ARTIFICIAL NEURAL NETWORKS FOR THE COMPUTATION OF TRAFFIC QUEUES
|
|
- Estella Harrison
- 6 years ago
- Views:
Transcription
1 ARTIFICIAL NEURAL NETWORKS FOR THE COMPUTATION OF TRAFFIC QUEUES Pedro Hernández-Arauzo, Secundino López-García, Antonio Bahamonde Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón Campus de Viesques. E Gijón, Spain {pedro, secun, ABSTRACT In this paper we present the results of our experimentation with artificial neural networks for computing traffic queues. These results are compared with the performance of other training methods. The neural scores are slightly better than those obtained by symbolic procedures. But a deeper analysis suggests the convenience of a hybrid device to try to get the best outcomes from each method in each situation. INTRODUCTION Adaptive urban traffic controllers (AUTC) are systems that calculate and implement signal timings in an attempt to optimize vehicle flow according to the latest traffic situation measured in the streets. To do this, the system's eyes are loop detectors usually placed in the roads upstream of the traffic flow; in this way it is possible to gather the recent history of vehicles crossing the detector site. The problem faced in this work is how to compute the number of stationary vehicles in a traffic queue waiting for a green signal. In fact, queues are the most important parameters used to improve traffic n an urban network. In Figure 1 we sketch the overall architecture of an AUTC emphasizing the queue management module.
2 Users Other transport management systems Interface Timing Management Implementation in the network Queue management Other demand Indicators Detectors Urban Network Figure 1 Architecture of an adaptive urban traffic controller When detectors provide a faithful image of what is going on in the streets, in order to compute the queue, we only need to know the number of vehicles crossing the stop line per time unit in green phases; this is a statistically found data of the studied lane. Therefore, in this case, we can calculate the number of stationary vehicles in front of a red signal since we know how many vehicles are in the waiting area (the region between the loop detector and the stop line). Difficulties arise when vehicles in a queue in a given lane reach the loop detector. Then the traffic trying to approach the stop line cannot be measured since they have not crossed the loop yet because of the queue. Unfortunately, we do not have several detectors in each lane nor can we place them behind the entry to the lane. Thus we should try to provide the control system with an accurate estimation of the queue even in this case. In fact, our only opportunity to compute the queue is to wait until cars begin to move in the next green phase and then try to figure out which of the cars detected by the loop was the last in the queue. This paper is concerned with a method for obtaining an effective and efficient device to discover which was the last car in a queue as it passes over a loop detector buried in the lane. We use an artificial neural network which was trained with data from a simulator built specially for this purpose [Hernández-Arauzo, 96]. The behavior of the simulator has been induced from real data obtained in the city of Oviedo (Spain) with the facilities of an AUTC constructed by our team [Bahamonde et al., 92]. The final section is devoted to comparing the results thus obtained with those produced by other training methods used to synthesize an algorithm to compute queues. Here we include symbolic machine learning using Quinlan's C4.5 [Quinlan,
3 93]. In all cases we present the results compared with training data and actual data obtained from street observations. Both situations show similar, successful results with our neural approach; slightly better than those obtained by symbolic procedures. At the same time, we indirectly show the accurateness of our simulator. ACCOUNTANCY OF VEHICLES IN THE QUEUE Traffic management is based on a cyclic idea. At junctions green and red phases split up a given amount of seconds (cycle time) after which the same signal arrangement starts again. Thus, data provided by detectors are collected in integer arrays called profiles. In fact there are two kinds of profiles: flow and occupancy. The former counts the number of vehicles crossing the loop each time unit (usually 5 seconds). On the other hand, occupancy profiles components record for how long the vehicles were passing over the detector. To compute the number of vehicles stopped in front of a red signal, we can start by taking it for granted that profiles data are an accurate and reliable representation of street traffic flow. In which case, the queue in a time t can be computed by queue(t) = queue (t- ) + vehicles_in (t-, t) - vehicles_out (t-, t) We are mainly interested in queues at two singular instants of the cycle for a given lane: red and green starts. Thus, if we have some data about traffic behavior in the area, such as average vehicle length and speed, it will be not difficult to compute the number of vehicles entering the queue. The queue output calculation is usually based on a statistically found data called saturation flow: the mean number of vehicles escaping from a queue per time unit. But, as was stated in the Introduction, the problem arises when the queue reaches the detector (see Figure 2). vehicles in waiting area Detector vehicles in queue behind the detector vehicle queue Figure 2. The queue goes beyond the detector
4 The problem is that profiles measured by detectors with a stationary vehicle on them lose their effectiveness since they do not count vehicles approaching the lane stop line. Figure 3 represents a pair of flow and occupancy profiles reported by a stepped detector. Here, from the first measure instant of the cycle we have full occupancy (5 out of the 5 seconds time measure unit) without any crossing car, so we must infer that the queue goes farther back than the detector. Later, once the green is open (second 70), traffic begins to be counted: flow becomes not null while occupancy is smaller than 5 each time unit. The queue goes beyond the detector Flow Occupancy Green time Figure 3. Profiles reported by a detector with a stationary vehicle on it However, we should stress that flows reported from second 70 until 100 do not represent vehicles approaching the stop line; they are the vehicles in the queue behind the detector (see Figure 2). Thus, if we try to apply the conventional formulas for faithful flow profiles, the results will be totally erroneous. Figure 4 shows queue lengths during cycle time computed this way and compared with real ones reported by our simulator. Vehicles in queue Time Simulator queue Flow profile queue Figure 4 Therefore, in the cycle recorded in the profiles of Figure 3, the answer about the queue that we would want to give should look like this: the queue can be summed up by queue = vehicles between the stop line and detector (waiting area) +
5 vehicles counted in flow profile from second 70 until 100 To reach this solution, we should be able to separate the platoon of vehicles in the queue from any other group or individuals. Given that we know when the platoon starts to be reported, we should concentrate on discovering when the last vehicle in the queue is crossing the detector. The main goal of our approach to computing queues is to focus on this point. In the next section, we will present an experimental method to compute said time. THE EXPERIMENTS According to the remarks of the previous section, we should compute a function like the following: last_vehicle_in_queue? (measure_instant): truth_value. That is, given a measure instant, we must state whether or not it corresponds with the passing over the detector of a vehicle which was the last one in the queue waiting for the green signal. Nevertheless, this decision does not depend only on one measure instant, but also on its neighborhood. To be precise, we take a window of 3 values in each profile (flow and occupancy). Thus, every measure instant will be analyzed with its flow, occupancy, and those of the previous and next instants. Given a pair of flow and occupancy profiles where the queue went beyond the detector, we can obtain an estimation of the queue if we test all possible ends of queue with the function last_vehicle_in_queue? until we obtain a positive answer. That is, we must decide where the end of the queue platoon is in order to add up their members, as we pointed out in the previous section. We use the functions where_is_queue_end (flow_profile, occupancy_profile): integer compute_queue (flow_profile, occupancy_profile): integer to compute the measure instant where the queue ends and the number of vehicles in queue, respectively. To start the experiment, we generated 597 double profiles (flow and occupancy) in different traffic situations. In all of them the queue end went beyond the detector. Attached to the profiles, we recorded when the actual last vehicle in the queue crossed the detector. With this data, we built 3142 training instances with the following syntax: -1, occupancy i - 1,, occupancy i, +1, occupancy i + 1, detector_position, class The ANN used for this purpose was a simple 3-layared feedforward network with 7 input units according to the input file syntax, 1 output unit and 15 hidden units. The reference to the detector position must describe if it is placed before or after the stop line.
6 The training was carried out with the SNNS [SNNS, 95] system. The method used was standard backpropagation with a learning rate of 0.2 and a maximum error allowed in output of COMPARISON OF ANN RESULTS WITH THOSE OBTAINED BY OTHER METHODS In [Hernández-Arauzo, 96] and [Hernández-Arauzo, 95] another two methods have been used to determine algorithms allowing us compute the function last_vehicle_in_queue?, and hence the functions where_is_queue_end and compute_queue. The first method is an explicit knowledge machine learning system. We used C4.5 [Quinlan, 93] to synthesize a set of clasification rules. In the training examples file, each entry describes the neighborhood of a measure instance taken from the same original profiles used to train the above mentioned ANN. Since the expressive power of rule conditions do not capture the sequential idea of previous and next data, we defined a set of attributes that capture the core of them. Here we took into account our experiences and field trials in real urban networks. To be precise, we used the following set of attributes: occupancy i = 0 and occupancy i = 0 > occupancy i + 1 > occupancy i = 0 and occupancy i + 1 = > occupancy i + occupancy i / occupancy i - 1 / occupancy i + 1 / occupancy i + 1 The training material was separated into two blocks: those proceeding from detectors placed before the stop line and after the stop line. In this way, we generated a set of 10 rules to compute the predicate last_vehicle_in_queue? in all circumstances. The second method to compute queues (called segmentation) was borrowed from image data processing. We assign to every pair of flow and occupancy some kind of membership degree to a queue state. These degrees can be seen as gray levels in a one dimensional image whose borders correspond to the start and end of a platoon moving over the detector. In the training process here, we just try to figure out good degrees for flow-occupancy pairs. The chosen option was simply the frequency of pairs in observed queues in training profiles. As before, we first separated the original data according to the relative situation of the detector. Although the function last_vehicle_in_queue? is an essential tool for determining when the last vehicle in the queue is crossing the stop line and therefore for evaluating errors produced by all the methods, we should emphasize that what is
7 really important for our purpose are the errors in computing the functions where_is_queue_end and compute_queue. In the following tables we present the scores computed for the 3 methods, adding up the results for profiles from detectors placed before and after the stop line. In the first column we give the average difference between the computed measure instant and the actual one. In the second one, we show the relative errors found in the number of vehicles in the queue. The first table presents the results obtained by the 3 methods in training cases where_is_queue_end compute_queue METHOD (absolute error) (relative errors) Symbolic Rules % Data Segmentation % ANN % Results in training cases generated by our simulator where_is_queue_end compute_queue METHOD (absolute error) (relative errors) Symbolic Rules % Data Segmentation % ANN % Results in real cases taken from the streets In order to formalize these results from a statistical viewpoint, we collected a new sample of 232 profiles drawn from the simulator. Functions where_is_queue_end and compute_queue were computed for each profile in the sample and for each method. Finally, 95% Confidence Intervals for population mean when population standard deviation is unknown were constructed for each method and function. The following table shows the results:
8 where_is_queue_end compute_queue METHOD lower bound upper bound lower bound upper bound Symbolic Rules Data Segmentation ANN An analogous proccess was followed with the set of real profiles. The results were: where_is_queue_end compute_queue METHOD lower bound upper bound lower bound upper bound Symbolic Rules Data Segmentation ANN Thus we can conclude that errors in the function where_is_queue_end were lower than 1 at confidence coefficient 0.95 for all methods. Errors in the function compute_queue were 1 at most for the ANN method and 2 at most for the remaining ones. Moreover, it should be emphasized that intervals derived from the ANN method are significantly shorter and nearer to 0, thus providing the most accurate estimation method. In the same way we have constructed 95% Confidence Intervals for differences of population means with unequal variances between real and simulated profiles, obtaining the following results:
9 where_is_queue_end compute_queue METHOD lower bound upper bound lower bound upper bound Symbolic Rules Data Segmentation ANN All the methods produce 95% Confidence Intervals whose bounds are 1 unit at most from 0 and therefore we can estimate the functions where_is_queue_end and compute_queue for the real profiles using methods trained with the simulated profiles with an error lower than 1. In other words, profiles provided by the simulator are very reliable for evaluating queues. As before, the lengths of intervals associated with the ANN method are significantly shorter than those obtained from the other methods. CONCLUSIONS Traffic queue computation relies on too much unreliable data about traffic behavior. We have to know fuzzy things like average speed or rate of vehicles escaping from a queue per time unit. Environmental aspects such as the weather or the state of the road make these parameters vary. On the other hand, flow profiles, when the queue goes beyond their placement, present puzzling measures. To avoid this, we have devised an experiment to estimate traffic queues. Using a simulator we gathered together a large amount of traffic problems and then trained an ANN to solve them. To test the algorithm thus obtained, we used both artificial training problems and some real ones taken from the street. Since the performance is quite similar, we indirectly conclude that our simulator is a faithful one and provides realistic profiles. However, the main result of the experiments reported in this paper is that it is possible to compute queues by means of a trained ANN with errors smaller than 0.62 vehicles 95% of times. Additionally, we have compared these results with those obtained with another two methods. Although the performance of the neural method is slightly better, we have observed that each method has some typical problems where they usually to fail in computing an accurate queue. This suggests the convenience of a hybrid device to try to get the best outcomes from each method in each situation.
10 BIBLIOGRAPHY BAHAMONDE, A., LÓPEZ-GARCÍA, S., HERNÁNDEZ-ARAUZO, P., BILBAO, A., VELA, C.R., ITACA: An Intelligent Urban Traffic Controller, Proceedings of IFAC Symposium on Intelligent Components and Instruments for Control Applications, SICICA'92, p , Málaga, HERNÁNDEZ ARAUZO, P., BAHAMONDE, A., LÓPEZ GARCÍA, S., Sobre la Calculabilidad del tiempo de desalojo de una cola de vehiculos. Proceedings of VI Conferencia de la Asociación Española para la Inteligencia Artificial, CAEPIA-95, p Alicante, Spain, HERNÁNDEZ-ARAUZO, P., Traffic queues computation. A virtual problems model, p ii. Ph. D. dissertation. Universidad de Oviedo at Gijón. Nov QUINLAN, J. R.: C4.5: Programs for Machine Learning, p. x+302, Morgan Kaufmann Publishers, San Mateo (California), ZELLA, A. et al., SNNS: Stuttgart Neural Network Simulator. User Manual, Version 4.1. Institute for Parallel and Distributed High Performance Systems, Technical Report No. 6/
Graphical Support of the Traffic Simulation System
Graphical Support of the Traffic Simulation System Jana Hájková hajkovaj@students.zcu.cz Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Pilsen, Czech
More informationLearning to assess from pair-wise comparisons 1
Learning to assess from pair-wise comparisons 1 (**) J. Díez (*), J.J. del Coz (**), O. Luaces (**), F. Goyache (*), J. Alonso (**), A. M. Peña (***), and A. Bahamonde (**) (*) SERIDA-CENSYRA-Somió, C/
More informationWe have seen that as n increases, the length of our confidence interval decreases, the confidence interval will be more narrow.
{Confidence Intervals for Population Means} Now we will discuss a few loose ends. Before moving into our final discussion of confidence intervals for one population mean, let s review a few important results
More informationData analysis using Microsoft Excel
Introduction to Statistics Statistics may be defined as the science of collection, organization presentation analysis and interpretation of numerical data from the logical analysis. 1.Collection of Data
More informationMapping of Sensor and Route Coordinates for Smart Cities
1 Mapping of Sensor and Route Coordinates for Smart Cities Yasir Saleem, Noel Crespi Institute Mines-Telecom, Telecom SudParis, France yasir saleem.shaikh@telecom-sudparis.eu, noel.crespi@mines-telecom.fr
More informationAN INTELLIGENT TRAFFIC CONTROLLER BASED ON FUZZY LOGIC
AN INTELLIGENT TRAFFIC CONTROLLER BASED ON FUZZY LOGIC Bilal Ahmed Khan; Nai Shyan Lai Asia Pacific University of Technology and Innovation belalkhn22@gmail.com Abstract Traffic light plays an important
More informationPractical Tips for using Backpropagation
Practical Tips for using Backpropagation Keith L. Downing August 31, 2017 1 Introduction In practice, backpropagation is as much an art as a science. The user typically needs to try many combinations of
More informationCase-Based Reasoning
0/0/ Case-Based Reasoning In this lecture, we turn to another popular form of reasoning system: case based reasoning (CBR) Unlike Rule-based systems and Fuzzy Logic, CBR does not use any rules or logical
More informationWorst-Case Utilization Bound for EDF Scheduling on Real-Time Multiprocessor Systems
Worst-Case Utilization Bound for EDF Scheduling on Real-Time Multiprocessor Systems J.M. López, M. García, J.L. Díaz, D.F. García University of Oviedo Department of Computer Science Campus de Viesques,
More informationSearching with Partial Information
Searching with Partial Information Above we (unrealistically) assumed that the environment is fully observable and deterministic Moreover, we assumed that the agent knows what the effects of each action
More informationMetaData for Database Mining
MetaData for Database Mining John Cleary, Geoffrey Holmes, Sally Jo Cunningham, and Ian H. Witten Department of Computer Science University of Waikato Hamilton, New Zealand. Abstract: At present, a machine
More informationValidation for Data Classification
Validation for Data Classification HILARIO LÓPEZ and IVÁN MACHÓN and EVA FERNÁNDEZ Departamento de Ingeniería Eléctrica, Electrónica de Computadores y Sistemas Universidad de Oviedo Edificio Departamental
More informationAdvanced Operations Research Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras
Advanced Operations Research Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Lecture - 35 Quadratic Programming In this lecture, we continue our discussion on
More informationA NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL. Bosnia Herzegovina. Denizli 20070, Turkey. Buyukcekmece, Istanbul, Turkey
Mathematical and Computational Applications, Vol. 15, No. 2, pp. 269-278, 2010. Association for Scientific Research A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL B. Gültekin Çetiner 1, Murat Sari
More informationAdvanced Transportation Optimization Systems (ATOS)
Advanced Transportation Optimization Systems (ATOS) By Andrew Andrusko Undergraduate Student Student in Civil Engineering, Urban & Regional Studies, Social Studies, Geography, Geology Programs Minnesota
More informationQuantitative Models for Performance Enhancement of Information Retrieval from Relational Databases
Quantitative Models for Performance Enhancement of Information Retrieval from Relational Databases Jenna Estep Corvis Corporation, Columbia, MD 21046 Natarajan Gautam Harold and Inge Marcus Department
More informationPerformance Analysis of Data Mining Classification Techniques
Performance Analysis of Data Mining Classification Techniques Tejas Mehta 1, Dr. Dhaval Kathiriya 2 Ph.D. Student, School of Computer Science, Dr. Babasaheb Ambedkar Open University, Gujarat, India 1 Principal
More informationTowards more robust internetworks:
Towards more robust internetworks: an application of graph theory Authors Jamie Greenwood, MSc (Royal Holloway, 2016) Stephen Wolthusen, ISG, Royal Holloway Abstract As networks become increasingly connected
More informationA Lazy Approach for Machine Learning Algorithms
A Lazy Approach for Machine Learning Algorithms Inés M. Galván, José M. Valls, Nicolas Lecomte and Pedro Isasi Abstract Most machine learning algorithms are eager methods in the sense that a model is generated
More informationData Analysis and Solver Plugins for KSpread USER S MANUAL. Tomasz Maliszewski
Data Analysis and Solver Plugins for KSpread USER S MANUAL Tomasz Maliszewski tmaliszewski@wp.pl Table of Content CHAPTER 1: INTRODUCTION... 3 1.1. ABOUT DATA ANALYSIS PLUGIN... 3 1.3. ABOUT SOLVER PLUGIN...
More informationDSRC Field Trials Whitepaper
DSRC Field Trials Whitepaper August 19, 2017 www.cohdawireless.com 1 Overview Cohda has performed more than 300 Dedicated Short Range Communications (DSRC) field trials, comparing DSRC radios from multiple
More informationVirtual Robotics Challenge Technical Guide
Virtual Robotics Challenge Technical Guide DRAFT Version 2 on November 21, 2012 DISTAR Case 20372 Table of Contents 1 Introduction... 2 2 Computing Environment... 2 3 Conducting Runs... 3 4 Configurations...
More informationCohda Wireless White Paper DSRC Field Trials
Cohda Wireless White Paper DSRC Field Trials Copyright Cohda Wireless Pty Ltd ABN 84 107 936 309 Cohda Wireless Pty Ltd 82-84 Melbourne Street North Adelaide, SA 5006 Australia P +61 8 8364 4719 F +61
More informationDriveFaster: Optimizing a Traffic Light Grid System
DriveFaster: Optimizing a Traffic Light Grid System Abstract CS221 Fall 2016: Final Report Team Members: Xiaofan Li, Ahmed Jaffery Traffic lights are the central point of control of traffic for cities
More informationAPPLICATION OF AERIAL VIDEO FOR TRAFFIC FLOW MONITORING AND MANAGEMENT
Pitu Mirchandani, Professor, Department of Systems and Industrial Engineering Mark Hickman, Assistant Professor, Department of Civil Engineering Alejandro Angel, Graduate Researcher Dinesh Chandnani, Graduate
More informationFuzzy Partitioning with FID3.1
Fuzzy Partitioning with FID3.1 Cezary Z. Janikow Dept. of Mathematics and Computer Science University of Missouri St. Louis St. Louis, Missouri 63121 janikow@umsl.edu Maciej Fajfer Institute of Computing
More informationTrain schedule diagram drawing algorithm considering interrelationship between labels
Train schedule diagram drawing algorithm considering interrelationship between labels H. Izumi', N. Tomii',2 The University of Electro-Communications, Japan. 2Railway Technical Research Institute, Japan.
More informationFast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade Paul Viola and Michael Jones Mistubishi Electric Research Lab Cambridge, MA viola@merl.com and mjones@merl.com Abstract This
More informationPERFORMANCE MEASUREMENTS OF FEATURE TRACKING AND HISTOGRAM BASED TRAFFIC CONGESTION ALGORITHMS
PERFORMANCE MEASUREMENTS OF FEATURE TRACKING AND HISTOGRAM BASED TRAFFIC CONGESTION ALGORITHMS Ozgur Altun 1 and Kenan Aksoy 2 Proline Bilisim Sistemleri, Istanbul, Turkey 1 Research and Development Engineer,
More informationVARIATIONS IN CAPACITY AND DELAY ESTIMATES FROM MICROSCOPIC TRAFFIC SIMULATION MODELS
VARIATIONS IN CAPACITY AND DELAY ESTIMATES FROM MICROSCOPIC TRAFFIC SIMULATION MODELS (Transportation Research Record 1802, pp. 23-31, 2002) Zong Z. Tian Associate Transportation Researcher Texas Transportation
More informationEstimating Missing Attribute Values Using Dynamically-Ordered Attribute Trees
Estimating Missing Attribute Values Using Dynamically-Ordered Attribute Trees Jing Wang Computer Science Department, The University of Iowa jing-wang-1@uiowa.edu W. Nick Street Management Sciences Department,
More informationRough Set Approach to Unsupervised Neural Network based Pattern Classifier
Rough Set Approach to Unsupervised Neural based Pattern Classifier Ashwin Kothari, Member IAENG, Avinash Keskar, Shreesha Srinath, and Rakesh Chalsani Abstract Early Convergence, input feature space with
More informationImproving Stack Overflow Tag Prediction Using Eye Tracking Alina Lazar Youngstown State University Bonita Sharif, Youngstown State University
Improving Stack Overflow Tag Prediction Using Eye Tracking Alina Lazar, Youngstown State University Bonita Sharif, Youngstown State University Jenna Wise, Youngstown State University Alyssa Pawluk, Youngstown
More information1 Digital 3D models and civil engineering design
Application of digital 3D models on urban planning and highway design. L.A. Hernández & S. Hernández ETS de Ingenieros de Caminos, Canales y Puertos, Universidad de La Coruña, Campus de Elviña, 15071 La
More informationNEURON IMPLEMENTATION USING SYSTEM GENERATOR
NEURON IMPLEMENTATION USING SYSTEM GENERATOR Luis Aguiar,2, Leonardo Reis,2, Fernando Morgado Dias,2 Centro de Competências de Ciências Exactas e da Engenharia, Universidade da Madeira Campus da Penteada,
More informationHierarchical Routing System using Ant Based Control
Hierarchical Routing System using Ant Based Control Henrik Dibowski THESIS Delft, July 2003 Delft University of Technology, The Netherlands Faculty of Information Technology and Systems Knowledge Based
More informationImplementation of Classification Rules using Oracle PL/SQL
1 Implementation of Classification Rules using Oracle PL/SQL David Taniar 1 Gillian D cruz 1 J. Wenny Rahayu 2 1 School of Business Systems, Monash University, Australia Email: David.Taniar@infotech.monash.edu.au
More informationMachine Learning 13. week
Machine Learning 13. week Deep Learning Convolutional Neural Network Recurrent Neural Network 1 Why Deep Learning is so Popular? 1. Increase in the amount of data Thanks to the Internet, huge amount of
More informationThe negative effects of homogeneous traffic on merging sections
The negative effects of homogeneous traffic on merging sections J.A.C.M. Elbers a,1 and E.C. van Berkum a a Centre for Transport Studies University of Twente, Department of Civil Engineering Tel: +31 534893821,
More informationJournal of Emerging Trends in Computing and Information Sciences
Method of Fault Data Analysis of Sensor Node based on Wireless Data Communication Protocols Seung-Ki Ryu * Research Fellow, Korea Institute of Civil Engineering and Building Technology, Korea (*corresponding
More information6.001 Notes: Section 6.1
6.001 Notes: Section 6.1 Slide 6.1.1 When we first starting talking about Scheme expressions, you may recall we said that (almost) every Scheme expression had three components, a syntax (legal ways of
More informationFormal Model. Figure 1: The target concept T is a subset of the concept S = [0, 1]. The search agent needs to search S for a point in T.
Although this paper analyzes shaping with respect to its benefits on search problems, the reader should recognize that shaping is often intimately related to reinforcement learning. The objective in reinforcement
More informationTowards a benchmark of the ODE API methods for accessing ontologies In the WebODE platform
Towards a benchmark of the ODE API methods for accessing ontologies In the WebODE platform Oscar Corcho, Raúl García-Castro, Asunción Gómez-Pérez (Ontology Group, Departamento de Inteligencia Artificial,
More informationUSING IMAGES PATTERN RECOGNITION AND NEURAL NETWORKS FOR COATING QUALITY ASSESSMENT Image processing for quality assessment
USING IMAGES PATTERN RECOGNITION AND NEURAL NETWORKS FOR COATING QUALITY ASSESSMENT Image processing for quality assessment L.-M. CHANG and Y.A. ABDELRAZIG School of Civil Engineering, Purdue University,
More information3. If so, was this taken into consideration when designing the RFP?
9770 Culver Boulevard, Culver City, California 90232 Charles D. Herbertson, P.E., LS Public Works Director and City Engineer Mate Gaspar, P.E. Engineering Services Manager (310) 253-5600 FAX (310) 253-5626
More informationControlling user groups in traffic
Controlling user groups in traffic Jaap Vreeswijk 1, Luc Wismans 2, Bas Tutert 3 1. Imtech Traffic & Infra, Basicweg 16, 3821 BR Amersfoort, The Netherlands, Phone: +31 33 454 1724, Mail: jaap.vreeswijk@imtech.com
More informationDIFFERENCES BETWEEN THE METHOD OF MINI-MODELS AND THE K-NEAREST NEIGHBORS ON EXAMPLE OF MODELING OF UNEMPLOYMENT RATE IN POLAND
DIFFERENCES BETWEEN THE METHOD OF MINI-MODELS AND THE K-NEAREST NEIGHBORS ON EXAMPLE OF MODELING OF UNEMPLOYMENT RATE IN POLAND Andrzej Piegat a), b), Barbara Wąsikowska b), Marcin Korzeń a) a) Faculty
More informationAnalysis and Designing of Proposed Intelligent Road Traffic Congestion Control System with Image Mosaicking Technique
27 Analysis and Designing of Proposed Intelligent Road Traffic Congestion Control System with Image Mosaicking Technique Prof. (Dr.) Y.P.Singh, Director, KLS Institute of Engineering & Technology, Chandok,
More informationMULTI-VIEW TARGET CLASSIFICATION IN SYNTHETIC APERTURE SONAR IMAGERY
MULTI-VIEW TARGET CLASSIFICATION IN SYNTHETIC APERTURE SONAR IMAGERY David Williams a, Johannes Groen b ab NATO Undersea Research Centre, Viale San Bartolomeo 400, 19126 La Spezia, Italy Contact Author:
More informationEfficient SQL-Querying Method for Data Mining in Large Data Bases
Efficient SQL-Querying Method for Data Mining in Large Data Bases Nguyen Hung Son Institute of Mathematics Warsaw University Banacha 2, 02095, Warsaw, Poland Abstract Data mining can be understood as a
More informationChapter 10. Fundamental Network Algorithms. M. E. J. Newman. May 6, M. E. J. Newman Chapter 10 May 6, / 33
Chapter 10 Fundamental Network Algorithms M. E. J. Newman May 6, 2015 M. E. J. Newman Chapter 10 May 6, 2015 1 / 33 Table of Contents 1 Algorithms for Degrees and Degree Distributions Degree-Degree Correlation
More informationAttachment 1. PenPlace Transportation and Traffic: Neighboring Communities Concerns, Issues and Proposed Action Plan
Attachment 1 PenPlace Transportation and Traffic: Neighboring Communities Concerns, Issues and Proposed Action Plan Traffic Impact Analysis (TIA) and Neighborhood Traffic Study The PenPlace TIA relies
More informationBinary Representations of Integers and the Performance of Selectorecombinative Genetic Algorithms
Binary Representations of Integers and the Performance of Selectorecombinative Genetic Algorithms Franz Rothlauf Department of Information Systems University of Bayreuth, Germany franz.rothlauf@uni-bayreuth.de
More informationAccuracy of Matching between Probe-Vehicle and GIS Map
Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences Shanghai, P. R. China, June 25-27, 2008, pp. 59-64 Accuracy of Matching between
More informationEECS 428 Final Project Report Distributed Real-Time Process Control Over TCP and the Internet Brian Robinson
EECS 428 Final Project Report Distributed Real-Time Process Control Over TCP and the Internet Brian Robinson 1.0 Introduction Distributed real-time process control, from a network communication view, involves
More informationTraffic Signs Recognition using HP and HOG Descriptors Combined to MLP and SVM Classifiers
Traffic Signs Recognition using HP and HOG Descriptors Combined to MLP and SVM Classifiers A. Salhi, B. Minaoui, M. Fakir, H. Chakib, H. Grimech Faculty of science and Technology Sultan Moulay Slimane
More informationTelecommunication and Informatics University of North Carolina, Technical University of Gdansk Charlotte, NC 28223, USA
A Decoder-based Evolutionary Algorithm for Constrained Parameter Optimization Problems S lawomir Kozie l 1 and Zbigniew Michalewicz 2 1 Department of Electronics, 2 Department of Computer Science, Telecommunication
More informationRelation Organization of SOM Initial Map by Improved Node Exchange
JOURNAL OF COMPUTERS, VOL. 3, NO. 9, SEPTEMBER 2008 77 Relation Organization of SOM Initial Map by Improved Node Echange MIYOSHI Tsutomu Department of Information and Electronics, Tottori University, Tottori,
More informationIntroduction to and calibration of a conceptual LUTI model based on neural networks
Urban Transport 591 Introduction to and calibration of a conceptual LUTI model based on neural networks F. Tillema & M. F. A. M. van Maarseveen Centre for transport studies, Civil Engineering, University
More informationImproving Tree-Based Classification Rules Using a Particle Swarm Optimization
Improving Tree-Based Classification Rules Using a Particle Swarm Optimization Chi-Hyuck Jun *, Yun-Ju Cho, and Hyeseon Lee Department of Industrial and Management Engineering Pohang University of Science
More informationVirtual Instruments and Soft Sensors 2.1 Virtual Instruments
2 Virtual Instruments and Soft Sensors 2.1 Virtual Instruments In this chapter, the concepts of virtual instruments (VIs) and of soft sensors will be introduced in some detail. In particular, we will commence
More informationFace recognition algorithms: performance evaluation
Face recognition algorithms: performance evaluation Project Report Marco Del Coco - Pierluigi Carcagnì Institute of Applied Sciences and Intelligent systems c/o Dhitech scarl Campus Universitario via Monteroni
More informationALGORITHM AND SOFTWARE BASED ON MLPNN FOR ESTIMATING CHANNEL USE IN THE SPECTRAL DECISION STAGE IN COGNITIVE RADIO NETWORKS
ALGORITHM AND SOFTWARE BASED ON MLPNN FOR ESTIMATING CHANNEL USE IN THE SPECTRAL DECISION STAGE IN COGNITIVE RADIO NETWORKS Johana Hernández Viveros 1, Danilo López Sarmiento 2 and Nelson Enrique Vera
More informationDetection and Classification of Vehicles
Detection and Classification of Vehicles Gupte et al. 2002 Zeeshan Mohammad ECG 782 Dr. Brendan Morris. Introduction Previously, magnetic loop detectors were used to count vehicles passing over them. Advantages
More informationSYSTEM CONFIGURATION AND FUNCTIONAL OUTLINE OF MPD ROAD TRAFFIC CONTROL CENTER
Maintaining 'Control Consoles' Responsible for Controlling Traffic across Metropolitan Tokyo Masayuki Kurosawa Traffic Facilities and Control Division, Traffic Bureau Tokyo Metropolitan Police Department
More informationNeurally Inspired Mechanisms for the Dynamic Visual Attention Map Generation Task
Neurally Inspired Mechanisms for the Dynamic Visual Attention Map Generation Task Maria T. López 1, Miguel A. Fernández 1, Antonio Fernández-Caballero 1, and Ana E. Delgado 2 1 Departamento de Informática
More informationDeep Learning. Architecture Design for. Sargur N. Srihari
Architecture Design for Deep Learning Sargur N. srihari@cedar.buffalo.edu 1 Topics Overview 1. Example: Learning XOR 2. Gradient-Based Learning 3. Hidden Units 4. Architecture Design 5. Backpropagation
More informationWHO KEEPS THE CITY S RHYTHM FLOWING?
WHO KEEPS THE CITY S RHYTHM FLOWING? IMPLEMENT YOUR TRAFFIC-ADAPTIVE NETWORK CONTROL Short delays, moderate travel times, fewer emissions, reduced noise. There are plenty of reasons to optimise traffic
More informationDynamic Traffic Pattern Classification Using Artificial Neural Networks
14 TRANSPORTATION RESEARCH RECORD 1399 Dynamic Traffic Pattern Classification Using Artificial Neural Networks }IUYI HUA AND ARDESHIR FAGHRI Because of the difficulty of modeling the traffic conditions
More informationADAPTIVE TRAFFIC LIGHT IN IMAGE PROCESSING BASED- INTELLIGENT TRANSPORTATION SYSTEM: A REVIEW
ADAPTIVE TRAFFIC LIGHT IN IMAGE PROCESSING BASED- INTELLIGENT TRANSPORTATION SYSTEM: A REVIEW 1 Mustafa Mohammed Hassan Mustafa* 2 Atika Malik * 3 Amir Mohammed Talib Faculty of Engineering, Future University,
More informationProcess State Estimation in a Wastewater Biological Treatment
Process State Estimation in a Wastewater Biological Treatment IVÁN MACHÓN and HILARIO LÓPEZ and ANTONIO ROBLES Departamento de Ingeniería Eléctrica, Electrónica de Computadores y Sistemas Universidad de
More information1 Introduction RHIT UNDERGRAD. MATH. J., VOL. 17, NO. 1 PAGE 159
RHIT UNDERGRAD. MATH. J., VOL. 17, NO. 1 PAGE 159 1 Introduction Kidney transplantation is widely accepted as the preferred treatment for the majority of patients with end stage renal disease [11]. Patients
More informationUser s Manual. Version
COPERT Street Level User s Manual Version 2.2.53 Contents Introduction... 3 Installing COPERT street level... 4 Main menu items... 5 Menu Project... 5 Project > Project > Create file... 5 Project > Project
More informationThe Encoding Complexity of Network Coding
The Encoding Complexity of Network Coding Michael Langberg Alexander Sprintson Jehoshua Bruck California Institute of Technology Email: mikel,spalex,bruck @caltech.edu Abstract In the multicast network
More informationIoannis Psarros Department of Civil Engineering and Intermodal Freight Transportation Institute, Memphis, TN
Ioannis Psarros Department of Civil Engineering and Intermodal Freight Transportation Institute, Memphis, TN CIVL 7904/8904: Traffic Flow Theory (Spring 2014) April 5, 2014 Transportation Research Increased
More informationA Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection (Kohavi, 1995)
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection (Kohavi, 1995) Department of Information, Operations and Management Sciences Stern School of Business, NYU padamopo@stern.nyu.edu
More informationVerification Plan: Mitchell Hammock Road. Adaptive Traffic Signal Control System. Prepared by: City of Oviedo. Draft 1: June 2015
Verification Plan: Mitchell Hammock Road Adaptive Traffic Signal Control System Red Bug Lake Road from Slavia Road to SR 426 Mitchell Hammock Road from SR 426 to Lockwood Boulevard Lockwood Boulevard from
More informationLane Detection using Fuzzy C-Means Clustering
Lane Detection using Fuzzy C-Means Clustering Kwang-Baek Kim, Doo Heon Song 2, Jae-Hyun Cho 3 Dept. of Computer Engineering, Silla University, Busan, Korea 2 Dept. of Computer Games, Yong-in SongDam University,
More informationDESIGNING ALGORITHMS FOR SEARCHING FOR OPTIMAL/TWIN POINTS OF SALE IN EXPANSION STRATEGIES FOR GEOMARKETING TOOLS
X MODELLING WEEK DESIGNING ALGORITHMS FOR SEARCHING FOR OPTIMAL/TWIN POINTS OF SALE IN EXPANSION STRATEGIES FOR GEOMARKETING TOOLS FACULTY OF MATHEMATICS PARTICIPANTS: AMANDA CABANILLAS (UCM) MIRIAM FERNÁNDEZ
More informationFace Detection on Similar Color Photographs
Face Detection on Similar Color Photographs Scott Leahy EE368: Digital Image Processing Professor: Bernd Girod Stanford University Spring 2003 Final Project: Face Detection Leahy, 2/2 Table of Contents
More informationFUZZY BOOLEAN ALGEBRAS AND LUKASIEWICZ LOGIC. Angel Garrido
Acta Universitatis Apulensis ISSN: 1582-5329 No. 22/2010 pp. 101-111 FUZZY BOOLEAN ALGEBRAS AND LUKASIEWICZ LOGIC Angel Garrido Abstract. In this paper, we analyze the more adequate tools to solve many
More informationIdentification of Vehicle Class and Speed for Mixed Sensor Technology using Fuzzy- Neural & Genetic Algorithm : A Design Approach
Identification of Vehicle Class and Speed for Mixed Sensor Technology using Fuzzy- Neural & Genetic Algorithm : A Design Approach Prashant Sharma, Research Scholar, GHRCE, Nagpur, India, Dr. Preeti Bajaj,
More informationSimplifying OCR Neural Networks with Oracle Learning
SCIMA 2003 - International Workshop on Soft Computing Techniques in Instrumentation, Measurement and Related Applications Provo, Utah, USA, 17 May 2003 Simplifying OCR Neural Networks with Oracle Learning
More informationThe Surveyor s Guide to Automotive Simulation:
DLR.de Chart 1 The Surveyor s Guide to Automotive Simulation: Development and Evaluation of Guidelines for Straightforward Road Surveying for Driving Simulator Databases and Test Development of Driver
More informationIB Chemistry IA Checklist Design (D)
Taken from blogs.bethel.k12.or.us/aweyand/files/2010/11/ia-checklist.doc IB Chemistry IA Checklist Design (D) Aspect 1: Defining the problem and selecting variables Report has a title which clearly reflects
More informationUsing CODEQ to Train Feed-forward Neural Networks
Using CODEQ to Train Feed-forward Neural Networks Mahamed G. H. Omran 1 and Faisal al-adwani 2 1 Department of Computer Science, Gulf University for Science and Technology, Kuwait, Kuwait omran.m@gust.edu.kw
More informationAmerican Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) ISSN (Print) , ISSN (Online)
American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) ISSN (Print) 2313-4410, ISSN (Online) 2313-4402 Global Society of Scientific Research and Researchers http://asrjetsjournal.org/
More informationNEW HYBRID LEARNING ALGORITHMS IN ADAPTIVE NEURO FUZZY INFERENCE SYSTEMS FOR CONTRACTION SCOUR MODELING
Proceedings of the 4 th International Conference on Environmental Science and Technology Rhodes, Greece, 3-5 September 05 NEW HYBRID LEARNING ALGRITHMS IN ADAPTIVE NEUR FUZZY INFERENCE SYSTEMS FR CNTRACTIN
More informationPerformance Evaluation of Non-Intrusive Methods for Traffic Data Collection. Kamal Banger, Ministry of Transportation of Ontario
Abstract Performance Evaluation of Non-Intrusive Methods for Traffic Data Collection Kamal Banger, Ministry of Transportation of Ontario Nancy Adriano, P.Eng., Ministry of Transportation of Ontario For
More informationExecutive Overview. D1.3.2-VanetMobiSim/Ns-2: A VANET simulator for CARLINK
Executive Overview Title: D1.3.2-VanetMobiSim/Ns-2: A VANET simulator for CARLINK Summary: This document provides a brief introduction and analysis of the simulation tool VanetMobiSim/NS-2 proposed for
More informationData can be in the form of numbers, words, measurements, observations or even just descriptions of things.
+ What is Data? Data is a collection of facts. Data can be in the form of numbers, words, measurements, observations or even just descriptions of things. In most cases, data needs to be interpreted and
More informationPerformance Analysis of the Intertwined Effects between Network Layers for g Transmissions
Performance Analysis of the Intertwined Effects between Network Layers for 802.11g Transmissions Jon Gretarsson, Feng Li, Mingzhe Li, Ashish Samant, Huahui Wu, Mark Claypool and Robert Kinicki WPI Computer
More informationAN INTRODUCTION TO FUZZY SETS Analysis and Design. Witold Pedrycz and Fernando Gomide
AN INTRODUCTION TO FUZZY SETS Analysis and Design Witold Pedrycz and Fernando Gomide A Bradford Book The MIT Press Cambridge, Massachusetts London, England Foreword - Preface Introduction xiii xxv xxi
More informationOnline Simultaneous Localization and Mapping in Dynamic Environments
To appear in Proceedings of the Intl. Conf. on Robotics and Automation ICRA New Orleans, Louisiana, Apr, 2004 Online Simultaneous Localization and Mapping in Dynamic Environments Denis Wolf and Gaurav
More informationA USER-FRIENDLY AUTOMATIC TOOL FOR IMAGE CLASSIFICATION BASED ON NEURAL NETWORKS
A USER-FRIENDLY AUTOMATIC TOOL FOR IMAGE CLASSIFICATION BASED ON NEURAL NETWORKS B. Buttarazzi, F. Del Frate*, C. Solimini Università Tor Vergata, Ingegneria DISP, Via del Politecnico 1, I-00133 Rome,
More informationAPPLICATION OF THE FUZZY MIN-MAX NEURAL NETWORK CLASSIFIER TO PROBLEMS WITH CONTINUOUS AND DISCRETE ATTRIBUTES
APPLICATION OF THE FUZZY MIN-MAX NEURAL NETWORK CLASSIFIER TO PROBLEMS WITH CONTINUOUS AND DISCRETE ATTRIBUTES A. Likas, K. Blekas and A. Stafylopatis National Technical University of Athens Department
More informationSEISMIC TESTING OF LARGE SCALE STRUCTURES WITH DISSIPATOR DEVICES
11 th International Conference on Vibration Problems Z. Dimitrovová et al. (eds.) Lisbon, Portugal, 9-12 September 2013 SEISMIC TESTING OF LARGE SCALE STRUCTURES WITH DISSIPATOR DEVICES Beatriz Zapico*
More informationUsing Genetic Algorithms to Solve the Box Stacking Problem
Using Genetic Algorithms to Solve the Box Stacking Problem Jenniffer Estrada, Kris Lee, Ryan Edgar October 7th, 2010 Abstract The box stacking or strip stacking problem is exceedingly difficult to solve
More informationSocial Voting Techniques: A Comparison of the Methods Used for Explicit Feedback in Recommendation Systems
Special Issue on Computer Science and Software Engineering Social Voting Techniques: A Comparison of the Methods Used for Explicit Feedback in Recommendation Systems Edward Rolando Nuñez-Valdez 1, Juan
More informationVisual object classification by sparse convolutional neural networks
Visual object classification by sparse convolutional neural networks Alexander Gepperth 1 1- Ruhr-Universität Bochum - Institute for Neural Dynamics Universitätsstraße 150, 44801 Bochum - Germany Abstract.
More information