| Title | Predviđanje količine prometa zasnovano na neuronskoj mreži |
| Author | Matea Ravnjak |
| Mentor(s) | Edouard Ivanjko (thesis advisor)
|
| Abstract | U ovom radu kreirana je neuronska mreža s ciljem predviđanja količine prometa na transportnoj
mreži. Pri tome se misli na cestovni transport (broj vozila) te na telekomunikacijski promet,
odnosno prijenos podataka. Na osnovu ulaznih varijabli prikupljenih tijekom određenog vremena,
predviđa se broj vozila odnosno količina podatkovnog prometa za idući vremenski interval.
Korištena je višeslojna struktura neuronske mreže, a rezultati pokazuju da je neuronska mreža
sposobna prilikom učenja aproksimirati funkciju ulaznih i izlaznih varijabli uz visoku točnost. |
| Keywords | artificial intelligence neural networks prediction transport systems multi-layer architecture road network telecommunication network |
| Parallel title (English) | Neural network based traffic prediction |
| Committee Members | Edouard Ivanjko
Štefica Mrvelj
Sadko Mandžuka
Hrvoje Gold
|
| Granter | University of Zagreb
Faculty of Transport and Traffic Sciences |
| Lower level organizational units | Division of Intelligent Transport Systems and Logistics
Department of Intelligent Transport Systems |
| Place | Zagreb |
| State | Croatia |
| Scientific field, discipline, subdiscipline | TECHNICAL SCIENCES
Traffic and Transport Technology
Postal and Telecommunications Traffic
|
| Study programme type | university |
| Study level | graduate |
| Study programme | Road Transport, Railway Transport, Water Transport, Air Transport, Postal Transport, Information and Communications Traffic, Urban Transport |
| Academic title abbreviation | mag. ing. traff. |
| Genre | master's thesis |
| Language | Croatian |
| Defense date | 2015-09-24 |
| Parallel abstract (English) | In this paper, neural network is created in order to predict the amount of traffic in the transport
networks. This refers to road transport (number of vehicles) and telecommunication traffic, i.e. data
transfer. Based on the input variables collected during a certain time, neutral network made
predictions for number of vehicles and the amount of data traffic for the next time interval.
Multilayer structure neutral network is used, and the results show that the neural network is capable
to learn approximate function of input and output variables with high accuracy. |
| Parallel keywords (Croatian) | umjetna inteligencija neuronske mreže predviđanje transportni sustavi višeslojna arhitektura cestovna mreža telekomunikacijska mreža |
| Resource type | text |
| Access condition | Open access |
| Terms of use |  |
| URN:NBN | https://urn.nsk.hr/urn:nbn:hr:119:749606 |
| Committer | Mojca Brenko-Puzak |
