scholarly journals Location Extraction and Prediction Method Based on Floating Car Spatial-Temporal Trajectory

2020 ◽  
Vol 9 (5) ◽  
pp. 302
Author(s):  
Shaoming Pan ◽  
Ziying Li ◽  
Yanwen Chong
Keyword(s):  
Prediction Model ◽  
Dynamic Environment ◽  
Prediction Method ◽  
Dynamic Parameter ◽  
Location Prediction ◽  
Estimation Of Parameters ◽  
Extraction Algorithm ◽  
Dynamic Parameter Estimation ◽  
And Behavior ◽  
Temporal Trajectory

Predicting the next important location by mining the user’s historical spatial-temporal trajectory can be done for behavioral analysis and path planning. Since extracting the important location precisely is the premise of next location prediction, an enhanced location extraction algorithm is proposed to meet the requirements of dynamic trajectory via dynamic parameter estimation. To realize the estimation of parameters dynamically, the differences of floating car velocity in terms of spatial distribution and behavior in time distribution are considered in the location extraction algorithm. Then, an improved recurrent neural network (RNN) model is designed to mine the variation law of floating car trajectories to improve the accuracy of important location prediction under different conditions. Different from the traditional prediction model considering only the constraint of distance, the attention mechanism and semantic information are considered simultaneously by the proposed prediction model. Finally, the floating car trajectory of Beijing is selected for our experiments, and the results show that the proposed location extraction algorithm can meet the requirements of a dynamic environment and our model achieves high prediction accuracy.

scholarly journals UB-LSTM: A Trajectory Prediction Method Combined with Vehicle Behavior Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Haipeng Xiao ◽  
Chaoqun Wang ◽  
Zhixiong Li ◽  
Rendong Wang ◽  
Cao Bo ◽  
...  
Keyword(s):  
Prediction Model ◽  
Trajectory Optimization ◽  
Dynamic Environment ◽  
Prediction Method ◽  
Trajectory Prediction ◽  
Behavior Recognition ◽  
Recognition Model ◽  
Bidirectional Lstm ◽  
Vehicle Trajectory ◽  
Actual Trajectory

In order to make an accurate prediction of vehicle trajectory in a dynamic environment, a Unidirectional and Bidirectional LSTM (UB-LSTM) vehicle trajectory prediction model combined with behavior recognition is proposed, and then an acceleration trajectory optimization algorithm is proposed. Firstly, the interactive information with the surrounding vehicles is obtained by calculation, then the vehicle behavior recognition model is established by using LSTM, and the vehicle information is input into the behavior recognition model to identify vehicle behavior. Then, the trajectory prediction model is established based on Unidirectional and Bidirectional LSTM, and the identified vehicle behavior and the input information of the behavior recognition model are input into the trajectory prediction model to predict the horizontal and vertical speed and coordinates of the vehicle in the next 3 seconds. Experiments are carried out with NGSIM data sets, and the experimental results show that the mean square error (MSE) between the predicted trajectory and the actual trajectory obtained by this method is 0.124, which is 97.2% lower than that of the method that does not consider vehicle behavior and directly predicts the trajectory. The test loss is 0.000497, which is 95.68% lower than that without considering vehicle behavior. The predicted trajectory is obviously optimized, closer to the actual trajectory, and the performance is more stable.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

scholarly journals Nonsmooth Recursive Identification of Sandwich Systems with Backlash-Like Hysteresis

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ruili Dong ◽  
Yonghong Tan ◽  
Hui Chen ◽  
Yangqiu Xie
Keyword(s):  
Estimation Algorithm ◽  
Dynamic Parameter ◽  
Bundle Method ◽  
Recursive Identification ◽  
Identification Algorithm ◽  
Nonsmooth Problem ◽  
Positioning Stage ◽  
Dynamic Parameter Estimation ◽  
Estimation Scheme ◽  
Sandwich System

A recursive gradient identification algorithm based on the bundle method for sandwich systems with backlash-like hysteresis is presented in this paper. In this method, a dynamic parameter estimation scheme based on a subgradient is developed to handle the nonsmooth problem caused by the backlash embedded in the system. The search direction of the algorithm is estimated based on the so-called bundle method. Then, the convergence of the algorithm is discussed. After that, simulation results on a nonsmooth sandwich system are presented to validate the proposed estimation algorithm. Finally, the application of the proposed method to anX-Ymoving positioning stage is illustrated.

scholarly journals Developing an Enhanced Short-Range Railroad Track Condition Prediction Model for Optimal Maintenance Scheduling

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Xu ◽  
Chuanjun Jia ◽  
Ye Li ◽  
Quanxin Sun ◽  
Rengkui Liu
Keyword(s):  
Prediction Model ◽  
Short Range ◽  
Prediction Method ◽  
Maintenance Scheduling ◽  
Railroad Track ◽  
Track Geometry ◽  
Track Maintenance ◽  
Track Condition ◽  
Optimal Maintenance ◽  
Process Work

As railroad infrastructure becomes older and older and rail transportation is developing towards higher speed and heavier axle, the risk to safe rail transport and the expenses for railroad maintenance are increasing. The railroad infrastructure deterioration (prediction) model is vital to reducing the risk and the expenses. A short-range track condition prediction method was developed in our previous research on railroad track deterioration analysis. It is intended to provide track maintenance managers with two or three months of track condition in advance to schedule track maintenance activities more smartly. Recent comparison analyses on track geometrical exceptions calculated from track condition measured with track geometry cars and those predicted by the method showed that the method fails to provide reliable condition for some analysis sections. This paper presented the enhancement to the method. One year of track geometry data for the Jiulong-Beijing railroad from track geometry cars was used to conduct error analyses and comparison analyses. Analysis results imply that the enhanced model is robust to make reliable predictions. Our in-process work on applying those predicted conditions for optimal track maintenance scheduling is discussed in brief as well.

A Hardware Implementation of an Online Frequency Dynamic Parameter Estimation

2021 ◽  
Author(s):  
J. Quiroz ◽  
R. Perez ◽  
H. Chavez ◽  
Julia Matevosyan ◽  
Felix Rafael Segundo Sevilla
Keyword(s):  
Parameter Estimation ◽  
Hardware Implementation ◽  
Dynamic Parameter ◽  
Dynamic Parameter Estimation

Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

scholarly journals A geographical location prediction method based on continuous time series Markov model

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0207063 ◽  
Author(s):  
Yongping Du ◽  
Chencheng Wang ◽  
Yanlei Qiao ◽  
Dongyue Zhao ◽  
Wenyang Guo
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Continuous Time ◽  
Geographical Location ◽  
Prediction Method ◽  
Location Prediction

scholarly journals A single-station prediction model as a contribution to instantaneous mapping

1994 ◽  
Vol 37 (2) ◽  
Author(s):  
I. Stanislawska
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Method ◽  
Physical Processes ◽  
The Past ◽  
Ionospheric Station ◽  
Single Station ◽  
Daily Forecast

The paper presents two opposite approaches for single-station prediction and forecast. Both methods are based on different assumptions of physical processes in the ionosphere and need the different set of incoming data. Different heliogeophysical data, mainly f0F2 parameters from the past were analyzed for f0F2 obtaining for the requested period ahead. In the first method - the autocovariance prediction method - the time series of f0F2 from one station are used for daily forecast at that point. The second method may be used for obtaining f0F2 not only at the particular ionospheric station, but also at any point within the considered area.

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