scholarly journals Methods for estimating vehicle queues at a marine terminal: A computational comparison

2014 ◽  
Vol 24 (3) ◽  
pp. 611-619 ◽  
Author(s):  
Gang Chen ◽  
Zhong-Zhen Yang
Keyword(s):  
Queue Length ◽  
Simulation Software ◽  
Urban Traffic ◽  
Estimation Accuracy ◽  
Estimation Model ◽  
Advantages And Disadvantages ◽  
Natural Logarithm ◽  
Length Estimation ◽  
Proposed Model ◽  
Simulation Based

Abstract A long queue of vehicles at the gate of a marine terminal is a common traffic phenomenon in a port-city, which sometimes causes problems in urban traffic. In order to be able to solve this issue, we firstly need accurate models to estimate such a vehicle queue length. In this paper, we compare the existing methods in a case study, and evaluate their advantages and disadvantages. Particularly, we develop a simulation-based regression model, using the micro traffic simulation software PARAMIC. In simulation, it is found that the queue transient process follows a natural logarithm curve. Then, based on these curves, we develop a queue length estimation model. In the numerical experiment, the proposed model exhibits better estimation accuracy than the other existing methods

scholarly journals Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3527
Author(s):  
Melanija Vezočnik ◽  
Roman Kamnik ◽  
Matjaz B. Juric
Keyword(s):  
Principal Component Analysis ◽  
Human Body ◽  
Inertial Sensor ◽  
Principal Component ◽  
Step Length ◽  
Component Analysis ◽  
Indoor Positioning ◽  
Estimation Model ◽  
Length Estimation ◽  
Proposed Model

Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements.

Development of Vehicle Queue Length Estimation Model Using Deep Learning

2018 ◽  
Vol 17 (2) ◽  
pp. 39-57
Author(s):  
Lee Yong-Ju ◽  
◽  
Hwang Jae-Seong ◽  
Kim Soo-Hee ◽  
Lee Choul-Ki
Keyword(s):  
Deep Learning ◽  
Queue Length ◽  
Estimation Model ◽  
Length Estimation

scholarly journals Dynamic Analysis of Traffic State and Congestion Propagation on Bidirectional Grid Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Shu-bin Li ◽  
Bai-bai Fu ◽  
Jian-feng Zheng
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Network Performance ◽  
Simulation Software ◽  
Urban Traffic ◽  
Grid Network ◽  
Traffic State ◽  
Proposed Model ◽  
Air Pollutions ◽  
Density Relationship

Many traffic problems in China such as traffic jams and air pollutions are mainly caused by the increasing traffic volume. In order to alleviate the traffic congestion and improve the network performance, the analysis of traffic state and congestion propagation has attracted a great interest. In this paper, an improved mesoscopic traffic flow model is proposed to capture the speed-density relationship on segments, the length of queue, the flow on links, and so forth, The self-developed dynamic traffic simulation software (DynaCHINA) is used to reproduce the traffic congestion and propagation in a bidirectional grid network for different demand levels. The simulation results show that the proposed model and method are capable of capturing the real traffic states. Hence, our results can provide decision supports for the urban traffic management and planning.

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.

scholarly journals Continuous Noninvasive Blood Gas Estimation in Critically Ill Pediatric Patients With Respiratory Failure

2020 ◽  
Author(s):  
Junzi Dong ◽  
Minnan Xu-Wilson ◽  
Bryan R. Conroy ◽  
Robinder G. Khemani ◽  
Christopher J.L. Newth
Keyword(s):  
Clinical Laboratory ◽  
Care Center ◽  
Pediatric Intensive Care ◽  
Estimation Accuracy ◽  
Validation Dataset ◽  
Blood Gas ◽  
Estimation Model ◽  
Blood Draw ◽  
Proposed Model ◽  
Clinical Laboratory Improvement Amendments

Abstract BackgroundPatients supported by mechanical ventilation require frequent invasive blood gas samples to monitor and adjust the level of support. We developed a transparent and novel blood gas estimation model to provide continuous monitoring of blood pH and arterial CO2 in between gaps of blood draws, using only readily available noninvasive data sources in ventilated patients. MethodsThe model was trained on a derivation dataset (1,883 patients, 12,344 samples) from a tertiary pediatric intensive care center, and tested on a validation dataset (286 patients, 4,030 samples) from the same center obtained at a later time. The model uses pairwise non-linear interactions between predictors and provides point-estimates of blood gas pH and arterial CO2 along with a range of prediction uncertainty.ResultsThe model predicted within Clinical Laboratory Improvement Amendments of 1988 (CLIA) acceptable blood gas machine equivalent in 74% of pH samples and 80% of PCO2 samples. Prediction uncertainty from the model improved estimation accuracy by identifying and abstaining on a minority of high-uncertainty samples.ConclusionsThe proposed model estimates blood gas pH and CO2 accurately in a large percentage of samples. The model’s abstention recommendation coupled with ranked display of top predictors for each estimation lends itself to real-time monitoring of between gaps of blood draws, and the model may be used to help users determine when a new blood draw is required and delay blood draws when not needed.

scholarly journals An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

2020 ◽  
Vol 10 (6) ◽  
pp. 2078 ◽  
Author(s):  
Kai Gao ◽  
Shuo Huang ◽  
Farong Han ◽  
Shuo Li ◽  
Wenguang Wu ◽  
...  
Keyword(s):  
Queue Length ◽  
Estimation Algorithm ◽  
Traffic Monitoring ◽  
Traffic Information ◽  
Estimation Accuracy ◽  
Connected Vehicles ◽  
Mobile Service ◽  
Traffic Light ◽  
Neuron Network ◽  
Length Estimation

Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm.

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.

Analysis of Queue Estimation Process at Signalized Intersections Under Low Connected Vehicle Penetration Rates

2020 ◽  
Author(s):  
Osama A Osman ◽  
Peter R. Bakhit ◽  
Sherif Ishak
Keyword(s):  
Queue Length ◽  
Assessment Tool ◽  
Signalized Intersections ◽  
Estimation Accuracy ◽  
Connected Vehicle ◽  
Factors Affecting ◽  
Estimation Process ◽  
Cycle Basis ◽  
Traffic Conditions ◽  
Length Estimation

This study investigates the factors affecting estimation accuracy of queue length at signalized intersections under low penetration of connected vehicles. A shockwave-based algorithm is proposed to estimate the maximum queue length and residual queue on a cycle-by-cycle basis. Simulation data collected from three consecutive signalized intersections were used to extract trajectories of CVs under five different market penetration rates and two different traffic conditions (under-saturated and moderate). The results confirm that the queue length estimation process is probabilistic and affected by the stochastic changes in traffic conditions. This probabilistic nature is defined by a queue formation coverage index (QI) that proved to significantly affect the queue length estimation accuracy. Overall, the results show that the queue estimates accuracy is acceptable when a QI value of at least 50% is achieved. In such limited data environments, the QI showed the potential to help as an assessment tool to evaluate the obtained queue estimates.

scholarly journals Cost Estimation Model Using Modified Parameters for Construction Projects

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Sae-Hyun Ji ◽  
Joseph Ahn ◽  
Hyun-Soo Lee ◽  
Kyeongjin Han
Keyword(s):  
Cost Estimation ◽  
Construction Projects ◽  
Cost Model ◽  
Influence Factors ◽  
Influential Factors ◽  
Estimation Accuracy ◽  
Estimation Model ◽  
Proposed Model ◽  
Cost Estimation Model ◽  
The Cost

Construction projects require huge amounts of capital and have many risk factors due to the unique industry characteristics. For a project to be successful, accurate cost estimation during the design phase is very important. Thus, this research aims to develop a cost estimation model where a modification method integrates influential factors with significant parameters. This study identified a modified parameter-making process, which integrates many influential factors into a small number of significant parameters. The proposed model estimates the cost using quantity-based modified parameters multiplied by their price. A case study was conducted with 24-residence building project, and the estimation accuracy of the suggested method and a CBR model were compared. The proposed model achieved higher overall cost-estimation accuracy and stability. A large number of influence factors can be modified as simple representatives and overcome the limitations of a conventional cost estimation model. The paper originality relates to providing a modified parameter-making process to enhance reliability of a cost estimation. In addition, the suggested cost model can actively respond to the iterative requirements of recalculation of the cost.

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