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000921824 019__ $$a1099687621
000921824 020__ $$a9783030103743$$q(electronic book)
000921824 020__ $$a3030103749$$q(electronic book)
000921824 020__ $$z9783030103736
000921824 020__ $$z3030103730
000921824 0247_ $$a10.1007/978-3-030-10374-3$$2doi
000921824 035__ $$aSP(OCoLC)on1096433525
000921824 035__ $$aSP(OCoLC)1096433525$$z(OCoLC)1099687621
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000921824 049__ $$aISEA
000921824 050_4 $$aTE153
000921824 08204 $$a388.10285$$223
000921824 1001_ $$aPradhan, Biswajeet,$$eauthor.
000921824 24510 $$aLaser scanning systems in highway and safety assessment :$$banalysis of highway geometry and safety using LiDAR /$$cBiswajeet Pradhan, Maher Ibrahim Sameen.
000921824 264_1 $$aCham, Switzerland :$$bSpringer,$$c2020.
000921824 300__ $$a1 online resource (xv, 157 pages).
000921824 336__ $$atext$$btxt$$2rdacontent
000921824 337__ $$acomputer$$bc$$2rdamedia
000921824 338__ $$aonline resource$$bcr$$2rdacarrier
000921824 4901_ $$aAdvances in science, technology & innovation,$$x2522-8714
000921824 504__ $$aIncludes bibliographical references.
000921824 5050_ $$aIntroduction to Laser Scanning Technology -- Road Geometric Modeling Using Laser-Scanning Data -- Optimizing support vector machine and ensemble trees using the Taguchi method for automatic road network extraction -- Road Geometric Modeling Using a Novel Hierarchical Approach -- Introduction to Neural Networks -- Traffic Accidents Predictions with Neural Networks: A Review -- Applications of Deep Learning in Severity Prediction of Traffic Accidents -- Accident Modelling Using Feedforward Neural Networks -- Accident Severity Prediction with Convolutional Neural Networks -- Injury Severity Prediction Using Recurrent Neural Networks -- Improving Traffic Accident Prediction Models with Transfer Learning -- A Comparative Study between Neural Networks, Support Vector Machine, and Logistic Regression for Accident Predictions -- Estimation of Accident Factor Importance in Neural Network Models.
000921824 506__ $$aAccess limited to authorized users.
000921824 520__ $$aThis book aims to promote the core understanding of a proper modelling of road traffic accidents by deep learning methods using traffic information and road geometry delineated from laser scanning data. The first two chapters of the book introduce the reader to laser scanning technology with creative explanation and graphical illustrations, review and recent methods of extracting geometric road parameters. The next three chapters present different machine learning and statistical techniques applied to extract road geometry information from laser scanning data. Chapters 6 and 7 present methods for modelling roadside features and automatic road geometry identification in vector data. After that, this book goes on reviewing methods used for road traffic accident modelling including accident frequency and injury severity of the traffic accident (Chapter 8). Then, the next chapter explores the details of neural networks and their performance in predicting the traffic accidents along with a comparison with common data mining models. Chapter 10 presents a novel hybrid model combining extreme gradient boosting and deep neural networks for predicting injury severity of road traffic accidents. This chapter is followed by deep learning applications in modelling accident data using feed-forward, convolutional, recurrent neural network models (Chapter 11). The final chapter (Chapter 12) presents a procedure for modelling traffic accident with little data based on the concept of transfer learning. This book aims to help graduate students, professionals, decision makers, and road planners in developing better traffic accident prediction models using advanced neural networks.
000921824 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 12, 2019).
000921824 650_0 $$aRoads$$xRemote sensing.
000921824 650_0 $$aOptical radar.
000921824 7001_ $$aSameen, Maher Ibrahim,$$eauthor.
000921824 77608 $$iPrint version: $$z3030103730$$z9783030103736$$w(OCoLC)1076228264
000921824 830_0 $$aAdvances in science, technology & innovation.
000921824 852__ $$bebk
000921824 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-10374-3$$zOnline Access$$91397441.1
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000921824 980__ $$aBIB
000921824 982__ $$aEbook
000921824 983__ $$aOnline
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