A Kalman Filter-Based Queue Length Estimation Method with Low-Penetration Mobile Sensor Data at Signalized Intersections

2018 ◽  
Vol 2672 (45) ◽  
pp. 253-264 ◽  
Author(s):  
Juyuan Yin ◽  
Jian Sun ◽  
Keshuang Tang
Keyword(s):  
Kalman Filter ◽  
Queue Length ◽  
Estimation Method ◽  
Mobile Internet ◽  
Internet Technology ◽  
Sensor Data ◽  
Linear Regression Method ◽  
Mobile Sensor ◽  
Length Estimation ◽  
Queue Lengths

Queue length estimation is of great importance for signal performance measures and signal optimization. With the development of connected vehicle technology and mobile internet technology, using mobile sensor data instead of fixed detector data to estimate queue length has become a significant research topic. This study proposes a queue length estimation method using low-penetration mobile sensor data as the only input. The proposed method is based on the combination of Kalman Filtering and shockwave theory. The critical points are identified from raw spatiotemporal points and allocated to different cycles for subsequent estimation. To apply the Kalman Filter, a state-space model with two state variables and the system noise determined by queue-forming acceleration is established, which can characterize the stochastic property of queue forming. The Kalman Filter with joining points as measurement input recursively estimates real-time queue lengths; on the other hand, queue-discharging waves are estimated with a line fitted to leaving points. By calculating the crossing point of the queue-forming wave and the queue-discharging wave of a cycle, the maximum queue length is also estimated. A case study with DiDi mobile sensor data and ground truth maximum queue lengths at Huanggang-Fuzhong intersection, Shenzhen, China, shows that the mean absolute percentage error is only 11.2%. Moreover, the sensitivity analysis shows that the proposed estimation method achieves much better performance than the classical linear regression method, especially in extremely low penetration rates.

scholarly journals A Real-Time Queue Length Estimation Method Based on Probe Vehicles in CV Environment

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 20825-20839 ◽  
Author(s):  
Haiqing Liu ◽  
Wenli Liang ◽  
Laxmisha Rai ◽  
Kunmin Teng ◽  
Shengli Wang
Keyword(s):  
Real Time ◽  
Queue Length ◽  
Estimation Method ◽  
Probe Vehicles ◽  
Length Estimation

scholarly journals Efficient Video-Based Vehicle Queue Length Estimation Using Computer Vision and Deep Learning for an Urban Traffic Scenario

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1786
Author(s):  
Muhammad Umair ◽  
Muhammad Umar Farooq ◽  
Rana Hammad Raza ◽  
Qian Chen ◽  
Baher Abdulhai
Keyword(s):  
Traffic Engineering ◽  
Queue Length ◽  
Urban Traffic ◽  
Fine Tuning ◽  
Low Resolution ◽  
Vehicle Class ◽  
Traffic Lane ◽  
Length Estimation ◽  
Traffic Scenario ◽  
Queue Lengths

In the traffic engineering realm, queue length estimation is considered one of the most critical challenges in the Intelligent Transportation System (ITS). Queue lengths are important for determining traffic capacity and quality, such that the risk for blockage in any traffic lane could be minimized. The Vision-based sensors show huge potentials compared to fixed or moving sensors as they offer flexibility for data acquisition due to large-scale deployment at a huge pace. Compared to others, these sensors offer low installation/maintenance costs and also help with other traffic surveillance related tasks. In this research, a CNN-based approach for estimation of vehicle queue length in an urban traffic scenario using low-resolution traffic videos is proposed. The system calculates queue length without the knowledge of any camera parameter or onsite calibration information. The estimation in terms of the number of cars is considered a priority as compared to queue length in the number of meters since the vehicular delay is the number of waiting cars times the wait time. Therefore, this research estimates queue length based on total vehicle count. However, length in meters is also provided by approximating average vehicle size as 5 m. The CNN-based approach helps with accurate tracking of vehicles’ positions and computing queue lengths without the need for installation of any roadside or in-vehicle sensors. Using a pre-trained 80-classes YOLOv4 model, an overall accuracy of 73% and 88% was achieved for vehicle-based and pixel-based queue length estimation. After further fine-tuning of model on the low-resolution traffic images and narrowing down the output classes to vehicle class only, an average accuracy of 83% and 93%, respectively, was achieved which shows the efficiency and robustness of the proposed approach.

Shockwave-Based Queue Length Estimation Method for Presignals for Bus Priority

2018 ◽  
Vol 144 (9) ◽  
pp. 04018057 ◽  
Author(s):  
Yunyi Liang ◽  
Zhizhou Wu ◽  
Jinyang Li ◽  
Fuliang Li ◽  
Yinhai Wang
Keyword(s):  
Queue Length ◽  
Estimation Method ◽  
Bus Priority ◽  
Length Estimation

Left-Turn Spillback Probability Estimation in a Connected Vehicle Environment

2019 ◽  
Vol 2673 (4) ◽  
pp. 753-761
Author(s):  
Xiaowei Cao ◽  
Jian Jiao ◽  
Yunlong Zhang ◽  
Xiubin Wang
Keyword(s):  
Control Strategy ◽  
Queue Length ◽  
Estimation Method ◽  
Signal Control ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Model Accuracy ◽  
Left Turn ◽  
Microscopic Traffic Simulation ◽  
Length Estimation

At intersections in which the left-turn bay does not have sufficient length or the left-turn volume is relatively high, left-turn vehicles may spill back and block the adjacent through traffic. This paper aims to develop quantitative measures of the left-turn spillback, and by using the results on spillback probability, develop a suitable signal control strategy. We first develop an improved queue length estimation method for vehicles in the left-turn bay based on Comert and Cetin’s general queue length estimation method with connected vehicles, after which we propose a probabilistic model to measure the left-turn spillback probability at an intersection in a connected environment. The model accuracy is validated with results from microscopic traffic simulation. The effect of bay length is also studied. In the end, a signal control demonstration is presented to show the efficiency of the proposed method in signal control.

scholarly journals A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles

2019 ◽  
Vol 27 (4) ◽  
pp. 341-354 ◽  
Author(s):  
Azadeh Emami ◽  
Majid Sarvi ◽  
Saeed Asadi Bagloee
Keyword(s):  
Neural Network ◽  
Queue Length ◽  
Travel Demand ◽  
Penetration Rate ◽  
Estimation Method ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Market Penetration ◽  
Length Estimation

AbstractThis paper presents a novel method to estimate queue length at signalised intersections using connected vehicle (CV) data. The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers. The model is applicable for real-time applications when there are sufficient training data available to train the estimation model. To this end, we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication (the communication platform used in CV system). The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems. Furthermore, a linear regression model is applied to weigh the importance of input variables to be used in a neural network model. Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions, a varying number of CVs (i.e. CVs’ market penetration rate) as well as various traffic signal control scenarios. As it is expected, when the market penetration rate increases, the accuracy of the model enhances consequently. In a congested traffic condition (saturated flow), the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates. Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters, the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information. The proposed algorithm is also tested using large size data from a CV test bed (i.e. Australian Integrated Multimodal Ecosystem) currently underway in Melbourne, Australia. The simulation results show that the model can perform well irrespective of the intersection layouts, traffic signal plans and arrival patterns of vehicles. Based on the numerical results, 20% penetration rate of CVs is a critical threshold. For penetration rates below 20%, prediction algorithms fail to produce reliable outcomes.

scholarly journals DIRECT IMAGE-TO-GEOMETRY REGISTRATION USING MOBILE SENSOR DATA

2016 ◽  
Vol III-2 ◽  
pp. 121-128
Author(s):  
C. Kehl ◽  
S. J. Buckley ◽  
R. L. Gawthorpe ◽  
I. Viola ◽  
J. A. Howell
Keyword(s):  
Mobile Devices ◽  
Pose Estimation ◽  
Laser Scanning ◽  
Feature Matching ◽  
Estimation Method ◽  
Environmental Parameters ◽  
Sensor Data ◽  
Mobile Sensor ◽  
Surface Geometry ◽  
The Impact

Adding supplementary texture and 2D image-based annotations to 3D surface models is a useful next step for domain specialists to make use of photorealistic products of laser scanning and photogrammetry. This requires a registration between the new camera imagery and the model geometry to be solved, which can be a time-consuming task without appropriate automation. The increasing availability of photorealistic models, coupled with the proliferation of mobile devices, gives users the possibility to complement their models in real time. Modern mobile devices deliver digital photographs of increasing quality, as well as on-board sensor data, which can be used as input for practical and automatic camera registration procedures. Their familiar user interface also improves manual registration procedures. This paper introduces a fully automatic pose estimation method using the on-board sensor data for initial exterior orientation, and feature matching between an acquired photograph and a synthesised rendering of the orientated 3D scene as input for fine alignment. The paper also introduces a user-friendly manual camera registration- and pose estimation interface for mobile devices, based on existing surface geometry and numerical optimisation methods. The article further assesses the automatic algorithm’s accuracy compared to traditional methods, and the impact of computational- and environmental parameters. Experiments using urban and geological case studies show a significant sensitivity of the automatic procedure to the quality of the initial mobile sensor values. Changing natural lighting conditions remain a challenge for automatic pose estimation techniques, although progress is presented here. Finally, the automatically-registered mobile images are used as the basis for adding user annotations to the input textured model.

scholarly journals Real-time Queue Length Estimation Applying Shockwave Theory at Urban Signalized Intersections

2021 ◽  
Author(s):  
Márton Tamás Horváth ◽  
Tamás Tettamanti
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Queue Length ◽  
Traffic Management ◽  
Urban Traffic ◽  
Length Estimation ◽  
Queue Lengths ◽  
General Goal ◽  
Store And Forward ◽  
Urban Traffic Control

Signal control is a basic need for urban traffic control; however, it is a very rough intervention in the free flow of traffic, which often results in queues in front of signal heads. The general goal is to reduce the delays caused, and to plan efficient traffic management on the network. For this, the exact knowledge of queue lengths on links is one of crucial importance. This article presents a link-based methodology for real-time queue length estimation in urban signalized road networks. The model uses a Kalman Filter-based recursive method and estimates the length of the queue in every cycle. The input of the filter, i.e. the dynamics of queue length is described by the traffic shockwave theory and the store and forward model. The method requires one loop-detector per link placed at the appropriate position, for which the article also provides suggestions.

Modeling the Impacts of Traffic Flow Arrival Profiles on Ramp Metering Queues

2018 ◽  
Vol 2672 (15) ◽  
pp. 85-92 ◽  
Author(s):  
Guangchuan Yang ◽  
Rui Yue ◽  
Zong Tian ◽  
Hao Xu
Keyword(s):  
Traffic Flow ◽  
Queue Length ◽  
Ramp Metering ◽  
Signalized Intersection ◽  
Signal Timing ◽  
Microscopic Simulation ◽  
Length Estimation ◽  
Platoon Dispersion ◽  
Queue Lengths ◽  
Potential Impact

An adequate queue storage length is critical for a metered on-ramp to prevent ramp queue spillback to the upstream signalized intersection. Previous research on queue length estimation or queue storage length design at metered ramps has not taken into account the potential impact of various on-ramp traffic flow arrival profiles on ramp queue lengths. This paper depicts the traffic flow arrival profiles and queue generation processes at three different metered ramp categories. Based on a large number of microscopic simulation runs, it is found that, under a given demand-to-capacity scenario, the queue at a metered ramp with two on-ramp feeding movements is more likely to be cleared in a cycle than at a metered ramp with three on-ramp feeding movements. Also, the platoon dispersion effect significantly reduces the ramp queue length, and hence the queue storage needs at a metered ramp. In addition, this paper reveals that ramp queue length tends to increase linearly with upstream signal cycle length. The design of queue storage length for a metered on-ramp hence needs to fully consider the various ramp configurations and upstream signal timing settings.

scholarly journals DIRECT IMAGE-TO-GEOMETRY REGISTRATION USING MOBILE SENSOR DATA

2016 ◽  
Vol III-2 ◽  
pp. 121-128 ◽  
Author(s):  
C. Kehl ◽  
S. J. Buckley ◽  
R. L. Gawthorpe ◽  
I. Viola ◽  
J. A. Howell
Keyword(s):  
Mobile Devices ◽  
Pose Estimation ◽  
Laser Scanning ◽  
Feature Matching ◽  
Estimation Method ◽  
Environmental Parameters ◽  
Sensor Data ◽  
Mobile Sensor ◽  
Surface Geometry ◽  
The Impact

Adding supplementary texture and 2D image-based annotations to 3D surface models is a useful next step for domain specialists to make use of photorealistic products of laser scanning and photogrammetry. This requires a registration between the new camera imagery and the model geometry to be solved, which can be a time-consuming task without appropriate automation. The increasing availability of photorealistic models, coupled with the proliferation of mobile devices, gives users the possibility to complement their models in real time. Modern mobile devices deliver digital photographs of increasing quality, as well as on-board sensor data, which can be used as input for practical and automatic camera registration procedures. Their familiar user interface also improves manual registration procedures. This paper introduces a fully automatic pose estimation method using the on-board sensor data for initial exterior orientation, and feature matching between an acquired photograph and a synthesised rendering of the orientated 3D scene as input for fine alignment. The paper also introduces a user-friendly manual camera registration- and pose estimation interface for mobile devices, based on existing surface geometry and numerical optimisation methods. The article further assesses the automatic algorithm’s accuracy compared to traditional methods, and the impact of computational- and environmental parameters. Experiments using urban and geological case studies show a significant sensitivity of the automatic procedure to the quality of the initial mobile sensor values. Changing natural lighting conditions remain a challenge for automatic pose estimation techniques, although progress is presented here. Finally, the automatically-registered mobile images are used as the basis for adding user annotations to the input textured model.

scholarly journals Implementasi Algoritma 2 Step Kalman Filter Untuk Mengurangi Noise Pada Estimasi Data Accelerometer

2019 ◽  
Vol 3 (1) ◽  
pp. 142
Author(s):  
Wahyu Sukestyastama Putra
Keyword(s):  
Kalman Filter ◽  
Technological Development ◽  
Estimation Method ◽  
Stable Condition ◽  
Sensor Data ◽  
Average Error ◽  
Sensor Output ◽  
Step Method ◽  
Accelerometer Sensor ◽  
Step Algorithm

An accelerometer is a useful sensor in technological development. Currently, the accelerometer is found on smartphone devices, navigation devices, and wearable devices. However, processing the sensor output signal into data that can be interpreted is not easy. This is because the output of an accelerometer sensor has significant noise. In this study, the authors are interested in developing an estimation method using a Kalman Filter. Kalman filter is an estimator so it is expected that the sensor data are more resistant to noise interference. In this study, the author innovated the 2 step Kalman filter. The study was conducted because the use of 1 step still has noise on the estimation results. Based on the analysis of the algorithm simulation results, it can be concluded that the Kalman filter 2-step algorithm has good performance in estimating the accelerometer sensor output. When compared with the Kalman filter 1 step algorithm, the Kalman filter 2 step algorithm has a smaller average error estimation and is able to achieve a constant/stable condition faster than the Kalman filter 1 step method

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