An Unscented Kalman Filter-Based Method for Reconstructing Vehicle Trajectories at Signalized Intersections

On-board data of detected vehicles play a critical role in the management of urban road traffic operation and the estimation of traffic status. Unfortunately, due to limitations of technology and privacy issues, the sampling frequency of the detected vehicle data is low and the coverage is also limited. Continuous vehicle trajectories cannot be obtained. To overcome the above problems, this paper proposes an unscented Kalman filter (UKF)-based method to reconstruct the trajectories at signalized intersections using sparse probe data of vehicles. We first divide the intersection into multiple road sections and use a quadratic programming problem to estimate the travel time of each section. The weight of each initial possible trajectory is calculated separately, and the trajectory is updated using the unscented Kalman filter (UKF); then, the trajectory between two updates is also obtained accordingly. Finally, the method is applied to the actual scenario provided by the NGSIM data and compared with the real trajectory. The mean absolute error (MAE) is adopted to evaluate the accuracy of the proposed trajectory reconstruction. Sensitivity analysis is provided in order to provide the requirement of sampling frequency to obtain highly accurate reconstructed vehicle trajectories under this method. The results demonstrate the applicability of the technique to the signalized intersection. Therefore, the method enables us to obtain richer and more accurate trajectory data information, providing a strong prior basis for future urban road traffic management and scholars using trajectory data for various studies.

Download Full-text

A hybrid method for lithium-ion batteries state-of-charge estimation based on gated recurrent unit neural network and an adaptive unscented Kalman filter

Journal of Electrochemical Energy Conversion and Storage ◽

10.1115/1.4053361 ◽

2021 ◽

pp. 1-36

Author(s):

Shuai Xu ◽

Fei Zhou ◽

Yucheng Liu

Keyword(s):

Neural Network ◽

Kalman Filter ◽

Hybrid Method ◽

Unscented Kalman Filter ◽

Absolute Error ◽

State Of Charge ◽

Estimation Accuracy ◽

Adaptive Unscented Kalman Filter ◽

Battery State Of Charge ◽

Gated Recurrent Unit

Abstract Among the battery state of charge estimation methods, the Kalman-based filter algorithms are sensitive to the battery model while the neural network-based algorithms are decided by hyperparameters. In this paper, a hybrid approach composed of a gated recurrent unit neural network and an adaptive unscented Kalman filter method is proposed. A gated recurrent unit neural network is first used to acquire the nonlinear relationship between the battery state of charge and battery measurement signals, and then an adaptive unscented Kalman filter is utilized to filter out the output noise of the neural network to further improve estimation accuracy. The hybrid method avoids the establishment of accurate battery models and the search for optimal hyperparameters. The data of dynamical street test and US06 test are used as training dataset and validation dataset, respectively, while the data collected from the tests under federal urban driving schedules and Beijing driving cycle conditions are taken as testing dataset. As compared with some hybrid methods proposed in other literature, the hybrid method has the best estimation accuracy and generalization for various driving cycles at different ambient temperatures. The root mean square error and the mean absolute error all are less than 1.5%, and the maximum absolute error are less than 2%. In addition, it also exhibits powerful robustness against the abnormal values of the battery signals and can converge to the true value in just 5 seconds.

Download Full-text

A PROCESS-ORIENTED SPATIOTEMPORAL CLUSTERING METHOD FOR COMPLEX TRAJECTORIES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-1237-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 1237-1242

Author(s):

J. Liu ◽

C. Xue ◽

C. Wu ◽

Q. Dong

Keyword(s):

Clustering Algorithm ◽

Critical Role ◽

Trajectory Data ◽

Clustering Method ◽

Vehicle Trajectories ◽

The Pacific ◽

Spatiotemporal Clustering ◽

Process Oriented ◽

Clustering Pattern ◽

Complex Trajectories

<p><strong>Abstract.</strong> Considering the critical role of trajectory data in Big Data era for dynamic geographical processes, human behaviour analysis and meteorological prediction, trajectory clustering has attracted growing attention. Many literatures have discussed the spatiot emporal clustering method of simple trajectories (i.e., has no branches, e.g. vehicle trajectories), yet there are few researches for clustering complex trajectories (i.e., has at least one split and/or merger and/or split -merger branch, e.g. ocean eddy trajectories, rainstorm trajectories). For addressing this issue, we propose a Process-Oriented Spatiotemporal Clustering Method (POSCM) for clustering complex trajectory data. The POSCM includes three parts: the first uses the semantic of process-sequence-state to represent the complex trajectories; the second proposes a Hierarchical Similarity Measurement Method (HSMM) to get the similarity between any two complex trajectories; in the last step, the complex trajectories clustering pattern is extracted through density-based clustering algorithm. Experiments on simulated trajectories are used to evaluate the POSCM and demonstrate the advantage by comparing against that of the VF2 algorithm. The POSCM is applied to the sea surface temperature abnormal variations trajectories from January 1950 to December 2017 in the Pacific Ocean. As shown in this case study, some new mined spatiotemporal patterns can provide new references for understanding the behaviours of marine abnormal variations under the background of the global change.</p>

Download Full-text

Multi-Stage Pedestrian Positioning Using Filtered WiFi Scanner Data in an Urban Road Environment

Sensors ◽

10.3390/s20113259 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3259 ◽

Cited By ~ 2

Author(s):

Zilin Huang ◽

Lunhui Xu ◽

Yongjie Lin

Keyword(s):

Kalman Filter ◽

Real Time ◽

Polynomial Regression ◽

Unscented Kalman Filter ◽

Distance Estimation ◽

Taylor Series Expansion ◽

Propagation Model ◽

High Position ◽

Urban Road ◽

Multi Stage

Since widespread applications of wireless sensors networks, low-speed traffic positioning based on the received signal strength indicator (RSSI) from personal devices with WiFi broadcasts has attracted considerable attention. This study presents a new range-based localization method for outdoor pedestrian positioning by using the combination of offline RSSI distance estimation and real-time continuous position fitting, which can achieve high-position accuracy in the urban road environment. At the offline stage, the piecewise polynomial regression model (PPRM) is proposed to formulate the Euclidean distance between the targets and WiFi scanners by replacing the common propagation model (PM). The online stage includes three procedures. Firstly, a constant velocity Kalman filter (CVKF) is developed to smooth the real-time RSSI time series and estimate the target-detector distance. Then, a least squares Taylor series expansion (LS-TSE) is developed to calculate the actual 2-dimensional coordinate with the replacement of existing trilateral localization. Thirdly, a trajectory-based technique of the unscented Kalman filter (UKF) is introduced to smooth estimated positioning points. In tests that used field scenarios from Guangzhou, China, the experiments demonstrate that the combined CVKF and PPRM can achieve the highly accurate distance estimator of <1.98 m error with the probability of 90% or larger, which outperforms the existing propagation model. In addition, the online method can achieve average positioning error of 1.67 m with the much better than classical methods.

Download Full-text

An Urban Road-Traffic Commuting Dynamics Study Based on Hotspot Clustering and a New Proposed Urban Commuting Electrostatics Model

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8040190 ◽

2019 ◽

Vol 8 (4) ◽

pp. 190

Author(s):

Ni ◽

Huang ◽

Meng ◽

Zhou ◽

Keyword(s):

Electric Field ◽

Field Intensity ◽

Clustering Analysis ◽

Electric Field Intensity ◽

Road Traffic ◽

Rapid Development ◽

Traffic System ◽

Urban Road ◽

Vehicle Trajectories ◽

Beijing China

With the recent rapid development of cities, the dynamics of urban road-traffic commuting are becoming more and more complex. In this research, we study urban road-traffic commuting dynamics based on clustering analysis and a new proposed urban commuting electrostatics model. As a case study, we investigate the characteristics of urban road-traffic commuting dynamics during the morning rush hour in Beijing, China, using over 1.3 million Global Positioning System (GPS) data records of vehicle trajectories. The hotspot clusters are identified using clustering analysis, after which the urban commuting electric field is simulated based on an urban commuting electrostatics model. The results show that the areas with high electric field intensity tend to have slow traffic, and also that the vehicles in most areas tend to head in the same direction as the electric field. The results above verify the validity of the model, in that the electric field intensity can reflect the traffic pressure of an area, and that the direction of the electric field can reflect the traffic direction in that area. This new proposed urban commuting electrostatics model helps greatly in understanding urban road-traffic commuting dynamics and has broad applicability for the optimization of urban and traffic system planning.

Download Full-text