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210
Similarity of semantic relations
 Computational Linguistics
, 2006
"... There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words ..."
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Cited by 98 (6 self)
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There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This article introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47 % on a collection of 374 collegelevel multiplechoice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) The patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56 % on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM. 1.
Stateoftheart of Vehicular Traffic Flow Modelling
 Delft University of Technology, Delft, The
, 2001
"... Nowadays traffic flow and congestion is one of the main societal and economical problems related to transportation in industrialised countries. In this respect, managing traffic in congested networks requires a clear understanding of traffic flow operations. That is, insights into what causes conge ..."
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Cited by 66 (1 self)
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Nowadays traffic flow and congestion is one of the main societal and economical problems related to transportation in industrialised countries. In this respect, managing traffic in congested networks requires a clear understanding of traffic flow operations. That is, insights into what causes congestion, what determines the time and location of traffic breakdown, how does the congestion propagate through the network, etc., are essential. For this purpose, during the past fifty years, a wide range of traffic flow theories and models have been developed to answer these research questions. This paper presents a overview of some fifty years of modelling vehicular traffic flow. A rich variety of modelling approaches developed so far and in use today will be discussed and compared. The considered models are classified based on the levelofdetail with which the vehicular flow is described. For each of the categories, issues like modelling accuracy, applicability, generalisability, and model calibration and validation, are discussed.
Corpusbased learning of analogies and semantic relations
 Machine Learning
, 2005
"... Abstract. We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the SAT college entrance exam. A verbal analogy has the form A:B::C:D, meaning “A is to B as C is to D”; fo ..."
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Cited by 63 (12 self)
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Abstract. We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the SAT college entrance exam. A verbal analogy has the form A:B::C:D, meaning “A is to B as C is to D”; for example, mason:stone::carpenter:wood. SAT analogy questions provide a word pair, A:B, and the problem is to select the most analogous word pair, C:D, from a set of five choices. The VSM algorithm correctly answers 47 % of a collection of 374 collegelevel analogy questions (random guessing would yield 20 % correct; the average collegebound senior high school student answers about 57 % correctly). We motivate this research by applying it to a difficult problem in natural language processing, determining semantic relations in nounmodifier pairs. The problem is to classify a nounmodifier pair, such as “laser printer”, according to the semantic relation between the noun (printer) and the modifier (laser). We use a supervised nearestneighbour algorithm that assigns a class to a given nounmodifier pair by finding the most analogous nounmodifier pair in the training data. With 30 classes of semantic relations, on a collection of 600 labeled nounmodifier pairs, the learning algorithm attains an F value of 26.5 % (random guessing: 3.3%). With 5 classes of semantic relations, the F value is 43.2 % (random: 20%). The performance is stateoftheart for both verbal analogies and nounmodifier relations.
Micro and macrosimulation of freeway traffic
 Math. Comput. Modelling
"... We present simulations of congested traffic in circular and open systems with a nonlocal, gaskineticbased traffic model and a novel carfollowing model. The model parameters are all intuitive and can be easily calibrated. Micro and macrosimulations with these models for identical vehicles on a si ..."
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Cited by 36 (1 self)
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We present simulations of congested traffic in circular and open systems with a nonlocal, gaskineticbased traffic model and a novel carfollowing model. The model parameters are all intuitive and can be easily calibrated. Micro and macrosimulations with these models for identical vehicles on a single lane produce the same traffic states, which also qualitatively agree with empirical traffic observations. Moreover, the phase diagrams of traffic states in the presence of bottlenecks for the microscopic carfollowing model and the macroscopic gaskineticbased model almost agree. In both cases, we found metastable regimes, spatially coexistent states, and a small region of tristability. The distinction of different types of vehicles (cars and long vehicles) yields additional insight and allows to reproduce empirical data even more realistically, including the observed fluctuation properties of traffic flows like the wide scattering of congested traffic data. Finally, as an alternative to the gaskinetic approach, we propose a new scheme for deriving nonlocal macroscopic traffic models from given microscopic carfollowing models. Assuming identical (macroscopic) initial and boundary conditions, we show that there are microscopic models for which the corresponding macroscopic version displays an almost identical dynamics. This enables us to combine micro and macrosimulations of road sections by simple algorithms, and even to simulate them simultaneously.
A largescale agentbased traffic microsimulation based on queue model
 IN PROCEEDINGS OF SWISS TRANSPORT RESEARCH CONFERENCE (STRC), MONTE VERITA, CH
, 2003
"... We use the socalled queue model introducted by Gawron as the base of the traffic dynamics in our microsimulation. The queue model describes the links with a flow capacity that limits the number of agents that can leave the link and a space constraint which defines the limit of the number of agents ..."
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Cited by 24 (14 self)
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We use the socalled queue model introducted by Gawron as the base of the traffic dynamics in our microsimulation. The queue model describes the links with a flow capacity that limits the number of agents that can leave the link and a space constraint which defines the limit of the number of agents that can be on a link at the same time. Free flow speed is the third key component of traffic dynamics in the model. Flow capacity and space constraint together model physical queues, which can spill back beyond the end of the link. A consequence of this is that fairness between the incoming traffic streams becomes an issue, since in a spillback situation they cannot be served at their full rate. We implement and verify a simple solution to this; the solution is much simpler than the one chosen in many other models. The traffic microsimulation is “largescale ” which means the simulation is capable of modeling the behavior of millions of agents simultaneously. We utilize a parallel implementation to speed up the computation. In this implementation, the data is distributed onto a number of computing node, each of which runs a smaller portion of the data. Data distribution and communication
A theory of traffic flow in automated highway systems
 TRANSPORTATION RESEARCH PART C
, 1996
"... This paper presents a theory for automated traffic flow, based on an abstraction of vehicle activities like entry, exit and cruising, derived from a vehicle's automatic control laws. An activity is represented in the flow model by the space occupied by avehicle engaged in that activity. The the ..."
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This paper presents a theory for automated traffic flow, based on an abstraction of vehicle activities like entry, exit and cruising, derived from a vehicle's automatic control laws. An activity is represented in the flow model by the space occupied by avehicle engaged in that activity. The theory formulates TMC traffic plans as the specification of the activities and speed of vehicles, and the entry and exit flows for each highway section. We show that flows that achieve capacity can be realized by stationary plans that also minimize travel time. These optimum plans can be calculated by solving a linear programming problem. We illustrate these concepts by calculating the capacities of a one lane automated highway system, and compare adaptive cruise control and platooning strategies for automation. The theory permits the study of transient phenomena such as congestion, and TMC feedback traffic rules designed to deal with transients. We propose a "greedy" TMC rule that always achieves capacity but does not minimize travel time. Finally, we undertake a microscopic study of the "entry" activity, and show how lack of coordination between entering vehicles and vehicles on the main line disrupts traffic flow and increases travel time.
AdjointBased Control of a New Eulerian Network Model of Air Traffic Flow
, 2006
"... An Eulerian network model for air traffic flow in the National Airspace System is developed and used to design flow control schemes which could be used by Air Traffic Controllers to optimize traffic flow. The model relies on a modified version of the Lighthill–Whitham–Richards (LWR) partial differe ..."
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Cited by 22 (4 self)
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An Eulerian network model for air traffic flow in the National Airspace System is developed and used to design flow control schemes which could be used by Air Traffic Controllers to optimize traffic flow. The model relies on a modified version of the Lighthill–Whitham–Richards (LWR) partial differential equation (PDE), which contains a velocity control term inside the divergence operator. This PDE can be related to aircraft count, which is a key metric in air traffic control. An analytical solution to the LWR PDE is constructed for a benchmark problem, to assess the gridsize required to compute a numerical solution at a prescribed accuracy. The Jameson–Schmidt–Turkel (JST) scheme is selected among other numerical schemes to perform simulations, and evidence of numerical convergence is assessed against this analytical solution. Linear numerical schemes are discarded because of their poor performance. The model is validated against actual air traffic data (ETMS data), by showing that the Eulerian description enables good aircraft count predictions, provided a good choice of numerical parameters is made. This model is then embedded as the key constraint in an optimization problem, that of maximizing the throughput at a destination airport while maintaining aircraft density below a legal threshold in a set of sectors of the airspace. The optimization problem is solved by constructing the adjoint problem of the linearized network control problem, which provides an explicit formula for the gradient. Constraints are enforced using a logarithmic barrier. Simulations of actual air traffic data and control scenarios involving several airports between Chicago and the U.S. East Coast demonstrate the feasibility of the method.
Highway traffic state estimation using improved mixture Kalman filters for effective ramp metering control
 In Proc. of th 42nd IEEE Conf. on Decision and Control
, 2003
"... Abstract — In this paper, we use a cell transmission model based switching statespace model to estimate vehicle densities and congestion modes at unmeasured locations on a highway section. The mixture Kalman filter algorithm, which is based on sequential Monte Carlo method, is employed to approxima ..."
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Cited by 22 (4 self)
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Abstract — In this paper, we use a cell transmission model based switching statespace model to estimate vehicle densities and congestion modes at unmeasured locations on a highway section. The mixture Kalman filter algorithm, which is based on sequential Monte Carlo method, is employed to approximately solve the difficult problem of inference on a switching statespace model with an unobserved discrete state. We propose a scheme to prevent the risk of weight underflow and to introduce forgetting. The estimation results show that comparable accuracies can be achieved using either a small or a large number of sampling sequences, thus make it possible to carry out efficient online filtering. Underflow prevention and forgetting improves estimation accuracy in our examples. On average, a mean percentage error of approximately 10 % is achieved for the vehicle density estimation. The estimation performance is consistent with data sets from various days. I.
The Lagged CellTransmission Model
, 1999
"... Celltransmission models of highway traffic are discrete versions of the simple continuum (kinematic wave) model of traffic flow that are convenient for computer implementation. They are in the Godunov family of finite difference approximation methods for partial differential equations. In a celltr ..."
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Cited by 21 (3 self)
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Celltransmission models of highway traffic are discrete versions of the simple continuum (kinematic wave) model of traffic flow that are convenient for computer implementation. They are in the Godunov family of finite difference approximation methods for partial differential equations. In a celltransmission scheme one partitions a highway into small sections (cells) and keeps track of the cell contents (number of vehicles) as time passes. The record is updated at closely spaced instants (clock ticks) by calculating the number of vehicles that cross the boundary separating each pair of adjoining cells during the corresponding clock interval. This average flow is the result of a comparison between the maximum number of vehicles that can be "sent" by the cell directly upstream of the boundary and those that can be "received" by the downstream cell. The sending (receiving) flow is a simple function of the current traffic density in the upstream (downstream) cell. The particular form of ...
Mixture Kalman filter based highway congestion mode and vehicle density estimator and its application
 in Proceedings of the American Control Conference
, 2004
"... Abstract — In this paper, we present our latest results on developing and implementing a traffic congestion mode and vehicle density estimator for a segment of Interstate 210 in Southern California. Using a mixture Kalman filtering (MKF) algorithm on the switchingmode traffic model, the estimator i ..."
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Abstract — In this paper, we present our latest results on developing and implementing a traffic congestion mode and vehicle density estimator for a segment of Interstate 210 in Southern California. Using a mixture Kalman filtering (MKF) algorithm on the switchingmode traffic model, the estimator is able to provide estimated vehicle densities at unmeasured locations, as well as the congestion statuses (freeflow or congested), which are not directly observed. The program runs efficiently, thus making it possible to carry out estimation in real time. I.