LEADER 05812nam 22007455 450 001 9910366654803321 005 20200706172558.0 010 $a3-030-10374-9 024 7 $a10.1007/978-3-030-10374-3 035 $a(CKB)4100000007823566 035 $a(DE-He213)978-3-030-10374-3 035 $a(MiAaPQ)EBC5941252 035 $a(PPN)235670448 035 $a(EXLCZ)994100000007823566 100 $a20190402d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLaser Scanning Systems in Highway and Safety Assessment $eAnalysis of Highway Geometry and Safety Using LiDAR /$fby Biswajeet Pradhan, Maher Ibrahim Sameen 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XV, 157 p.) 225 1 $aAdvances in Science, Technology & Innovation, IEREK Interdisciplinary Series for Sustainable Development,$x2522-8714 311 $a3-030-10373-0 320 $aIncludes bibliographical references. 327 $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. 330 $aThis book aims to promote the core understanding of a proper modelling of road traffic accidents by deep learning methods using traffic information and road geometry delineated from laser scanning data. The first two chapters of the book introduce the reader to laser scanning technology with creative explanation and graphical illustrations, review and recent methods of extracting geometric road parameters. The next three chapters present different machine learning and statistical techniques applied to extract road geometry information from laser scanning data. Chapters 6 and 7 present methods for modelling roadside features and automatic road geometry identification in vector data. After that, this book goes on reviewing methods used for road traffic accident modelling including accident frequency and injury severity of the traffic accident (Chapter 8). Then, the next chapter explores the details of neural networks and their performance in predicting the traffic accidents along with a comparison with common data mining models. Chapter 10 presents a novel hybrid model combining extreme gradient boosting and deep neural networks for predicting injury severity of road traffic accidents. This chapter is followed by deep learning applications in modelling accident data using feed-forward, convolutional, recurrent neural network models (Chapter 11). The final chapter (Chapter 12) presents a procedure for modelling traffic accident with little data based on the concept of transfer learning. This book aims to help graduate students, professionals, decision makers, and road planners in developing better traffic accident prediction models using advanced neural networks. 410 0$aAdvances in Science, Technology & Innovation, IEREK Interdisciplinary Series for Sustainable Development,$x2522-8714 606 $aTransportation engineering 606 $aTraffic engineering 606 $aEnvironmental management 606 $aRemote sensing 606 $aNeural networks (Computer science)  606 $aSociophysics 606 $aEconophysics 606 $aTransportation Technology and Traffic Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T23120 606 $aEnvironmental Management$3https://scigraph.springernature.com/ontologies/product-market-codes/U17009 606 $aRemote Sensing/Photogrammetry$3https://scigraph.springernature.com/ontologies/product-market-codes/J13010 606 $aMathematical Models of Cognitive Processes and Neural Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/M13100 606 $aData-driven Science, Modeling and Theory Building$3https://scigraph.springernature.com/ontologies/product-market-codes/P33030 615 0$aTransportation engineering. 615 0$aTraffic engineering. 615 0$aEnvironmental management. 615 0$aRemote sensing. 615 0$aNeural networks (Computer science) . 615 0$aSociophysics. 615 0$aEconophysics. 615 14$aTransportation Technology and Traffic Engineering. 615 24$aEnvironmental Management. 615 24$aRemote Sensing/Photogrammetry. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aData-driven Science, Modeling and Theory Building. 676 $a388.10285 676 $a388.10285 700 $aPradhan$b Biswajeet$4aut$4http://id.loc.gov/vocabulary/relators/aut$0961540 702 $aIbrahim Sameen$b Maher$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910366654803321 996 $aLaser Scanning Systems in Highway and Safety Assessment$92179942 997 $aUNINA