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Titlebook: Intelligent Transport Systems – From Research and Development to the Market Uptake; First International Tatiana Ková?iková,?ubo? Buzna,Nat

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41#
發(fā)表于 2025-3-28 16:10:50 | 只看該作者
1867-8211 Helsinki, Finland, in November 2017. The 30 revised full papers were selected from 47 submissions and are organized in 6 thematic sessions on planning and sustainable transport and smart cities, intelligent rail transport systems, transport modelling and simulation & big data application, ITS safet
42#
發(fā)表于 2025-3-28 20:17:48 | 只看該作者
Conference proceedings 2018 Finland, in November 2017. The 30 revised full papers were selected from 47 submissions and are organized in 6 thematic sessions on planning and sustainable transport and smart cities, intelligent rail transport systems, transport modelling and simulation & big data application, ITS safety and secu
43#
發(fā)表于 2025-3-29 02:43:31 | 只看該作者
Impact of Public Transport Priority on Traffic in Chosen Part of the City of Martinre mostly in city centres. Inappropriate conditions for the transport of persons cause congestion, and hence the time losses of all users of means of transport. For this reason, it is necessary to ensure quality, fast, safe and dynamic transport for people. There are several ways to achieve this and one is the public transport priority.
44#
發(fā)表于 2025-3-29 04:13:02 | 只看該作者
45#
發(fā)表于 2025-3-29 08:29:16 | 只看該作者
46#
發(fā)表于 2025-3-29 15:26:33 | 只看該作者
47#
發(fā)表于 2025-3-29 15:54:11 | 只看該作者
1867-8211 g and sustainable transport and smart cities, intelligent rail transport systems, transport modelling and simulation & big data application, ITS safety and security, cooperative ITS and autonomous driving, and intelligent traffic management.978-3-319-93709-0978-3-319-93710-6Series ISSN 1867-8211 Series E-ISSN 1867-822X
48#
發(fā)表于 2025-3-29 21:31:18 | 只看該作者
49#
發(fā)表于 2025-3-30 02:55:30 | 只看該作者
50#
發(fā)表于 2025-3-30 05:56:23 | 只看該作者
Vehicles Recognition Based on Point Cloud Representationted in long duration of experiment (<90?h). Therefore, other experiments were done with filtered dataset. In filtered dataset, best result in SVM was 79% with RBF kernel. For the next experiment, CNN was used. With data augmentation the result was 80%, without 89%.
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