scholarly journals Cooperative Waiting Time Estimation Method Based on Crowd Behavior Characteristics Using Acceleration and RSSI

2019 ◽  
Vol 23 (1) ◽  
pp. 1-8
Author(s):  
Tomoya Hayakawa ◽  
Masakatsu Ogawa
Keyword(s):  
Waiting Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Crowd Behavior ◽  
Behavior Characteristics

Short-Term Vehicle Velocity Forecasting via Cycle Segmentation Model Selection

2021 ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Model Selection ◽  
Waiting Time ◽  
Short Term Memory ◽  
Critical Role ◽  
Time Estimation ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Vehicle Velocity ◽  
Selection Framework

Abstract Vehicle velocity forecasting plays a critical role in scheduling the operations of varying systems and devices in a passenger vehicle. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to contribute to the improvement of vehicle energy efficiency for commuting routes. The generated driving cycles are divided into cycle segments based on intersection/stop identification, deceleration and reacceleration identification, and waiting time estimation, which could be used for better evaluating the effectiveness of model localization. Then, a segment-based vehicle velocity forecasting model is developed, where a machine learning model is trained/developed at each segment, using the hidden Markov chain (HMM) model and long short-term memory network (LSTM). To further improve the forecasting accuracy, a localized model selection framework is developed, which can dynamically choose a forecasting model (i.e., HMM or LSTM) for each segment. Results show that (i) the segment-based forecast could improve the forecasting accuracy by up to 24%, compared the whole cycle-based forecast; and (ii) the localized model selection framework could further improve the forecasting accuracy by 6.8%, compared to the segment-based LSTM model. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.

Reasoning: Source of Variability in the Boothroyd and Dewhurst Assembly Time Estimation Method

2012 ◽  
Author(s):  
Essam Namouz ◽  
Joshua D. Summers ◽  
Gregory M. Mocko
Keyword(s):  
Engineering Students ◽  
Large Range ◽  
Estimation Method ◽  
Time Estimation ◽  
Handling Time ◽  
Time Estimate ◽  
Time Analysis ◽  
Assembly Time ◽  
Time Estimates ◽  
Initial Results

This paper evaluates the effect of making a subjective decision in a design for assembly time analysis. An example is found in the first set of questions for estimating handling time of a part the user chose “parts are easy to grasp and manipulate” as opposed to “parts present handling difficulties”. The subjectivity is explored through a study of assembly time estimates generated by a class of mechanical engineering students in the time analysis of a clicker pen based on the Boothroyd and Dewhurst estimation method. The assembly times calculated by the class ranged from a minimum of 23.64 seconds to a maximum of 44.89 seconds (range of 21.25 seconds). This large range in results serves as motivation in determining the effect that answering a subjective decision has on the resulting assembly time estimate. Initial results indicate that not answering the first level of subjective questions will result in assembly time estimate within 15% of the time had the subjective question been answered. The probability density plots of the time estimates also indicates that 63% of the time, the estimated assembly time without making the subjective decision will fall within the normal distribution had the subjective decision been made. This provides evidence that there is an opportunity to reduce the amount of subjective questions that a user must answer to estimate the assembly time of a product.

Time Estimation Methods—Do They Influence Prospective Duration Estimates?

Perception ◽  
1993 ◽  
Vol 22 (1) ◽  
pp. 91-101 ◽  
Author(s):  
Dan Zakay
Keyword(s):  
Information Processing ◽  
Positive Relationship ◽  
Negative Relationship ◽  
Estimation Method ◽  
Time Estimation ◽  
Estimation Methods ◽  
Clock Time ◽  
Processing Load ◽  
Attentional Model ◽  
Time Length

The validity of an attentional model of prospective time estimation was tested in three experiments. In the first experiment two variables were manipulated: (1) nontemporal information processing load during the estimated interval, and (2) time estimation method, ie production of time simultaneously with the performance of a second task, or reproduction of time immediately upon termination of a task whose duration has to be measured. As predicted, a positive relationship between produced time length and information processing load demanded by a simultaneous task, and a negative relationship between reproduced time length and information processing load during the estimated interval, were found. The results were replicated in a second experiment in which verbal estimates of time were also measured and the objective duration of the estimated interval was varied. The pattern of results obtained for verbal estimates was similar to that obtained for reproduced ones. The results of a third experiment indicated that produced and reproduced times were positively correlated with clock time. The results are interpreted as supporting an attentional model of prospective time estimation.

Soft Transformer Energization: Ramping Time Estimation Method for Inrush Current Mitigation

2021 ◽  
Author(s):  
Abdulrahman Alassi ◽  
Khaled Ahmed ◽  
Agusti Egea-Alvarez ◽  
Colin Foote
Keyword(s):  
Estimation Method ◽  
Time Estimation ◽  
Inrush Current

scholarly journals A new lost time estimation method for right-turn traffic in Japan considering signal phasing and sneakers

2012 ◽  
Vol 47 (8) ◽  
pp. 704-719 ◽  
Author(s):  
Keshuang Tang ◽  
Takeshi Ono ◽  
Masao Kuwahara ◽  
Shinji Tanaka
Keyword(s):  
Estimation Method ◽  
Time Estimation ◽  
Lost Time

scholarly journals Life Time Estimation Method for the Feeder Transformers

2017 ◽  
Vol 58 (4) ◽  
pp. 277-284 ◽  
Author(s):  
Masataka AKAGI ◽  
Tsurugi YOSHII ◽  
Hideki IMAMURA
Keyword(s):  
Estimation Method ◽  
Time Estimation ◽  
Life Time

Travel time estimation method for urban road based on traffic stream directions

2016 ◽  
Vol 12 (6) ◽  
pp. 479-503 ◽  
Author(s):  
Dianhai Wang ◽  
Fengjie Fu ◽  
Xiaoqin Luo ◽  
Sheng Jin ◽  
Dongfang Ma
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Road ◽  
Traffic Stream
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