A Bus Passenger Flow Estimation Method Based on POI Data and AFC Data Fusion

2020 ◽  
pp. 352-367
Author(s):  
Yuexiao Cai ◽  
Yunlong Zhao ◽  
Jinqian Yang ◽  
Changxin Wang
Keyword(s):  
Data Fusion ◽  
Estimation Method ◽  
Flow Estimation ◽  
Passenger Flow

A Real-Time Passenger Flow Estimation and Prediction Method for Urban Bus Transit Systems

2017 ◽  
Vol 18 (11) ◽  
pp. 3168-3178 ◽  
Author(s):  
Jun Zhang ◽  
Dayong Shen ◽  
Lai Tu ◽  
Fan Zhang ◽  
Chengzhong Xu ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Method ◽  
Transit Systems ◽  
Flow Estimation ◽  
Bus Transit ◽  
Estimation And Prediction ◽  
Passenger Flow ◽  
Urban Bus

Research on data fusion method of multi-source complex system

2021 ◽  
Author(s):  
Yuxiang Cai
Keyword(s):  
Data Fusion ◽  
Estimation Method ◽  
Fuzzy Neural ◽  
Accurate Perception ◽  
Value Estimation ◽  
Null Value ◽  
Neural Network Algorithm ◽  
Basic Technology ◽  
Power And Energy ◽  
The Internet Of Things

Multi source fusion of data collected by various sensors to realize accurate perception is the key basic technology of the Internet of things. At present, there are many problems in the fusion of various kinds of data collected by sensors, such as more noise and more null values. In this paper, the fuzzy neural network algorithm is proposed to establish the model, combined with the Delphi method and the null value estimation method based on the prediction value to construct the data fusion system. This method has rich application scenarios in the construction of IOT system in the field of power and energy.

A new optical flow estimation method in joint EO/IR video surveillance

2007 ◽  
Author(s):  
Hong Man ◽  
Robert J. Holt ◽  
Jing Wang ◽  
Rainer Martini ◽  
Ravi Netravali ◽  
...  
Keyword(s):  
Optical Flow ◽  
Video Surveillance ◽  
Estimation Method ◽  
Flow Estimation ◽  
Optical Flow Estimation

Federated IMM-UKF Algorithm for Multi-Sensor Data Fusion

2013 ◽  
Vol 753-755 ◽  
pp. 2117-2120 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Data Fusion ◽  
Nonlinear Filtering ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Cost Effective ◽  
Estimation Algorithm ◽  
Sensor Data ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model

The accuracy of multi-sensor navigational data fusion by federated Kalman filter will be reduced in condition that the systems dynamics model is nonlinear and the noise statistical properties are unknown. To address this problem, a federated Interacting Multiple Model-Unscented Kalman Filteing (IMM-UKF) algorithm is presented. The UKF is a nonlinear estimation method which can achieve the accuracy at least to the second-order. The IMM estimation algorithm is one of the cost-effective adaptive estimation algorithm for systems involving parametric changes. The combination of IMM with UKF could deal with the problem of nonlinear filtering with uncertain noise. Simulation results show that the method can improve the accuracy of INS/GPS/odometer integrated navigation.

scholarly journals IN-CYLINDER MASS FLOW ESTIMATION AND MANIFOLD PRESSURE DYNAMICS FOR STATE PREDICTION IN SI ENGINES

2014 ◽  
Vol 54 (3) ◽  
pp. 240-247 ◽  
Author(s):  
Wojnar Sławomir ◽  
Boris Rohal-Ilkiv ◽  
Peter Šimončic ◽  
Marek Honek ◽  
Csambál Jozef
Keyword(s):  
Control Strategies ◽  
Estimation Method ◽  
Mean Value ◽  
Intake Manifold ◽  
Flow Estimation ◽  
Engine Model ◽  
Pressure Dynamics ◽  
Estimation And Control ◽  
Cylinder Flow ◽  
And Control

The aim of this paper is to present a simple model of the intake manifold dynamics of a spark ignition (SI) engine and its possible application for estimation and control purposes. We focus on pressure dynamics, which may be regarded as the foundation for estimating future states and for designing model predictive control strategies suitable for maintaining the desired air fuel ratio (AFR). The flow rate measured at the inlet of the intake manifold and the in-cylinder flow estimation are considered as parts of the proposed model. In-cylinder flow estimation is crucial for engine control, where an accurate amount of aspired air forms the basis for computing the manipulated variables. The solutions presented here are based on the mean value engine model (MVEM) approach, using the speed-density method. The proposed in-cylinder flow estimation method is compared to measured values in an experimental setting, while one-step-ahead prediction is illustrated using simulation results.

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