Kalman Filtering Method for Real-Time Queue Length Estimation in a Connected Vehicle Environment

Abstract

Queue length estimation is of great importance for measuring traffic signal performance and optimizing traffic signal timing plans. With the development of connected vehicle (CV) technology, using mobile CV data instead of fixed detector data to estimate queue length has become an important research topic. This study focuses on real-time queue length estimation for an isolated intersection with CV data. A Kalman filtering method is proposed to estimate the queue length in real time using traffic signal timing and real-time traffic flow parameters (i.e., saturated flow rate, traffic volume, and penetration rate), which are estimated using CV trajectories data. A simulation intersection was built and calibrated using field data to evaluate the performance of the proposed method and the benchmark method. Results show that when the CV penetration rate is at 30%, the average values of mean absolute errors, mean absolute percentage errors, and root mean square errors are just 1.6 vehicles, 20.9%, and 2.5 vehicles, respectively. The performance of the proposed model is also better than the benchmark method when the penetration rate of CVs is higher than 20%, which proves the validity of the proposed method. Furthermore, sensitivity analysis indicates that the proposed method requires a high penetration rate of at least 30%.

[Retracted] Applications of Deep Learning in the Evaluation and Analysis of College Students’ Mental Health

“…In the process of analyzing and evaluating college students' mental health by using deep learning, the general process can be understood as three links with different characteristics and different emphasis directions: e first part is to understand the commonness and difference of the analysis and evaluation of different college students' mental health under the same data structure and algorithm model [27]. In the theoretical model of deep learning, firstly, the operation structure and processing model of convolutional neural network and multi-layer neuron self-coding neural network are used to analyze the mental health of college students, then the data structure obtained from the analysis is arranged, and the big data analysis theory and method are used to conduct secondary analysis, so as to find the commonness of most college students' mental health.…”

Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway

“…Vehicular trajectory provides massive spatial-temporal traffic information, which has been used in many transportation research, for example, transportation fuel consumption and emissions [1][2][3], traffic signal optimization [4][5][6], traffic flow modelling, validation, and calibration [7][8][9][10][11], driving behaviour analysis [12,13], traffic state estimation [14][15][16][17] in terms of volume [18,19], queue length [20][21][22], and fundamental diagram [23,24]. Therefore, it is of great significance to obtain more vehicle trajectory data [25,26].…”