Integrated Method for Online Calibration of Real-Time Traffic Network Management Systems

2015 ◽  
Vol 2528 (1) ◽  
pp. 106-115 ◽  
Author(s):  
Hossein Hashemi ◽  
Khaled Abdelghany
Keyword(s):  
Network Management ◽  
Real Time ◽  
Simulation Models ◽  
Calibration Method ◽  
Propagation Model ◽  
Traffic Network ◽  
Operational Conditions ◽  
Online Calibration ◽  
Integrated Method ◽  
Real Time Traffic

This paper presents an integrated method for online calibration of realtime traffic network simulation models. The method integrates a time-dependent demand adjustment module and a link-based traffic flow propagation model calibration module. These modules use available realtime traffic observations to minimize inconsistency between the model estimation results and real-world observations. The modules are integrated into a real-time traffic network management system that was developed for the US-75 corridor in Dallas, Texas. Results illustrate that the online calibration method is effective in enhancing the model's consistency in the different operational conditions.

Toward an Autonomic Architecture for Real-Time Traffic Network Management

2012 ◽  
Vol 16 (2) ◽  
pp. 45-59 ◽  
Author(s):  
Hamideh Etemadnia ◽  
Khaled Abdelghany ◽  
Salim Hariri
Keyword(s):  
Network Management ◽  
Real Time ◽  
Traffic Network ◽  
Real Time Traffic

scholarly journals An Online Calibration Method for a Galvanometric System Based on Wavelet Kernel ELM

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1353 ◽  
Author(s):  
Wugang Zhang ◽  
Wei Guo ◽  
Chuanwei Zhang ◽  
Shuanfeng Zhao
Keyword(s):  
Real Time ◽  
Calibration Method ◽  
System Structure ◽  
Data Sets ◽  
Position Measurement ◽  
Online Calibration ◽  
Time Performance ◽  
Calibration Methods ◽  
Testing Data ◽  
Mean Square Errors

The online calibration method of a two-dimensional (2D) galvanometer requires both high precision and better real-time performance to meet the needs of moving target position measurement, which presents some challenges for traditional calibration methods. In this paper, a new online calibration method is proposed using the wavelet kernel extreme learning machine (KELM). Firstly, a system structure is created and its experiment setup is established. The online calibration method is then analyzed based on a wavelet KELM algorithm. Finally, the acquisition methods of the training data are set, two groups of testing data sets are presented, and the verification method is described. The calibration effects of the existing methods and wavelet KELM methods are compared in terms of both accuracy and speed. The results show that, for the two testing data sets, the root mean square errors (RMSE) of the Mexican Hat wavelet KELM are reduced by 16.4% and 38.6%, respectively, which are smaller than that of the original ELM, and the standard deviations (Sd) are reduced by 19.2% and 36.6%, respectively, indicating the proposed method has better generalization and noise suppression performance for the nonlinear samples of the 2D galvanometer. Although the online operation time of KELM is longer than ELM, due to the complexity of the wavelet kernel, it still has better real-time performance.

Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city

2020 ◽  
Vol 108 ◽  
pp. 976-986 ◽  
Author(s):  
Jiachen Yang ◽  
Yurong Han ◽  
Yafang Wang ◽  
Bin Jiang ◽  
Zhihan Lv ◽  
...  
Keyword(s):  
Real Time ◽  
Smart City ◽  
Traffic Network ◽  
Real Time Traffic ◽  
Clustering Model

Linear Bus Holding Model for Real-Time Traffic Network Control

2015 ◽  
pp. 303-319
Author(s):  
Leonardo G. Hernández-Landa ◽  
Miguel L. Morales-Marroquín ◽  
Romeo Sánchez Nigenda ◽  
Yasmín Á. Ríos-Solís
Keyword(s):  
Real Time ◽  
Network Control ◽  
Traffic Network ◽  
Real Time Traffic
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