Elżbieta Macioszek & Grzegorz Sierpiński 
Present Approach to Traffic Flow Theory and Research in Civil and Transportation Engineering [PDF ebook] 

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This book presents many valuable tips for making decisions related to traffic flow in the transport networks. The knowledge base in practical examples, as well as the decision support systems described in this book, finds interest among people who face the daily challenge of searching for solutions to the problems of contemporary transport networks and systems. The publication is therefore addressed to local authorities related to the planning and development of development strategies for selected areas with regard to transport (both in the urban and regional dimension) and to representatives of business and industry, as people directly involved in the implementation of traffic engineering solutions. The tips contained in individual sections of the publication allow to look at a given problem in an advanced way and facilitate the selection of the appropriate strategy (among others, in relation to the evaluation of BEV and FCHEV electric vehicles in the creation of a sustainable transport systems, development of ecological public transport on the example of selected cities, impact of drivers’ waiting time on the gap acceptance at median, uncontrolled T-intersections). In turn, due to a new approach to theoretical models (including, inter alia, the application of genetic algorithms for the planning of urban rail transportation system, comprehensive estimate of life cycle costs of new technical systems using reliability verification algorithm, application and comparison of machine learning algorithms in traffic signals prediction), the publication also interests scientists and researchers carrying out research in this area.

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Langue Anglais ● Format PDF ● Pages 202 ● ISBN 9783030933708 ● Taille du fichier 28.5 MB ● Éditeur Elżbieta Macioszek & Grzegorz Sierpiński ● Maison d’édition Springer International Publishing ● Lieu Cham ● Pays CH ● Publié 2022 ● Téléchargeable 24 mois ● Devise EUR ● ID 8261575 ● Protection contre la copie DRM sociale

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