specific inflow rate and specific productivity index to well characteristics taking into account the intrinsically stochastic nature of reservoir properties along the well completion interval. A general solution to our model is available in a non-closed, analytical form. We have derived a closed form solution for some particular cases. The practical utility of the model is illustrated by considering a case study with prolific and medium productivity reservoirs. Finally, we identify limitations in using our model.