Temat: traffic parameters - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Estimation of the bus delay at the stopping point on the base of traffic parameters
Autorzy:: Horbachov, P.
Naumov, V.
Kolii, O.
Powiązania:: https://bibliotekanauki.pl/articles/223852.pdf
Data publikacji:: 2015
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: urban transport
traffic parameters
time delays
mathematical model
stopping point
transport miejski
parametry ruchu
opóźnienia czasowe
model matematyczny
punkt zatrzymania
Opis:: Contemporary methods of spatial planning of urban transport systems provide for designers enough opportunities in selecting the placement of stopping points for public transport. However in every city there exist very intense sections of the road network with a small width of the roadway. In these sections there is no opportunity to allocate special lanes for public transport. If the stop pockets on such street exist, there appear traffic conflicts when buses depart from the stopping point. Authors propose theoretical model for estimation of the bus delay at the stopping point on the base of traffic parameters. Use of the proposed model allows reducing amount of field surveys while grounding the decisions about rational variant of allocation of the bus stopping points. The paper describes some experimental results obtained with the use of the proposed model while field surveys at the most loaded streets in the central part of Kharkiv (Ukraine).
Źródło:: Archives of Transport; 2015, 35, 3; 15-25
0866-9546
2300-8830
Pojawia się w:: Archives of Transport
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Traffic fatalities prediction based on support vector machine
Autorzy:: Li, T.
Yang, Y.
Wang, Y.
Chen, C.
Yao, J.
Powiązania:: https://bibliotekanauki.pl/articles/223743.pdf
Data publikacji:: 2016
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: traffic accident
support vector machine
SVM
particle swarm optimization (PSO)
PSO
prediction model
optimal parameters
wypadek drogowy
Particle Swarm Optimization
model prognostyczny
optymalne parametry
Opis:: To effectively predict traffic fatalities and promote the friendly development of transportation, a prediction model of traffic fatalities is established based on support vector machine (SVM). As the prediction accuracy of SVM largely depends on the selection of parameters, Particle Swarm Optimization (PSO) is introduced to find the optimal parameters. In this paper, small sample and nonlinear data are used to predict fatalities of traffic accident. Traffic accident statistics data of China from 1981 to 2012 are chosen as experimental data. The input variables for predicting accident are highway mileage, vehicle number and population size while the output variables are traffic fatality. To verify the validity of the proposed prediction method, the back-propagation neural network (BPNN) prediction model and SVM prediction model are also used to predict the traffic fatalities. The results show that compared with BPNN prediction model and SVM model, the prediction model of traffic fatalities based on PSO-SVM has higher prediction precision and smaller errors. The model can be more effective to forecast the traffic fatalities. And the method using particle swarm optimization algorithm for parameter optimization of SVM is feasible and effective. In addition, this method avoids overcomes the problem of “over learning” in neural network training progress.
Źródło:: Archives of Transport; 2016, 39, 3; 21-30
0866-9546
2300-8830
Pojawia się w:: Archives of Transport
Dostawca treści:: Biblioteka Nauki

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