On-site Traffic Accident Detection with Both Social Media and Traffic Data
|
|
- Charleen Russell
- 5 years ago
- Views:
Transcription
1 On-site Traffic Accident Detection with Both Social Media and Traffic Data Zhenhua Zhang Civil, Structural and Environmental Engineering University at Buffalo, The State University of New York, Buffalo, NY, USA Qing He 1 Civil, Structural and Environmental Engineering and Industrial and Systems Engineering University at Buffalo, The State University of New York, Buffalo, NY, USA 1. Introduction qinghe@buffalo.edu Social media receives increasing attentions as crowdsourced information for traffic operations and management. One recent trending study is to use social media (e.g. tweets) to detect onsite traffic accidents (Gu et al., 2016). However, the shortcomings of using tweets directly as a detector of traffic accidents are almost as obvious as its merits. There are two major challenges to be addressed before the use of tweets in traffic accident detection. First, as compared to events that arouse enormous public concerns such as key basketball games, extreme weathers or traditional festivals, the influence of traffic accidents are comparably a midget. From our observation, tweets related to traffic accidents are thus in small quantity. What s more, most of them are confined to a small area and limited to a relatively short time interval. Second, the challenge in tweets lies in its inheritable complexity and unstructured nature of data: language ambiguity (Chen et al., 2014). The context of tweet is limited to 140 words which is not long enough for accurate automatic language processing using some keyword pairs. For example, internet traffic is slow and internet shows traffic is slow may deliver totally different information. In addition, it remains unknown how effective the social media based detection methods is as compared with traditional loop detector based method. To address above challenges, we propose a method to combine both the traffic-related metrics and tweet information for accurate real-time detection of on-site traffic accidents. In principle, the fusion of multi-source data provides significant advantages over single source data (Hall and Llinas, 1997), and the integration of data sources is expected to produce more synthetic and informative results. 2. Data Description and Models The study area is the vast road network of Northern Virginia (NOVA). The area has long been known for its heavy traffic (Cervero, 1994). It is a typical rural road network with more than 1,200 signalized intersections. Each intersection equips with an average of 12 lane-based loop detectors, the total of which amount to nearly 15,000 in NOVA. (1) Traffic data, including traffic flow and occupancy, were collected by these loop detectors at an interval of 15 minutes for 12 months, from January 2014 to December (2) Tweet data were collected through Twitter Streaming API with geo-location filter. Filtering by the coordinates, we extracted tweets posted only from NOVA region. There are more than 584,000 tweets throughout the year of 2014, and all of them have specific date and location information. (3) The accident data were extracted from traffic incident database maintained by Virginia Department of Transportation (VDOT) with detailed time and location information. The accidents include collision, disabled vehicle, vehicle on fire etc. 1 Corresponding author.
2 We employ the support vector machines (SVMs) as our classification model. We first give manual labels for the tweets and select the corresponding features separately based on traffic and tweet data. In training the regression model, we further implement 5-fold cross validation to increase the accuracy of the predicted model. 3. Feature selection based on tweet data Tweet features are extracted from the keywords of candidate tweets. From the whole tweet database, we obtain nearly 1500 eligible tweets which: Include the tweets that may contain any of accident, incident, crash, collision, head on, damage, pile up, rear end, rear-end, sideswipe, lost control, rolled over, roll over, tailgating Include the words that are relevant to accidents but apparently misspelled or personally modified including acident, incdent, etc. Include other variations of accident-related words such as the word pairs that have a hyphen in word pair such as roll-over, etc. Exclude the words related to transportation authority or news media. In single feature selection, each tweet is further decomposed into separate words that are called token in our paper. Then, we select the useful token features by three steps: stop-word filtering, keyword stemming and correlated-word filtering. The process is as shown in Figure 1: Tweet database T: T 1 : I saw a traffic accident in front. T 2 : Car damage on Route 1. Stemmed tokens: see traffic accident route saw see accident accidents traffic route Tokenization Stemming Tokens: i saw a traffic in accident front car damage on, route 1 see is an case damages accidents Stop-word filtering Tokens: I saw a traffic in accident front car damage on route 1 see is an case damages accidents Figure 1 Steps of token filtering and stemming In correlated-word filtering, we select those tokens that may correlate with our traffic accident label. The correlation benchmark we choose is phi coefficient. The coefficient (usually denoted as ϕ) between two variables x and y is calculated as: φ = n 11n 00 n 10 n 01 n 1 n 0 n 0 n 1, where n ab is the counts for x = a and y = b; when a or b = 0 or 1, we consider both counts for x or y. Those tokens whose φ is higher than 0.1 are selected. Following this rule, 46 tokens are selected and part of them are shown in the list: Features Correlation Features Correlation Features Correlation traffic virginia near accidently glad 0.146
3 car closed bad major accident lanes From Table 1, some of the tokens may be accounted by the geographic uniqueness such as virginia ; some may direct to the road names such as 95, 270. Others may be that of relevant topics traffic, accident. Potentially, the co-occurrence of certain keyword pairs in a tweet may indicate the existence of traffic accident. We further select the features from paired tokens by study the association rules between the manual label. The association rules can be unveiled by the Apriori algorithm which can find the regularities in large-scale binary data by two major probabilities: confidence and support: conf(l i t j1 t j2 t jm ) = supp(l i t j 1 t j2 t jm ) supp(t j 1 t j2 t jm ) ; supp(t j ) = sizeof({t i,t j T i }). sizeof({t i }) By setting the support equal to 0.1 and confidence equal to 0.5, our results show that most paired tokens contain accident. The paired features in a given tweet are equal to 1 if the tweet contains the corresponding paired tokens and otherwise 0. Parts of the features are shown in the following list: loop inner accident mile accident road accident exit accident major accident close accident south accident left accident bad loop accident accident involve accident block lane 4. Feature selection based on traffic data For each detector, we evenly divide the traffic occupancy into N separate groups. For each traffic occupancy group, we take the median of the corresponding traffic flow values as the traffic signature. We use the median because it is less affected by outliers than mean. The traffic signature of a detector d is defined as the vector of these traffic flow values. That is: F d = (F 1 d, F 2 d,, F o d,, F N d ), where F o d is the median value of traffic flow given a range of occupancy o in detector d. One can see that for each detector, the traffic pattern is a vector of N traffic flow values. We assume the relationship between traffic flow and occupancy in a given location may not change over time. However, these traffic signatures among detectors may be quite different. Those detectors with similar traffic signatures should be clustered into the same group. To cluster the traffic patterns of each detector, we employ the K-means algorithm without predefining the clustering centers and the number of clusters. We finally cluster nearly 15,000 detectors into 15 different clusters/groups. For each cluster, the traffic flows over a specified occupancy interval are distributed around their cluster centers. Further, the outliers can be quantified by a probabilistic method that measures its deviation degree. Our empirical examinations show that the distributions of the traffic flow in a particular cluster and occupancy interval follows a Gaussian distribution as shown in Figure 2. Therefore, the traffic outliers be quantified by the probability P dt of traffic accident where d is the detector and t is time.
4 (a) (b) Figure 2 (a) Comparisons between clustered centers and the original traffic flow and occupancy data in one detector; (b) The traffic flow distribution over a range of occupancy. To better quantify the traffic influence as to a tweet post, we mainly study the traffic related information within certain spatial and temporal ranges. Based on these traffic data, two features are then generated for our regression model for each tweet: mean and 75 th percentile value of P dt : p traffic = 1 NUM t dom(t) d dom(d) Pdt ;. q traffic = Q3( {P dt, d dom(d) t dom(t)}. 5. Results and Conclusions Single token Single token + Traffic data Single token + Paired token All features Accuracy Precision1 Precision2 Figure 3 Comparisons of accuracy and precision using different features; precision 1 is for accident, precision 2 is for non-accident. The results indicate that that paired tokens can possibly capture the association rules can increase the accuracy of the traffic accident detection. We even employ our model with features of single and paired tokens to predict the accident label of all tweets in NOVA. For each accident-related tweet, we make comparison between the prediction results and the traffic management log maintained by VDOT. The time and location differences of tweets to their nearest accident records are shown in Figure 4.
5 Density Density 0e+00 2e-04 4e Time difference (min) Space difference (meter) (a) (b) Figure 4 (a) Time and (b) space difference between the accident-related tweets and the accident records by VDOT. We can conclude that first, sometimes the tweet reflection on the traffic accident is much faster than the traditional methods; second, tweets can sometimes capture those mild accidents that do not incur the attention of traffic police can make up for the deficiencies of traffic management log. Also, there are problems with the tweet accident-prediction methods that the locations are not so accurate and the coverage of tweets are not high enough to cover all traffic accidents in the whole area. References Cervero, R., Rail transit and joint development: Land market impacts in Washington, DC and Atlanta. Journal of the American Planning Association 60, Chen, P.-T., Chen, F., Qian, Z., Road traffic congestion monitoring in social media with hinge-loss Markov random fields, Data Mining (ICDM), 2014 IEEE International Conference on. IEEE, pp Gu, Y., Qian, Z.S., Chen, F., From Twitter to detector: Real-time traffic incident detection using social media data. Transportation Research Part C: Emerging Technologies 67, Hall, D.L., Llinas, J., An introduction to multisensor data fusion. Proceedings of the IEEE 85, 6-23.
1. Travel time measurement using Bluetooth detectors 2. Travel times on arterials (characteristics & challenges) 3. Dealing with outliers 4.
1. Travel time measurement using Bluetooth detectors 2. Travel times on arterials (characteristics & challenges) 3. Dealing with outliers 4. Travel time prediction Travel time = 2 40 9:16:00 9:15:50 Travel
More informationSpatial-Temporal Data Mining in Traffic Incident Detection
Spatial-Temporal Data Mining in Traffic Incident Detection Ying Jin, Jing Dai, Chang-Tien Lu Department of Computer Science, Virginia Polytechnic Institute and State University {jiny, daij, ctlu}@vt.edu
More informationBIG DATA EUROPE TRANSPORT PILOT: INTRODUCING THESSALONIKI. Josep Maria Salanova Grau CERTH-HIT
BIG DATA EUROPE TRANSPORT PILOT: INTRODUCING THESSALONIKI Josep Maria Salanova Grau CERTH-HIT Thessaloniki on the map ~ 1.400.000 inhabitants & ~ 1.300.000 daily trips ~450.000 private cars & ~ 20.000
More informationIDENTIFICATION OF SIGNATURES TRANSMITTED OVER RAYLEIGH FADING CHANNEL BY USING HMM AND RLE
International Journal of Technology (2011) 1: 56 64 ISSN 2086 9614 IJTech 2011 IDENTIFICATION OF SIGNATURES TRANSMITTED OVER RAYLEIGH FADING CHANNEL BY USING HMM AND RLE Djamhari Sirat 1, Arman D. Diponegoro
More informationDeployment and Testing of Optimized Autonomous and Connected Vehicle Trajectories at a Closed- Course Signalized Intersection
Deployment and Testing of Optimized Autonomous and Connected Vehicle Trajectories at a Closed- Course Signalized Intersection Clark Letter*, Lily Elefteriadou, Mahmoud Pourmehrab, Aschkan Omidvar Civil
More informationTraffic Management for Smart Cities TNK115 SMART CITIES
Traffic Management for Smart Cities TNK115 SMART CITIES DAVID GUNDLEGÅRD DIVISION OF COMMUNICATION AND TRANSPORT SYSTEMS Outline Introduction Traffic sensors Traffic models Frameworks Information VS Control
More informationData fusion for traffic flow estimation at intersections
Data fusion for traffic flow estimation at intersections Axel WOLFERMANN Masao KUWAHARA Babak MEHRAN German Aerospace Center (DLR e. V.) Tohoku University Germany Japan Canada Outline Part I Motivation
More informationExperimental study of traffic noise and human response in an urban area: deviations from standard annoyance predictions
Experimental study of traffic noise and human response in an urban area: deviations from standard annoyance predictions Erik M. SALOMONS 1 ; Sabine A. JANSSEN 2 ; Henk L.M. VERHAGEN 3 ; Peter W. WESSELS
More informationESTIMATING ROAD TRAFFIC PARAMETERS FROM MOBILE COMMUNICATIONS
ESTIMATING ROAD TRAFFIC PARAMETERS FROM MOBILE COMMUNICATIONS R. Bolla, F. Davoli, A. Giordano Department of Communications, Computer and Systems Science (DIST University of Genoa Via Opera Pia 13, I-115
More informationData collection and modeling for APTS and ATIS under Indian conditions - Challenges and Solutions
Data collection and modeling for APTS and ATIS under Indian conditions - Challenges and Solutions Lelitha Vanajakshi Dept. of Civil Engg. IIT Madras, India lelitha@iitm.ac.in Outline Introduction Automated
More informationTraffic Control for a Swarm of Robots: Avoiding Group Conflicts
Traffic Control for a Swarm of Robots: Avoiding Group Conflicts Leandro Soriano Marcolino and Luiz Chaimowicz Abstract A very common problem in the navigation of robotic swarms is when groups of robots
More informationA Fuzzy Signal Controller for Isolated Intersections
1741741741741749 Journal of Uncertain Systems Vol.3, No.3, pp.174-182, 2009 Online at: www.jus.org.uk A Fuzzy Signal Controller for Isolated Intersections Mohammad Hossein Fazel Zarandi, Shabnam Rezapour
More informationAutomated Driving Car Using Image Processing
Automated Driving Car Using Image Processing Shrey Shah 1, Debjyoti Das Adhikary 2, Ashish Maheta 3 Abstract: In day to day life many car accidents occur due to lack of concentration as well as lack of
More informationAdvanced Techniques for Mobile Robotics Location-Based Activity Recognition
Advanced Techniques for Mobile Robotics Location-Based Activity Recognition Wolfram Burgard, Cyrill Stachniss, Kai Arras, Maren Bennewitz Activity Recognition Based on L. Liao, D. J. Patterson, D. Fox,
More informationAnurag Pande & Mohamed Abdel-Aty Department of Civil and Environmental Engineering, University of Central Florida, Orlando, FL
A Computing Approach Using Probabilistic Neural Networks for Instantaneous Appraisal of Rear-End Crash Risk Anurag Pande & Mohamed Abdel-Aty Department of Civil and Environmental Engineering, University
More informationOutline for this presentation. Introduction I -- background. Introduction I Background
Mining Spectrum Usage Data: A Large-Scale Spectrum Measurement Study Sixing Yin, Dawei Chen, Qian Zhang, Mingyan Liu, Shufang Li Outline for this presentation! Introduction! Methodology! Statistic and
More informationComputing Touristic Walking Routes using Geotagged Photographs from Flickr
Research Collection Conference Paper Computing Touristic Walking Routes using Geotagged Photographs from Flickr Author(s): Mor, Matan; Dalyot, Sagi Publication Date: 2018-01-15 Permanent Link: https://doi.org/10.3929/ethz-b-000225591
More informationObjective Evaluation of Edge Blur and Ringing Artefacts: Application to JPEG and JPEG 2000 Image Codecs
Objective Evaluation of Edge Blur and Artefacts: Application to JPEG and JPEG 2 Image Codecs G. A. D. Punchihewa, D. G. Bailey, and R. M. Hodgson Institute of Information Sciences and Technology, Massey
More informationWHITE PAPER BENEFITS OF OPTICOM GPS. Upgrading from Infrared to GPS Emergency Vehicle Preemption GLOB A L TRAFFIC TE CHNOLOGIE S
WHITE PAPER BENEFITS OF OPTICOM GPS Upgrading from Infrared to GPS Emergency Vehicle Preemption GLOB A L TRAFFIC TE CHNOLOGIE S 2 CONTENTS Overview 3 Operation 4 Advantages of Opticom GPS 5 Opticom GPS
More informationUsing Deep Learning for Sentiment Analysis and Opinion Mining
Using Deep Learning for Sentiment Analysis and Opinion Mining Gauging opinions is faster and more accurate. Abstract How does a computer analyze sentiment? How does a computer determine if a comment or
More informationInnovative mobility data collection tools for sustainable planning
Innovative mobility data collection tools for sustainable planning Dr. Maria Morfoulaki Center for Research and Technology Hellas (CERTH)/ Hellenic Institute of Transport (HIT) marmor@certh.gr Data requested
More informationLink Activation with Parallel Interference Cancellation in Multi-hop VANET
Link Activation with Parallel Interference Cancellation in Multi-hop VANET Meysam Azizian, Soumaya Cherkaoui and Abdelhakim Senhaji Hafid Department of Electrical and Computer Engineering, Université de
More informationFig.2 the simulation system model framework
International Conference on Information Science and Computer Applications (ISCA 2013) Simulation and Application of Urban intersection traffic flow model Yubin Li 1,a,Bingmou Cui 2,b,Siyu Hao 2,c,Yan Wei
More informationTraffic Incident Detection Enabled by Large Data Analytics. REaltime AnlytiCs on TranspORtation data
Traffic Incident Detection Enabled by Large Data Analytics REaltime AnlytiCs on TranspORtation data Authors Forrest Hoffman (standing) and Bill Hargrove sit "inside" the computer they constructed from
More informationASSESSING THE POTENTIAL FOR THE AUTOMATIC DETECTION OF INCIDENTS ON THE BASIS OF INFORMATION OBTAINED FROM ELECTRONIC TOLL TAGS
ASSESSING THE POTENTIAL FOR THE AUTOMATIC DETECTION OF INCIDENTS ON THE BASIS OF INFORMATION OBTAINED FROM ELECTRONIC TOLL TAGS Bruce Hellinga Department of Civil Engineering, University of Waterloo, Waterloo,
More informationNo-Reference Image Quality Assessment using Blur and Noise
o-reference Image Quality Assessment using and oise Min Goo Choi, Jung Hoon Jung, and Jae Wook Jeon International Science Inde Electrical and Computer Engineering waset.org/publication/2066 Abstract Assessment
More informationIntelligent Traffic Signal Control System Using Embedded System
Intelligent Traffic Signal Control System Using Embedded System Dinesh Rotake 1* Prof. Swapnili Karmore 2 1. Department of Electronics Engineering, G. H. Raisoni College of Engineering, Nagpur 2. Department
More informationNext Generation of Adaptive Traffic Signal Control
Next Generation of Adaptive Traffic Signal Control Pitu Mirchandani ATLAS Research Laboratory Arizona State University NSF Workshop Rutgers, New Brunswick, NJ June 7, 2010 Acknowledgements: FHWA, ADOT,
More informationComplex networks in applied research
IBM Research Ireland Smarter Urban Dynamics Complex networks in applied research Michele Berlingerio, PhD, Research Staff Member 2010 2016 IBM Corporation IBM Research: 5 new labs established since 2010
More informationGlobal Journal of Engineering Science and Research Management
A KERNEL BASED APPROACH: USING MOVIE SCRIPT FOR ASSESSING BOX OFFICE PERFORMANCE Mr.K.R. Dabhade *1 Ms. S.S. Ponde 2 *1 Computer Science Department. D.I.E.M.S. 2 Asst. Prof. Computer Science Department,
More informationApplying Multisensor Information Fusion Technology to Develop an UAV Aircraft with Collision Avoidance Model
1 Applying Multisensor Information Fusion Technology to Develop an UAV Aircraft with Collision Avoidance Model {Final Version with
More informationApplying Multisensor Information Fusion Technology to Develop an UAV Aircraft with Collision Avoidance Model
Applying Multisensor Information Fusion Technology to Develop an UAV Aircraft with Collision Avoidance Model by Dr. Buddy H Jeun and John Younker Sensor Fusion Technology, LLC 4522 Village Springs Run
More informationRecognition Of Vehicle Number Plate Using MATLAB
Recognition Of Vehicle Number Plate Using MATLAB Mr. Ami Kumar Parida 1, SH Mayuri 2,Pallabi Nayk 3,Nidhi Bharti 4 1Asst. Professor, Gandhi Institute Of Engineering and Technology, Gunupur 234Under Graduate,
More informationLatest trends in sentiment analysis - A survey
Latest trends in sentiment analysis - A survey Anju Rose G Punneliparambil PG Scholar Department of Computer Science & Engineering Govt. Engineering College, Thrissur, India anjurose.ar@gmail.com Abstract
More informationA Review of Related Work on Machine Learning in Semiconductor Manufacturing and Assembly Lines
A Review of Related Work on Machine Learning in Semiconductor Manufacturing and Assembly Lines DI Darko Stanisavljevic VIRTUAL VEHICLE DI Michael Spitzer VIRTUAL VEHICLE i-know 16 18.-19.10.2016, Graz
More informationLicense Plate Localisation based on Morphological Operations
License Plate Localisation based on Morphological Operations Xiaojun Zhai, Faycal Benssali and Soodamani Ramalingam School of Engineering & Technology University of Hertfordshire, UH Hatfield, UK Abstract
More informationFebruary 24, [Click for Most Updated Paper] [Click for Most Updated Online Appendices]
ONLINE APPENDICES for How Well Do Automated Linking Methods Perform in Historical Samples? Evidence from New Ground Truth Martha Bailey, 1,2 Connor Cole, 1 Morgan Henderson, 1 Catherine Massey 1 1 University
More informationComparative Study of various Surveys on Sentiment Analysis
Comparative Study of various Surveys on Milanjit Kaur 1, Deepak Kumar 2. 1 Student (M.Tech Scholar), Computer Science and Engineering, Lovely Professional University, Punjab, India. 2 Assistant Professor,
More informationClassification of Road Images for Lane Detection
Classification of Road Images for Lane Detection Mingyu Kim minkyu89@stanford.edu Insun Jang insunj@stanford.edu Eunmo Yang eyang89@stanford.edu 1. Introduction In the research on autonomous car, it is
More informationAnalysis of Data Mining Methods for Social Media
65 Analysis of Data Mining Methods for Social Media Keshav S Rawat Department of Computer Science & Informatics, Central university of Himachal Pradesh Dharamshala (Himachal Pradesh) Email:Keshav79699@gmail.com
More informationThe Pennsylvania State University The Graduate School A STATISTICS-BASED FRAMEWORK FOR BUS TRAVEL TIME PREDICTION
The Pennsylvania State University The Graduate School A STATISTICS-BASED FRAMEWORK FOR BUS TRAVEL TIME PREDICTION A Thesis in Computer Science and Engineering by Weiping Si c 2012 Weiping Si Submitted
More informationKernels and Support Vector Machines
Kernels and Support Vector Machines Machine Learning CSE446 Sham Kakade University of Washington November 1, 2016 2016 Sham Kakade 1 Announcements: Project Milestones coming up HW2 You ve implemented GD,
More informationFINAL REPORT IMPROVING THE EFFECTIVENESS OF TRAFFIC MONITORING BASED ON WIRELESS LOCATION TECHNOLOGY. Michael D. Fontaine, P.E. Research Scientist
FINAL REPORT IMPROVING THE EFFECTIVENESS OF TRAFFIC MONITORING BASED ON WIRELESS LOCATION TECHNOLOGY Michael D. Fontaine, P.E. Research Scientist Brian L. Smith, Ph.D. Faculty Research Scientist and Associate
More informationQosmotec. Software Solutions GmbH. Technical Overview. QPER C2X - Car-to-X Signal Strength Emulator and HiL Test Bench. Page 1
Qosmotec Software Solutions GmbH Technical Overview QPER C2X - Page 1 TABLE OF CONTENTS 0 DOCUMENT CONTROL...3 0.1 Imprint...3 0.2 Document Description...3 1 SYSTEM DESCRIPTION...4 1.1 General Concept...4
More informationClustering of traffic accidents with the use of the KDE+ method
Richard Andrášik*, Michal Bíl Transport Research Centre, Líšeňská 33a, 636 00 Brno, Czech Republic *e-mail: andrasik.richard@gmail.com Clustering of traffic accidents with the use of the KDE+ method TABLE
More informationA GI Science Perspective on Geocoding:
A GI Science Perspective on Geocoding: Accuracy, Repeatability and Implications for Geospatial Privacy Paul A Zandbergen Department of Geography University of New Mexico Geocoding as an Example of Applied
More informationCurrently 2 vacant engineer positions (1 Engineer level, 1 Managing Engineer level)
INDOT Agency Factoids (System/Comm.) Number of signalized intersections- 2570 200 connected by fiber 300 connected by radio 0 connected by twisted pair 225 connected by cellular 1500 not connected to communication
More informationWheeler-Classified Vehicle Detection System using CCTV Cameras
Wheeler-Classified Vehicle Detection System using CCTV Cameras Pratishtha Gupta Assistant Professor: Computer Science Banasthali University Jaipur, India G. N. Purohit Professor: Computer Science Banasthali
More informationSpeed Estimation in Forward Scattering Radar by Using Standard Deviation Method
Vol. 3, No. 3 Modern Applied Science Speed Estimation in Forward Scattering Radar by Using Standard Deviation Method Mutaz Salah, MFA Rasid & RSA Raja Abdullah Department of Computer and Communication
More informationReduce the Wait Time For Customers at Checkout
BADM PROJECT REPORT Reduce the Wait Time For Customers at Checkout Pankaj Sharma - 61310346 Bhaskar Kandukuri 61310697 Varun Unnikrishnan 61310181 Santosh Gowda 61310163 Anuj Bajpai - 61310663 1. Business
More informationIntelligent Technology for More Advanced Autonomous Driving
FEATURED ARTICLES Autonomous Driving Technology for Connected Cars Intelligent Technology for More Advanced Autonomous Driving Autonomous driving is recognized as an important technology for dealing with
More informationCellSense: A Probabilistic RSSI-based GSM Positioning System
CellSense: A Probabilistic RSSI-based GSM Positioning System Mohamed Ibrahim Wireless Intelligent Networks Center (WINC) Nile University Smart Village, Egypt Email: m.ibrahim@nileu.edu.eg Moustafa Youssef
More informationApplications of Music Processing
Lecture Music Processing Applications of Music Processing Christian Dittmar International Audio Laboratories Erlangen christian.dittmar@audiolabs-erlangen.de Singing Voice Detection Important pre-requisite
More informationUrban Traffic Bottleneck Identification Based on Congestion Propagation
Urban Traffic Bottleneck Identification Based on Congestion Propagation Wenwei Yue, Changle Li, Senior Member, IEEE and Guoqiang Mao, Fellow, IEEE State Key Laboratory of Integrated Services Networks,
More informationKeywords- Fuzzy Logic, Fuzzy Variables, Traffic Control, Membership Functions and Fuzzy Rule Base.
Volume 6, Issue 12, December 2016 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Fuzzy Logic
More informationA TWO-PART PREDICTIVE CODER FOR MULTITASK SIGNAL COMPRESSION. Scott Deeann Chen and Pierre Moulin
A TWO-PART PREDICTIVE CODER FOR MULTITASK SIGNAL COMPRESSION Scott Deeann Chen and Pierre Moulin University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering 5 North Mathews
More informationA VIDEO CAMERA ROAD SIGN SYSTEM OF THE EARLY WARNING FROM COLLISION WITH THE WILD ANIMALS
Vol. 12, Issue 1/2016, 42-46 DOI: 10.1515/cee-2016-0006 A VIDEO CAMERA ROAD SIGN SYSTEM OF THE EARLY WARNING FROM COLLISION WITH THE WILD ANIMALS Slavomir MATUSKA 1*, Robert HUDEC 2, Patrik KAMENCAY 3,
More informationSONG RETRIEVAL SYSTEM USING HIDDEN MARKOV MODELS
SONG RETRIEVAL SYSTEM USING HIDDEN MARKOV MODELS AKSHAY CHANDRASHEKARAN ANOOP RAMAKRISHNA akshayc@cmu.edu anoopr@andrew.cmu.edu ABHISHEK JAIN GE YANG ajain2@andrew.cmu.edu younger@cmu.edu NIDHI KOHLI R
More informationAlgorithm for Detector-Error Screening on Basis of Temporal and Spatial Information
Algorithm for Detector-Error Screening on Basis of Temporal and Spatial Information Yang (Carl) Lu, Xianfeng Yang, and Gang-Len Chang Although average effective vehicle length (AEVL) has been recognized
More informationA Vehicular Visual Tracking System Incorporating Global Positioning System
A Vehicular Visual Tracking System Incorporating Global Positioning System Hsien-Chou Liao and Yu-Shiang Wang Abstract Surveillance system is widely used in the traffic monitoring. The deployment of cameras
More informationVALIDATION OF LINK TRAVEL TIME USING GPS DATA: A Case Study of Western Expressway, Mumbai
Map Asia 2005 Jaarta, Indonesia VALIDATION OF LINK TRAVEL TIME USING GPS DATA: A Case Study of Western Expressway, Mumbai Saurabh Gupta 1, Tom V. Mathew 2 Transportation Systems Engineering Department
More informationSignal Patterns for Improving Light Rail Operation By Wintana Miller and Mark Madden DKS Associates
Signal Patterns for Improving Light Rail Operation By Wintana Miller and Mark Madden DKS Associates Abstract This paper describes the follow up to a pilot project to coordinate traffic signals with light
More informationBig Data Framework for Synchrophasor Data Analysis
Big Data Framework for Synchrophasor Data Analysis Pavel Etingov, Jason Hou, Huiying Ren, Heng Wang, Troy Zuroske, and Dimitri Zarzhitsky Pacific Northwest National Laboratory North American Synchrophasor
More informationDevelopment of an Advanced Loop Event Data Analyzer (ALEDA) System for Dual-Loop Detector Malfunction Detection and Investigation
Development of an Advanced Loop Event Data Analyzer (ALEDA) System for Dual-Loop Detector Malfunction Detection and Investigation Patikhom Cheevarunothai 1*, Yinhai Wang 2, and Nancy L. Nihan 3 1* Graduate
More informationSmartphone Motion Mode Recognition
proceedings Proceedings Smartphone Motion Mode Recognition Itzik Klein *, Yuval Solaz and Guy Ohayon Rafael, Advanced Defense Systems LTD., POB 2250, Haifa, 3102102 Israel; yuvalso@rafael.co.il (Y.S.);
More informationChapter 2 Distributed Consensus Estimation of Wireless Sensor Networks
Chapter 2 Distributed Consensus Estimation of Wireless Sensor Networks Recently, consensus based distributed estimation has attracted considerable attention from various fields to estimate deterministic
More informationGPS data correction using encoders and INS sensors
GPS data correction using encoders and INS sensors Sid Ahmed Berrabah Mechanical Department, Royal Military School, Belgium, Avenue de la Renaissance 30, 1000 Brussels, Belgium sidahmed.berrabah@rma.ac.be
More informationWHITE PAPER. NLP TOOL (Natural Language Processing) User Case: isocialcube (Social Networks Campaign Management)
WHITE PAPER NLP TOOL (Natural Language Processing) User Case: isocialcube (Social Networks Campaign Management) www.aynitech.com What does the Customer need? isocialcube s (ISC) helps companies manage
More informationTable 1. List of NFL divisions that have won the Superbowl over the past 52 years.
MA 2113 Homework #1 Table 1. List of NFL divisions that have won the Superbowl over the past 52 years. NFC North AFC West NFC East NFC North AFC South NFC North NFC East NFC East AFC West NFC East AFC
More informationTime-aware Collaborative Topic Regression: Towards Higher Relevance in Textual Items Recommendation
July, 12 th 2018 Time-aware Collaborative Topic Regression: Towards Higher Relevance in Textual Items Recommendation BIRNDL 2018, Ann Arbor Anas Alzogbi University of Freiburg Databases & Information Systems
More informationA Vehicular Visual Tracking System Incorporating Global Positioning System
A Vehicular Visual Tracking System Incorporating Global Positioning System Hsien-Chou Liao and Yu-Shiang Wang Abstract Surveillance system is widely used in the traffic monitoring. The deployment of cameras
More informationAdaptive Feature Analysis Based SAR Image Classification
I J C T A, 10(9), 2017, pp. 973-977 International Science Press ISSN: 0974-5572 Adaptive Feature Analysis Based SAR Image Classification Debabrata Samanta*, Abul Hasnat** and Mousumi Paul*** ABSTRACT SAR
More informationSpeed Enforcement Systems Based on Vision and Radar Fusion: An Implementation and Evaluation 1
Speed Enforcement Systems Based on Vision and Radar Fusion: An Implementation and Evaluation 1 Seungki Ryu *, 2 Youngtae Jo, 3 Yeohwan Yoon, 4 Sangman Lee, 5 Gwanho Choi 1 Research Fellow, Korea Institute
More informationAssociation Rule Mining. Entscheidungsunterstützungssysteme SS 18
Association Rule Mining Entscheidungsunterstützungssysteme SS 18 Frequent Pattern Analysis Frequent pattern: a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data
More informationWide-area Motion Imagery for Multi-INT Situational Awareness
Wide-area Motion Imagery for Multi-INT Situational Awareness Bernard V. Brower Jason Baker Brian Wenink Harris Corporation TABLE OF CONTENTS ABSTRACT... 3 INTRODUCTION WAMI HISTORY... 4 WAMI Capabilities
More informationA Vehicular Visual Tracking System Incorporating Global Positioning System
Vol:5, :6, 20 A Vehicular Visual Tracking System Incorporating Global Positioning System Hsien-Chou Liao and Yu-Shiang Wang International Science Index, Computer and Information Engineering Vol:5, :6,
More informationClassification of Voltage Sag Using Multi-resolution Analysis and Support Vector Machine
Journal of Clean Energy Technologies, Vol. 4, No. 3, May 2016 Classification of Voltage Sag Using Multi-resolution Analysis and Support Vector Machine Hanim Ismail, Zuhaina Zakaria, and Noraliza Hamzah
More informationUsing Administrative Records for Imputation in the Decennial Census 1
Using Administrative Records for Imputation in the Decennial Census 1 James Farber, Deborah Wagner, and Dean Resnick U.S. Census Bureau James Farber, U.S. Census Bureau, Washington, DC 20233-9200 Keywords:
More informationMarch 10, Greenbelt Road, Suite 400, Greenbelt, MD Tel: (301) Fax: (301)
Detection of High Risk Intersections Using Synthetic Machine Vision John Alesse, john.alesse.ctr@dot.gov Brian O Donnell, brian.odonnell.ctr@dot.gov Stinger Ghaffarian Technologies, Inc. Cambridge, Massachusetts
More informationGeorgia Department of Transportation. Automated Traffic Signal Performance Measures Reporting Details
Georgia Department of Transportation Automated Traffic Signal Performance Measures Prepared for: Georgia Department of Transportation 600 West Peachtree Street, NW Atlanta, Georgia 30308 Prepared by: Atkins
More informationSOUND: A Traffic Simulation Model for Oversaturated Traffic Flow on Urban Expressways
SOUND: A Traffic Simulation Model for Oversaturated Traffic Flow on Urban Expressways Toshio Yoshii 1) and Masao Kuwahara 2) 1: Research Assistant 2: Associate Professor Institute of Industrial Science,
More informationMaximum Likelihood Sequence Detection (MLSD) and the utilization of the Viterbi Algorithm
Maximum Likelihood Sequence Detection (MLSD) and the utilization of the Viterbi Algorithm Presented to Dr. Tareq Al-Naffouri By Mohamed Samir Mazloum Omar Diaa Shawky Abstract Signaling schemes with memory
More informationInterframe Coding of Global Image Signatures for Mobile Augmented Reality
Interframe Coding of Global Image Signatures for Mobile Augmented Reality David Chen 1, Mina Makar 1,2, Andre Araujo 1, Bernd Girod 1 1 Department of Electrical Engineering, Stanford University 2 Qualcomm
More informationInitialisation improvement in engineering feedforward ANN models.
Initialisation improvement in engineering feedforward ANN models. A. Krimpenis and G.-C. Vosniakos National Technical University of Athens, School of Mechanical Engineering, Manufacturing Technology Division,
More informationHigh-speed Noise Cancellation with Microphone Array
Noise Cancellation a Posteriori Probability, Maximum Criteria Independent Component Analysis High-speed Noise Cancellation with Microphone Array We propose the use of a microphone array based on independent
More informationOn the Optimality of WLAN Location Determination Systems
On the Optimality of WLAN Location Determination Systems Moustafa Youssef Department of Computer Science University of Maryland College Park, Maryland 20742 Email: moustafa@cs.umd.edu Ashok Agrawala Department
More informationanalysis of GPS total electron content Empirical orthogonal function (EOF) storm response 2016 NEROC Symposium M. Ruohoniemi (3)
Empirical orthogonal function (EOF) analysis of GPS total electron content storm response E. G. Thomas (1), A. J. Coster (2), S.-R. Zhang (2), R. M. McGranaghan (1), S. G. Shepherd (1), J. B. H. Baker
More informationModel-based Design of Coordinated Traffic Controllers
Model-based Design of Coordinated Traffic Controllers Roopak Sinha a, Partha Roop b, Prakash Ranjitkar c, Junbo Zeng d, Xingchen Zhu e a Lecturer, b,c Senior Lecturer, d,e Student a,b,c,d,e Faculty of
More informationSegment based Traffic Information Estimation Method Using Cellular Network Data
Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems Vienna, Austria, September 13-16, 2005 WA1.4 Segment based Traffic Information Estimation Method Using Cellular
More informationOBJECTIVE OF THE BOOK ORGANIZATION OF THE BOOK
xv Preface Advancement in technology leads to wide spread use of mounting cameras to capture video imagery. Such surveillance cameras are predominant in commercial institutions through recording the cameras
More informationENTERPRISE Transportation Pooled Fund Study TPF-5 (231)
ENTERPRISE Transportation Pooled Fund Study TPF-5 (231) Impacts of Traveler Information on the Overall Network FINAL REPORT Prepared by September 2012 i 1. Report No. ENT-2012-2 2. Government Accession
More informationFreeway Performance Measurement System (PeMS)
CALIFORNIA PATH PROGRAM INSTITUTE OF TRANSPORTATION STUDIES UNIVERSITY OF CALIFORNIA, BERKELEY Freeway Performance Measurement System (PeMS) Chao Chen California PATH Research Report UCB-ITS-PRR-2003-22
More informationAn Approach to Korean License Plate Recognition Based on Vertical Edge Matching
An Approach to Korean License Plate Recognition Based on Vertical Edge Matching Mei Yu and Yong Deak Kim Ajou University Suwon, 442-749, Korea Abstract License plate recognition (LPR) has many applications
More informationPrediction of LOS based Path-Loss in Urban Wireless Sensor Network Environments
Prediction of LOS based Path-Loss in Urban Wireless Sensor Network Environments Myungnam Bae, Inhwan Lee, Hyochan Bang ETRI, IoT Convergence Research Department, 218 Gajeongno, Yuseong-gu, Daejeon, 305-700,
More informationAssessing the Performance of Integrated Corridor Management (ICM) Strategies
Assessing the Performance of Integrated Corridor Management (ICM) Strategies Matt Burt, Battelle Research and Evaluation Session, NATMEC 2012 June 7, 2012 1 Presentation Outline The U.S. DOT ICM Program
More informationAnalysis of Temporal Logarithmic Perspective Phenomenon Based on Changing Density of Information
Analysis of Temporal Logarithmic Perspective Phenomenon Based on Changing Density of Information Yonghe Lu School of Information Management Sun Yat-sen University Guangzhou, China luyonghe@mail.sysu.edu.cn
More informationDevelopment of 24 GHz-band High Resolution Multi-Mode Radar
Special Issue Automobile Electronics Development of 24 GHz-band High Resolution Multi-Mode Radar Daisuke Inoue*, Kei Takahashi*, Hiroyasu Yano*, Noritaka Murofushi*, Sadao Matsushima*, Takashi Iijima*
More informationVision Based Intelligent Traffic Analysis System for Accident Detection and Reporting System
Vision Based Intelligent Traffic Analysis System for Accident Detection and Reporting System 1 Gayathri Elumalai, 2 O.S.P.Mathanki, 3 S.Swetha 1, 2, 3 III Year, Student, Department of CSE, Panimalar Institute
More informationPerformance Evaluation of a Mixed Vehicular Network with CAM-DCC and LIMERIC Vehicles
Performance Evaluation of a Mixed Vehicular Network with CAM-DCC and LIMERIC Vehicles Bin Cheng Joint work with Ali Rostami, Marco Gruteser WINLAB, Rutgers University, USA Gaurav Bansal, John B. Kenney
More informationFinal Version of Micro-Simulator
Scalable Data Analytics, Scalable Algorithms, Software Frameworks and Visualization ICT-2013 4.2.a Project FP6-619435/SPEEDD Deliverable D8.4 Distribution Public http://speedd-project.eu Final Version
More information