Examining Deterministic And Probabilistic Capacity Estimation Methods Under Mixed Traffic Using Empirical Data

2021 ◽  
Author(s):  
Ritvik Chauhan ◽  
Pallav Kumar ◽  
Shriniwas Arkatkar ◽  
Ashish Dhamaniya ◽  
Prasanta K. Sahu
Keyword(s):  
Empirical Data ◽  
Estimation Methods ◽  
Mixed Traffic ◽  
Capacity Estimation

Femtocell Selection Scheme for Reducing Unnecessary Handover and Enhancing Downlink QoS in Cognitive Femtocell Networks

2017 ◽  
Author(s):  
Hoang Nhu Dong ◽  
Hoang Nam Nguyen ◽  
Hoang Trong Minh ◽  
Takahiko Saba
Keyword(s):  
Cellular Networks ◽  
Estimation Methods ◽  
Mobility Prediction ◽  
Capacity Estimation ◽  
Femtocell Networks ◽  
Selection Scheme ◽  
Indoor Communications ◽  
Performance Results ◽  
Cognitive Femtocell

Femtocell networks have been proposed for indoor communications as the extension of cellular networks for enhancing coverage performance. Because femtocells have small coverage radius, typically from 15 to 30 meters, a femtocell user (FU) walking at low speed can still make several femtocell-to-femtocell handovers during its connection. When performing a femtocell-to-femtocell handover, femtocell selection used to select the target handover femtocell has to be able not only to reduce unnecessary handovers and but also to support FU’s quality of service (QoS). In the paper, we propose a femtocell selection scheme for femtocell-tofemtocell handover, named Mobility Prediction and Capacity Estimation based scheme (MPCE-based scheme), which has the advantages of the mobility prediction and femtocell’s available capacity estimation methods. Performance results obtained by computer simulation show that the proposed MPCE-based scheme can reduce unnecessary femtocell-tofemtocell handovers, maintain low data delay and improve the throughput of femtocell users. DOI: 10.32913/rd-ict.vol3.no14.536

Comparison of Highway Capacity Estimation Methods

2018 ◽  
Vol 2672 (15) ◽  
pp. 75-84 ◽  
Author(s):  
Mohamadamin Asgharzadeh ◽  
Alexandra Kondyli
Keyword(s):  
Kansas City ◽  
Critical Factor ◽  
Estimation Methods ◽  
Flow Index ◽  
Capacity Estimation ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Advantages And Disadvantages ◽  
Breakdown Probability ◽  
Consistent Performance

The capacity of a freeway segment is a critical factor for planning, design, and operational analysis of freeway facilities. This research aimed to perform a comparison among well-known freeway capacity estimation methods in order to investigate their application, as well as their advantages and disadvantages. Single estimate capacity methods such as the Van Aerde method, and breakdown probability methods, such as the product limit method (PLM), the Highway Capacity Manual (HCM) method, and the sustainable flow index (SFI) method, were applied at six merge bottleneck locations in the Kansas City area. The results from all methods were compared and the advantages and the disadvantages of each method were discussed. The HCM results showed a significant variability in the estimated breakdown probability function and the resulting capacities. The HCM method was also found to be sensitive to the breakdown probability ratios as a single breakdown observation can significantly shift the fitted distribution and the corresponding capacity estimate. The PLM model provided the highest capacity estimates, followed by the Van Aerde model capacities. The Van Aerde capacities were also found to be closer to the average pre-breakdown flow rates. Finally, the PLM and the SFI method showed consistent performance in comparison to the remaining methods, and flexibility in being applied on different sites with various characteristics.

scholarly journals Estimation of Critical Headway at Small Urban Roundabout

2020 ◽  
Vol 32 (1) ◽  
pp. 103-117
Author(s):  
Danijela Maslać ◽  
Dražen Cvitanić ◽  
Ivan Lovrić
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Project Design ◽  
Capacity Estimation ◽  
Local Conditions ◽  
Large Numbers ◽  
The World

Before choosing an intersection project design, an important step is to examine the justification of the construction on the basis of defined criteria. One of the key criteria is the analysis of capacity. Large numbers of roundabout capacity models are present in the world, most of them adapted to the conditions of the country they originate from and they need to be calibrated for local conditions. Key parameters for calibration are critical headway and follow-up headway. Follow-up headway can be measured directly in the field, while critical headway cannot be measured, but is estimated. Many critical headway estimation methods exist (over 30) and each of them provides different values. Different values of critical headway result in different capacity estimation values. This raises the question which method provides more realistic estimations under certain conditions. In this paper, four most frequently used critical headway estimation methods (Raff, Maximum likelihood method, Wu, Logit) were selected to be tested by comparison of theoretical capacity models and actual measured capacity at a small urban roundabout.

scholarly journals Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 256
Author(s):  
Yi Wu ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Capacity Estimation ◽  
Practical Applications

Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.

Analysis of Capacity Estimation Methods for Real-Time Applications in Internet Accesses

2008 ◽  
Author(s):  
Eduardo A. Viruete ◽  
Julian Fernandez-Navajas ◽  
Elena Macian-Senz ◽  
Ignacio Martinez ◽  
Rafael del-Hoyo ◽  
...  
Keyword(s):  
Real Time ◽  
Estimation Methods ◽  
Capacity Estimation ◽  
Real Time Applications

scholarly journals Analysis of Bicycle Headway Distribution, Saturation Flow and Capacity at a Signalized Intersection using Empirical Trajectory Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 10-21 ◽  
Author(s):  
Yufei Yuan ◽  
Bernat Goñi-Ros ◽  
Mees Poppe ◽  
Winnie Daamen ◽  
Serge P. Hoogendoorn
Keyword(s):  
Fixed Number ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Estimation Methods ◽  
Trajectory Data ◽  
Capacity Estimation ◽  
Flow Capacity ◽  
Headway Distribution ◽  
Cycle Path ◽  
Stop Line

Predicting the bicycle flow capacity at signalized intersections of various characteristics is crucial for urban infrastructure design and traffic management. However, it is also a difficult task because of the large heterogeneity in cycling behavior and several limitations of traditional capacity estimation methods. This paper proposes several methodological improvements, illustrates them using high-resolution trajectory data collected at a busy signalized intersection in the Netherlands, and investigates the influence of key variables of capacity estimation. More specifically, it shows that the (virtual) sublane width has a significant effect on the shape of the headway distribution at the stop line. Furthermore, a new method is proposed to calculate the saturation headway (a key variable determining capacity), which excludes the cyclists initially located close to the stop line using a distance-based rule instead of a fixed number (as is usually done in practice). It is also shown that the saturation headway is quite sensitive to the sublane width. Moreover, a new, empirically based method is proposed to identify the number of sublanes that can be accommodated in a given cycle path, which is another key influencing variable. This method yields considerably lower estimates of the number of sublanes than traditional methods, which rely solely on the (available) cycle path width. Finally, the authors show that methodological choices such as the sublane width and the method used to estimate the number of sublanes have a considerable effect on capacity estimates. Therefore, this paper highlights the need to define a sound methodology to estimate bicycle flow capacity at signalized intersections and proposes some steps to move toward that direction.

Link Cost Function and Link Capacity for Mixed Traffic Networks

2020 ◽  
Vol 2674 (9) ◽  
pp. 38-50
Author(s):  
Aathira K. Das ◽  
Bhargava Rama Chilukuri
Keyword(s):  
Cost Function ◽  
Empirical Data ◽  
Wave Model ◽  
Mixed Traffic ◽  
Vehicle Class ◽  
Link Capacity ◽  
The Road ◽  
Traffic Conditions ◽  
Link Cost ◽  
Link Travel Time

Link cost function and link capacity are critical factors in traffic assignment modeling. Popular link cost functions like the Bureau of Public Roads (BPR) function have well-known drawbacks and are not suitable for mixed traffic conditions where a variety of vehicle classes use the road in a non-lane-based movement. Similarly, capacity is generally considered as a constant value. However, in mixed traffic conditions, capacity is not constant, but a function of vehicle class composition. Toward addressing these issues, this paper proposes a link cost function in relation to link travel time and link capacity in relation to vehicular traffic flow for mixed traffic conditions. The functions are developed based on the kinematic wave model, which is popularly used for estimating traffic dynamics on the roads. The developed link cost function and link capacity use field measurable parameters that incorporate mixed traffic features. The functions are validated against empirical data obtained from 12 signal cycles from two different signalized intersections in Chennai, India, representing different scenarios of mixed traffic, and it was found that the results match well with the empirical data.

scholarly journals Study on Capacity Estimation Methods of Second-Life Application Batteries

2021 ◽  
Vol 12 (4) ◽  
pp. 163
Author(s):  
Linkang Ma ◽  
Caiping Zhang ◽  
Jinyu Wang ◽  
Kairang Wang ◽  
Jie Chen
Keyword(s):  
Linear Regression ◽  
Second Life ◽  
Degradation Mechanism ◽  
Feature Selection Method ◽  
Estimation Methods ◽  
Average Error ◽  
Curve Matching ◽  
Linear Regression Models ◽  
Capacity Estimation ◽  
In Series

For the capacity estimation problem of cells in series-retired battery modules, this paper proposed three different methods from the perspective of data-driven, battery curve matching and recession characteristics for different applications. Firstly, based on the premise that the battery history data are available, the features of the IC curve are selected as input for the linear regression models. To avoid multicollinearity among features, we apply a filter-based feature selection method to eliminate redundant features. The results show that the average errors with Multiple Linear Regression are within 1.5%. Secondly, for the situation with a lack of historical operating data, the battery-curve-matching-based method is proposed based on the Dynamic Time Warping algorithm. This method could achieve the curve matching between the reference cell and target cell, and then the curve contraction coefficients can be obtained. The result shows that the method’s average error is 2.34%. Thirdly, whereas the tougher situation is that only part of the battery curve is available, we present a substitute method based on the battery degradation mechanism. This method can estimate most of the battery plant capacity through the partial battery curve. The result shows that the method’s average error is within 2%. Lastly, we contrast the applicability and limitations of every method based on the retired battery test data after deep cycling aging.

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