Comparison of stochastic methods for control in air traffic management

This paper provides a direct comparison of two stochastic optimisation techniques (Markov Chain Monte Carlo and Sequential Monte Carlo) when applied to the problem of conflict resolution and aircraft trajectory control in air traffic management. The two methods are then also compared to another existing technique of Mixed-Integer Linear Programming which is also popular in distributed control. © 2011 IFAC.

Effects of longer heavy vehicles on traffic congestion

Two-lane, "microscopic" (vehicle-by-vehicle) simulations of motorway traffic are developed using existing models and validated using measured data from the M25 motorway. An energy consumption model is also built in, which takes the logged trajectories of simulated vehicles as drive-cycles. The simulations are used to investigate the effects on motorway congestion and fuel consumption if "longer and/or heavier vehicles" (LHVs) were to be permitted in the UK. Baseline scenarios are simulated with traffic composed of cars, light goods vehicles and standard heavy goods vehicles (HGVs). A proportion of conventional articulated HGVs is then replaced by a smaller number of LHVs carrying the same total payload mass and volume. Four LHV configurations are investigated: an 18.75 m, 46 t longer semi-trailer (LST); 25.25 m, 50 t and 60 t B-doubles and a 34 m, 82 t A-double. Metrics for congestion, freight fleet energy consumption and car energy consumption are defined for comparing the scenarios. Finally, variation of take-up level and LHV engine power for the LST and A-double are investigated. It is concluded that: (a) LHVs should reduce congestion particularly in dense traffic, however, a low mean proportion of freight traffic on UK roads and low take-up levels will limit this effect to be almost negligible; (b) LHVs can significantly improve the energy efficiency of freight fleets, giving up to a 23% reduction in fleet energy consumption at high take-up levels; (c) the small reduction in congestion caused by LHVs could improve the fuel consumption of other road users by up to 3% in dense traffic, however in free-flowing traffic an opposite effect occurs due to higher vehicle speeds and aerodynamic losses; and (d) underpowered LHVs have potential to generate severe congestion, however current manufacturers' recommendations appear suitable. © 2013 IMechE.

A Novel Architecture of Vision Chip for Fast Traffic Lane Detection and FPGA Implementation

This paper presents a novel architecture of vision chip for fast traffic lane detection (FTLD). The architecture consists of a 32*32 SIMD processing element (PE) array processor and a dual-core RISC processor. The PE array processor performs low-level pixel-parallel image processing at high speed and outputs image features for high-level image processing without I/O bottleneck. The dual-core processor carries out high-level image processing. A parallel fast lane detection algorithm for this architecture is developed. The FPGA system with a CMOS image sensor is used to implement the architecture. Experiment results show that the system can perform the fast traffic lane detection at 50fps rate. It is much faster than previous works and has good robustness that can operate in various intensity of light. The novel architecture of vision chip is able to meet the demand of real-time lane departure warning system.

adaptive location update mechanism for network-constrained moving objects in changeful traffic conditions

Natl Chiao Tung Univ, Dept Comp Sci

On the Convergence of Statistical Techniques for Inferring Network Traffic Demands

Accurate knowledge of traffic demands in a communication network enables or enhances a variety of traffic engineering and network management tasks of paramount importance for operational networks. Directly measuring a complete set of these demands is prohibitively expensive because of the huge amounts of data that must be collected and the performance impact that such measurements would impose on the regular behavior of the network. As a consequence, we must rely on statistical techniques to produce estimates of actual traffic demands from partial information. The performance of such techniques is however limited due to their reliance on limited information and the high amount of computations they incur, which limits their convergence behavior. In this paper we study strategies to improve the convergence of a powerful statistical technique based on an Expectation-Maximization iterative algorithm. First we analyze modeling approaches to generating starting points. We call these starting points informed priors since they are obtained using actual network information such as packet traces and SNMP link counts. Second we provide a very fast variant of the EM algorithm which extends its computation range, increasing its accuracy and decreasing its dependence on the quality of the starting point. Finally, we study the convergence characteristics of our EM algorithm and compare it against a recently proposed Weighted Least Squares approach.

Structural Analysis of Network Traffic Flows

Network traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a thorough understanding of OD flows is essential for modeling network traffic, and for addressing a wide variety of problems including traffic engineering, traffic matrix estimation, capacity planning, forecasting and anomaly detection. However, to date, OD flows have not been closely studied, and there is very little known about their properties. We present the first analysis of complete sets of OD flow timeseries, taken from two different backbone networks (Abilene and Sprint-Europe). Using Principal Component Analysis (PCA), we find that the set of OD flows has small intrinsic dimension. In fact, even in a network with over a hundred OD flows, these flows can be accurately modeled in time using a small number (10 or less) of independent components or dimensions. We also show how to use PCA to systematically decompose the structure of OD flow timeseries into three main constituents: common periodic trends, short-lived bursts, and noise. We provide insight into how the various constituents contribute to the overall structure of OD flows and explore the extent to which this decomposition varies over time.