Location estimation in a 3D environment using radio frequency identification tags

RFID tag location estimation in a 3D environment is investigated. The location of the tag with unknown coordinates can be estimated with certain accuracy. However, accuracy can be improved using the knowledge based on measurement of additional reference tags with known location. This thesis studies the mathematical formulation and practical realization of location sensing using RFID tags. Deviating from the standard use of RFID technology which employs one tag reader to identify the presence of tag, here multiple tag readers with known location are used to estimate the physical location of an individual tag, with/without the help of few reference tags with known locations. Mathematical model of this concept has been developed based on distance variations in terms of signal strength. Experimental approach with limited range passive tags has been carried out. Since the range of the RFID system was limited only to a few inches, signal strength variations were insignificant. Instead, time domain measurements with the help of an external antenna were conducted. The composite signal width including of the wake up signal of the interrogator, travel time between the interrogator and tag, and the tag\u27s response was measured and quantified. It was observed that the width of the signal was proportional to the distance between the tag reader and the tag. It was noticed that the use of four RFID tag readers yielded fairly accurate results to identify the location the tag based on the mathematical formulation developed here. Additionally, concept of trilateration has also been extended for tracking the tag of unknown location without the use of reference tags. Archival data set corresponding to all tag location due to four different tag readers was compiled. The unknown tag was probed with four tag readers and matching the data to the archival data set yielded unique and accurate results for its unknown location. It was demonstrated that both approaches were proved to be cost-effective techniques and estimation of the location of a specific tag has been achieved with sufficient accuracy

Incorporating General Incident Knowledge into Automatic Incident Detection: A Markov Logic Network Method

Automatic incident detection (AID) algorithms have been studied for more than 50 years. However, due to the development in some competing technologies such as cell phone call based detection, video detection, the importance of AID in traffic management has been decreasing over the years. In response to such trend, AID researchers introduced new universal and transferability requirements in addition to the traditional performance measures. Based on these requirements, the recent effort of AID research has been focused on applying new artificial intelligence (AI) models into incident detection and significant performance improvement has been observed comparing to earlier models. To fully address the new requirements, the existing AI models still have some limitations including 1) the black-box characteristics, 2) the overfitting issue, and 3) the requirement for clean, large, and accurate training data. Recently, Bayesian network (BN) based AID algorithm showed promising potentials in partially overcoming the above limitations with its open structure and explicit stochastic interpretation of incident knowledge. But BN still has its limitations such as the enforced cause-effect relationship among BN nodes and its Bayesian type of logic inference. In 2006, another more advanced statistical inference network, Markov Logic Network (MLN), was proposed in computer science, which can effectively overcome some limitations of BN and also bring the flexibility of applying various knowledge. In this study, an MLN-based AID algorithm is proposed. The proposed algorithm can interpret general types of traffic flow knowledge, not necessarily causality relationships. Meanwhile, a calibration method is also proposed to effective train the MLN. The algorithm is evaluated based on field data, collected at I-894 corridor in Milwaukee, WI. The results indicate promising potentials of the application of MLN in incident detection

Modification de la situation par simulation afin d'améliorer les déplacements dans un cadre tactique

Depuis la fin du 20e siècle, les villes sont de plus en plus le théâtre d'opérations militaires. Les milieux urbains étant des environnements extrêmement complexes, le commandement et contrôle y est particulièrement difficile. Un simple déplacement peut devenir problématique étant donné le caractère dynamique de l'environnement. Un trajet prévu initialement peut s'avérer impossible à compléter compte tenu de nouvelles menaces détectées pendant le déplacement. La planification d'un nouveau chemin sécuritaire étant longue à accomplir par un opérateur, l'aide d'un ordinateur serait souhaitable. C'est sur cette problématique que se penche notre travail. Plus particulièrement, il sera question de planification d'actions améliorant un déplacement. Deux actions seront considérées pour améliorer le déplacement d'un véhicule: l'édification d'un barrage routier et l'interception d'un véhicule menaçant. La première devant nécessairement être simulée pour en déterminer les effets, un simulateur de barrage routier a été développé dans ce travail. L'ensemble du planificateur présenté dans ce mémoire prend la forme d'un système multiagent. Les agents composant celui-ci sont les différentes ressources sur le terrain (véhicules de police, forces alliées...), ainsi que le véhicule dont le déplacement est à améliorer. Un prototype du planificateur a été développé dans ce mémoire. Dans le but de valider la simulation et la planification résultantes de ce prototype, un graphe de la ville de Québec a été utilisé comme environnement expérimental. De plus, des données de trafic ont été générées à partir d'une matrice origine-destination. Les résultats obtenus pour divers scénarios démontrent le plein potentiel du prototype. Le planificateur repère les segments d'un parcours menacés par un adversaire et assigne des unités pour accomplir des actions réglant ces problèmes

Estimation of origin-destination flows for dynamic traffic assignment

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 107-112).This thesis proposes a framework and method for dynamic origin-destination demand estimation. OD estimation is a critical component of a Dynamic Traffic Assignment system in that it determines the frequencies of drivers' trips through a network. The OD estimation method presented here allows for tunable optimization to three classes of objectives: Assigned traffic flows, deviation from historical data, and relative proportions in historical data. The method can be easily extended to make use of other sources of information such as direct measurements of OD flows from probe vehicles. The framework is extended to allow for nonnegativity and capacity constraints on the OD flows. As OD estimation is intended for use in a real-time setting, computational issues are critical, and several simplifications to increase computational efficiency are propsed and evaluted, called the Exact-Match estimator and the Large-Flow estimator. The algorithms presented are implemented as part of the DynaMIT (Dynamic Network Assignment for the Management of Information to Travelers) traffic estimation and prediction software, which incorporates models for driver route choice and traffic movement simulation.by Josef Joshua Brandriss.S.M