Reliability-Based Design for Passing Maneuvers Based on Observational Data

2020 ◽  
Vol 2674 (7) ◽  
pp. 127-134
Author(s):  
Udai Hassein
Keyword(s):  
Field Experiments ◽  
Driving Simulator ◽  
Driver Behavior ◽  
Real Life ◽  
Estimation Method ◽  
Random Variables ◽  
Minimum Length ◽  
Reliability Model ◽  
Reliability Based Design ◽  
Proposed Model

Two-lane roadways constitute the largest proportion of road networks. Their operational characteristics are significantly different from other road classifications. Allowing passing maneuvers is considered as one of the effective measures to improve mobility levels along two-lane highways, while crash records show that head-on collisions, which usually are attributed to passing maneuvers, are among the most common and most severe types of crashes on two-lane roadways. Therefore, rational and realistic estimation of the needed passing sight distance (PSD) considering driver behavior is essential for the safe design of passing zones along two-lane highways. Several random variables help to determine the minimum length required for safe passing maneuvers. Current PSD models are based on single deterministic values of the input variables to determine PSD values. This paper presents a reliability model PSD that accounts for the variability of the input random variables to offer a better representation of real-life conditions. The objectives of this paper are: (1) to design driving simulator and field experiments for data collection, (2) to develop a PSD model using the mechanics of passing maneuvers, (3) to develop a reliability model based on the first-order second-moment (FOSM) method, and (4) to validate the model using Monte Carlo simulation. In this study, driving simulator experiments were conducted to determine the passing behavior of drivers, and field data were used to validate the proposed PSD model. The proposed model accounts for the variability in the parameters by using the mean and standard deviation in a closed form estimation method. The analysis was performed for a design speed of 80 km/h, and the corresponding PSD distribution was established. A comparison of the results of the proposed model, which reflects driver behavior, and those of existing models was presented. Using the reliability-based design method, transportation engineers can adjust the PSD to fulfill a desired probability of non-compliance.

scholarly journals Predicting Driver Behavior Using Field Experiment Data and Driving Simulator Experiment Data: Assessing Impact of Elimination of Stop Regulation at Railway Crossings

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Toshihisa Sato ◽  
Motoyuki Akamatsu ◽  
Toru Shibata ◽  
Shingo Matsumoto ◽  
Naoki Hatakeyama ◽  
...  
Keyword(s):  
Field Experiments ◽  
Driving Simulator ◽  
Driver Behavior ◽  
Assessment Method ◽  
Driving Behavior ◽  
Experiment Data ◽  
Driving Speed ◽  
Railway Crossing ◽  
The Impact ◽  
Lead Vehicle

We investigated the impact of deregulating the presence of stop signs at railway crossings on car driver behavior. We estimated the probability that a driver would stop inside the crossing, thereby obstructing the tracks, when a lead vehicle suddenly stopped after the crossing and a stop regulation was eliminated. We proposed a new assessment method of the driving behavior as follows: first, collecting driving behavior data in a driving simulator and in a real road environment; then, predicting the probability based on the collected data. In the simulator experiment, we measured the distances between a lead vehicle and the driver’s vehicle and the driver’s response time to the deceleration of the leading vehicle when entering the railway crossing. We investigated the influence of the presence of two leading vehicles on the driver’s vehicle movements. The deceleration data were recorded in the field experiments. Slower driving speed led to a higher probability of stopping inside the railway crossing. The probability was higher when the vehicle in front of the leading vehicle did not slow down than when both the lead vehicle and the vehicle in front of it slowed down. Finally, advantages of our new assessment method were discussed.

scholarly journals Generalized Lindley-Quasi Xgamma distribution

2021 ◽  
Vol 17 (1) ◽  
pp. 5-30
Author(s):  
S. A. Wani ◽  
S. Shafi
Keyword(s):  
Maximum Likelihood ◽  
Goodness Of Fit ◽  
Real Life ◽  
Estimation Method ◽  
Maximum Likelihood Estimates ◽  
Ratio Test ◽  
Life Data ◽  
Weight Parameter ◽  
Real Life Data ◽  
Proposed Model

Abstract We obtained a new generalization of Lindley-Quasi Xgamma distribution by adding weight parameter to it through weighting technique and have shown the flexibility of proposed model. Expression for reliability measures, order statistics, Bonferroni curves & indices, Renyi entropy along with some other important properties are derived. Maximum likelihood estimation method is put to use for estimation of unknown parameters of proposed model. Simulation study for checking the performance of maximum likelihood estimates and for model comparison is carried out. Proposed model and its related models are fitted to real life data sets and goodness of fit measure Kolmogorov statistic & p-value, loss of information criteria’s AIC, BIC, AICC & HQIC are computed through R software to check the applicability of proposed model in real life. The significance of weight parameter is also tested by using likelihood ratio test for both randomly generated data as well as real life data.

scholarly journals A modified truncated distribution for modeling the heavy tail, engineering and environmental sciences data

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249001
Author(s):  
Ahtasham Gul ◽  
Muhammad Mohsin ◽  
Muhammad Adil ◽  
Mansoor Ali
Keyword(s):  
Hazard Function ◽  
Real Life ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Cumulative Distribution ◽  
Statistical Characteristics ◽  
Model Parameters ◽  
Truncated Distribution ◽  
Unknown Parameters ◽  
Proposed Model

Truncated models are imperative to efficiently analyze the finite data that we observe in almost all the real life situations. In this paper, a new truncated distribution having four parameters named Weibull-Truncated Exponential Distribution (W-TEXPD) is developed. The proposed model can be used as an alternative to the Exponential, standard Weibull and shifted Gamma-Weibull and three parameter Weibull distributions. The statistical characteristics including cumulative distribution function, hazard function, cumulative hazard function, central moments, skewness, kurtosis, percentile and entropy of the proposed model are derived. The maximum likelihood estimation method is employed to evaluate the unknown parameters of the W-TEXPD. A simulation study is also carried out to assess the performance of the model parameters. The proposed probability distribution is fitted on five data sets from different fields to demonstrate its vast application. A comparison of the proposed model with some extant models is given to justify the performance of the W-TEXPD.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

scholarly journals Comparative Analysis of Kinect-Based and Oculus-Based Gaze Region Estimation Methods in a Driving Simulator

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 26
Author(s):  
David González-Ortega ◽  
Francisco Javier Díaz-Pernas ◽  
Mario Martínez-Zarzuela ◽  
Míriam Antón-Rodríguez
Keyword(s):  
Virtual Reality ◽  
Comparative Analysis ◽  
Driving Simulator ◽  
Estimation Method ◽  
Estimation Methods ◽  
Gaze Estimation ◽  
Driving Simulators ◽  
Oculus Rift ◽  
Gaze Patterns ◽  
And Performance

Driver’s gaze information can be crucial in driving research because of its relation to driver attention. Particularly, the inclusion of gaze data in driving simulators broadens the scope of research studies as they can relate drivers’ gaze patterns to their features and performance. In this paper, we present two gaze region estimation modules integrated in a driving simulator. One uses the 3D Kinect device and another uses the virtual reality Oculus Rift device. The modules are able to detect the region, out of seven in which the driving scene was divided, where a driver is gazing at in every route processed frame. Four methods were implemented and compared for gaze estimation, which learn the relation between gaze displacement and head movement. Two are simpler and based on points that try to capture this relation and two are based on classifiers such as MLP and SVM. Experiments were carried out with 12 users that drove on the same scenario twice, each one with a different visualization display, first with a big screen and later with Oculus Rift. On the whole, Oculus Rift outperformed Kinect as the best hardware for gaze estimation. The Oculus-based gaze region estimation method with the highest performance achieved an accuracy of 97.94%. The information provided by the Oculus Rift module enriches the driving simulator data and makes it possible a multimodal driving performance analysis apart from the immersion and realism obtained with the virtual reality experience provided by Oculus.

Understanding Driver Behavior after Concussion: A Machine-Learning Approach

2020 ◽  
Vol 64 (1) ◽  
pp. 1911-1915
Author(s):  
Maryam Daniali ◽  
Dario D. Salvucci ◽  
Maria T. Schultheis
Keyword(s):  
Machine Learning ◽  
Task Performance ◽  
Verbal Memory ◽  
Driving Simulator ◽  
Cognitive Impairments ◽  
Driver Behavior ◽  
Memory Task ◽  
Learning Approach ◽  
Behavioral Task ◽  
Machine Learning Approach

Concussions are common cognitive impairments, but their effects on task performance in general, and on driving in particular, are not well understood. To better understand the effects of concussion on driving, we investigated previously gathered data on twenty-two people with a concussion, driving in a virtual-reality driving simulator (VRDS), and twenty-two non-concussed matched drivers. Participants were asked to per-form a behavioral task (either coin sorting or a verbal memory task) while driving. In this study, we chose a few common metrics from the VRDS and tracked their changes through time for each participant. Our pro-posed method—namely, the use of convolutional neural networks for classification and analysis—can accu-rately classify concussed driving and extract local features on driving sequences that translate to behavioral driving signatures. Overall, our method improves identification and understanding of clinically relevant driv-ing behaviors for concussed individuals and should generalize well to other types of impairments.

scholarly journals Approximate Bayesian Computation for Discrete Spaces

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 312
Author(s):  
Ilze A. Auzina ◽  
Jakub M. Tomczak
Keyword(s):  
Approximate Bayesian Computation ◽  
Likelihood Function ◽  
Real Life ◽  
Random Variables ◽  
Bayesian Computation ◽  
Inference Problem ◽  
Markov Kernel ◽  
Inference Problems ◽  
Binary Neural Network ◽  
Approximate Bayesian

Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined. For continuous random variables, likelihood-free inference problems can be solved via Approximate Bayesian Computation (ABC). However, an optimal alternative for discrete random variables is yet to be formulated. Here, we aim to fill this research gap. We propose an adjusted population-based MCMC ABC method by re-defining the standard ABC parameters to discrete ones and by introducing a novel Markov kernel that is inspired by differential evolution. We first assess the proposed Markov kernel on a likelihood-based inference problem, namely discovering the underlying diseases based on a QMR-DTnetwork and, subsequently, the entire method on three likelihood-free inference problems: (i) the QMR-DT network with the unknown likelihood function, (ii) the learning binary neural network, and (iii) neural architecture search. The obtained results indicate the high potential of the proposed framework and the superiority of the new Markov kernel.

scholarly journals An Alternative Promotion Time Cure Model with Overdispersed Number of Competing Causes: An Application to Melanoma Data

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1815
Author(s):  
Diego I. Gallardo ◽  
Mário de Castro ◽  
Héctor W. Gómez
Keyword(s):  
Estimation Method ◽  
Selection Criterion ◽  
Likelihood Method ◽  
Simulation Studies ◽  
Cure Rate ◽  
Nested Models ◽  
Proposed Model ◽  
The Em Algorithm ◽  
Competing Causes ◽  
The One

A cure rate model under the competing risks setup is proposed. For the number of competing causes related to the occurrence of the event of interest, we posit the one-parameter Bell distribution, which accommodates overdispersed counts. The model is parameterized in the cure rate, which is linked to covariates. Parameter estimation is based on the maximum likelihood method. Estimates are computed via the EM algorithm. In order to compare different models, a selection criterion for non-nested models is implemented. Results from simulation studies indicate that the estimation method and the model selection criterion have a good performance. A dataset on melanoma is analyzed using the proposed model as well as some models from the literature.

An Order Fulfillment Model for the LED Chip Manufacturing Plant

2013 ◽  
Vol 694-697 ◽  
pp. 3446-3452 ◽  
Author(s):  
Horng Huei Wu ◽  
Ming Feng Li ◽  
Tzu Fang Hsu
Keyword(s):  
Inventory Control ◽  
Real Life ◽  
Production Model ◽  
Order Fulfillment ◽  
Due Date ◽  
Make To Order ◽  
Proposed Model ◽  
General Production ◽  
Production Strategy ◽  
Production Output

The LED chip manufacturing (LED-CM) is an important process in the LED supply chain. The make-to-order production strategy is a general production model for the LED-CM plants to satisfy the variety requirement of their customers. However, the special features of the unstable production output and a product composed of the chips of different feasible Bins exist in the LED-CM plant. The production planner will confront the issue of effective inventory control and exact due-date performance under the severely competitive pressure. Therefore an effective order fulfillment procedure for production planners is a required key issue to accomplish the inventory control and exact due-date performance. An order fulfillment model for production planner is thus proposed in this paper to meet the requirement of the LED-CM plants. A real-life LED-CM case is also utilized to demonstrate and evaluate the application and effectiveness of the proposed model.

scholarly journals A multidimensional generalized many-facet Rasch model for rubric-based performance assessment

2021 ◽  
Author(s):  
Masaki Uto
Keyword(s):  
Performance Assessment ◽  
Rasch Model ◽  
Measurement Accuracy ◽  
Estimation Method ◽  
Real Data ◽  
Monte Carlo Algorithm ◽  
Irt Model ◽  
Irt Models ◽  
Proposed Model ◽  
Problem Item

AbstractPerformance assessment, in which human raters assess examinee performance in a practical task, often involves the use of a scoring rubric consisting of multiple evaluation items to increase the objectivity of evaluation. However, even when using a rubric, assigned scores are known to depend on characteristics of the rubric’s evaluation items and the raters, thus decreasing ability measurement accuracy. To resolve this problem, item response theory (IRT) models that can estimate examinee ability while considering the effects of these characteristics have been proposed. These IRT models assume unidimensionality, meaning that a rubric measures one latent ability. In practice, however, this assumption might not be satisfied because a rubric’s evaluation items are often designed to measure multiple sub-abilities that constitute a targeted ability. To address this issue, this study proposes a multidimensional IRT model for rubric-based performance assessment. Specifically, the proposed model is formulated as a multidimensional extension of a generalized many-facet Rasch model. Moreover, a No-U-Turn variant of the Hamiltonian Markov chain Monte Carlo algorithm is adopted as a parameter estimation method for the proposed model. The proposed model is useful not only for improving the ability measurement accuracy, but also for detailed analysis of rubric quality and rubric construct validity. The study demonstrates the effectiveness of the proposed model through simulation experiments and application to real data.

Sign in / Sign up

Export Citation Format

Share Document