Performance Measures for Short Term Traffic Condition Forecasting Algoithms

2013 ◽  
Author(s):  
Guo Jianhua
Keyword(s):  
Performance Measures ◽  
Short Term ◽  
Traffic Condition

Optimization of Dynamic Neural Network Performance for Short-Term Traffic Prediction

2003 ◽  
Vol 1836 (1) ◽  
pp. 45-56 ◽  
Author(s):  
Sherif Ishak ◽  
Prashanth Kotha ◽  
Ciprian Alecsandru
Keyword(s):  
Neural Networks ◽  
Network Performance ◽  
Information Dissemination ◽  
Target Location ◽  
Prediction Performance ◽  
Time Factor ◽  
Traffic Prediction ◽  
Short Term ◽  
Traffic Condition ◽  
Dynamic Neural Networks

An approach is presented for optimizing short-term traffic-prediction performance by using multiple topologies of dynamic neural networks and various network-related and traffic-related settings. The conducted study emphasized the potential benefit of optimizing the prediction performance by deploying multimodel approaches under parameters and traffic-condition settings. Emphasis was placed on the application of temporal-processing topologies in short-term speed predictions in the range of 5-min to 20-min horizons. Three network topologies were used: Jordan–Elman networks, partially recurrent networks, and time-lagged feedforward networks. The input patterns were constructed from data collected at the target location and at upstream and downstream locations. However, various combinations were also considered. To encourage the networks to associate with historical information on recurrent conditions, a time factor was attached to the input patterns to introduce time-recognition capabilities, in addition to information encoded in the recent past data. The optimal prediction settings (type of topology and input settings) were determined so that performance was maximized under different traffic conditions at the target and adjacent locations. The optimized performance of the dynamic neural networks was compared to that of a statistical nonlinear time series approach, which was outperformed in most cases. The study showed that no single topology consistently outperformed the others for all prediction horizons considered. However, the results showed that the significance of introducing the time factor was more pronounced under longer prediction horizons. A comparative evaluation of performance of optimal and nonoptimal settings showed substantial improvement in most cases. The applied procedure can also be used to identify the prediction reliability of information-dissemination systems.

scholarly journals Cold-Water Immersion and Contrast Water Therapy: No Improvement of Short-Term Recovery After Resistance Training

2017 ◽  
Vol 12 (7) ◽  
pp. 886-892 ◽  
Author(s):  
Christos K. Argus ◽  
James R. Broatch ◽  
Aaron C. Petersen ◽  
Remco Polman ◽  
David J. Bishop ◽  
...  
Keyword(s):  
Resistance Training ◽  
Performance Measures ◽  
Cold Water ◽  
Water Immersion ◽  
Training Session ◽  
Cold Water Immersion ◽  
Short Term ◽  
Recovery Strategies ◽  
And Performance ◽  
Contrast Water Therapy

Context:An athlete’s ability to recover quickly is important when there is limited time between training and competition. As such, recovery strategies are commonly used to expedite the recovery process.Purpose:To determine the effectiveness of both cold-water immersion (CWI) and contrast water therapy (CWT) compared with control on short-term recovery (<4 h) after a single full-body resistance-training session.Methods:Thirteen men (age 26 ± 5 y, weight 79 ± 7 kg, height 177 ± 5 cm) were assessed for perceptual (fatigue and soreness) and performance measures (maximal voluntary isometric contraction [MVC] of the knee extensors, weighted and unweighted countermovement jumps) before and immediately after the training session. Subjects then completed 1 of three 14-min recovery strategies (CWI, CWT, or passive sitting [CON]), with the perceptual and performance measures reassessed immediately, 2 h, and 4 h postrecovery.Results:Peak torque during MVC and jump performance were significantly decreased (P < .05) after the resistance-training session and remained depressed for at least 4 h postrecovery in all conditions. Neither CWI nor CWT had any effect on perceptual or performance measures over the 4-h recovery period.Conclusions:CWI and CWT did not improve short-term (<4-h) recovery after a conventional resistance-training session.

scholarly journals Short-Term Traffic Volume Forecasting with Asymmetric Loss Based on Enhanced KNN Method

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Zhiyuan Wang ◽  
Shouwen Ji ◽  
Bowen Yu
Keyword(s):  
Euclidean Distance ◽  
Intelligent Transportation System ◽  
Nearest Neighbor ◽  
Mean Squared Error ◽  
Essential Elements ◽  
Traffic Volume ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Traffic Condition ◽  
Asymmetric Loss

Short-term traffic volume forecasting is one of the most essential elements in Intelligent Transportation System (ITS) by providing prediction of traffic condition for traffic management and control applications. Among previous substantial forecasting approaches, K nearest neighbor (KNN) is a nonparametric and data-driven method popular for conciseness, interpretability, and real-time performance. However, in previous related researches, the limitations of Euclidean distance and forecasting with asymmetric loss have rarely been focused on. This research aims to fill up these gaps. This paper reconstructs Euclidean distance to overcome its limitation and proposes a KNN forecasting algorithm with asymmetric loss. Correspondingly, an asymmetric loss index, Imbalanced Mean Squared Error (IMSE), has also been proposed to test the effectiveness of newly designed algorithm. Moreover, the effect of Loess technique and suitable parameter value of dynamic KNN method have also been tested. In contrast to the traditional KNN algorithm, the proposed algorithm reduces the IMSE index by more than 10%, which shows its effectiveness when the cost of forecasting residual direction is notably different. This research expands the applicability of KNN method in short-term traffic volume forecasting and provides an available approach to forecast with asymmetric loss.

How to Pay Nonfamily Managers in Large Family Firms: A Principal—Agent Model

2011 ◽  
Vol 24 (1) ◽  
pp. 9-27 ◽  
Author(s):  
Jörn H. Block
Keyword(s):  
Performance Measures ◽  
Measurement Errors ◽  
Family Firms ◽  
Large Family ◽  
Principal Agent ◽  
Short Term ◽  
Compensation Contracts ◽  
Agent Model ◽  
Principal Agent Model ◽  
Term Performance

A large number of family firms employ nonfamily managers. This article analyzes the optimal compensation contracts of nonfamily managers employed by family firms using principal—agent analysis. The model shows that the contracts should have low incentive levels in terms of short-term performance measures. This finding is moderated by nonfamily managers’ responsiveness to incentives, their level of risk aversion, and measurement errors of effort related to short-term performance. The model allows a comparison between the contracts of family and nonfamily managers. This comparison shows that the contracts of family managers should include relatively greater incentives in terms of short-term performance measures. A number of propositions regarding the compensation of nonfamily managers employed by family firms are formulated. The implications of the model for family business research and practice are discussed.

scholarly journals Univariate Time Series Analysis of Short-Term Forecasting Horizons Using Artificial Neural Networks: The Case of Public Ambulance Emergency Preparedness

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tichaona W. Mapuwei ◽  
Oliver Bodhlyera ◽  
Henry Mwambi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Performance Measures ◽  
Test Statistic ◽  
Short Term ◽  
Neural Network Models ◽  
Linear Pattern ◽  
Univariate Time Series ◽  
Artificial Neural ◽  
Short Term Forecasting

This study examined the applicability of artificial neural network models in modelling univariate time series ambulance demand for short-term forecasting horizons in Zimbabwe. Bulawayo City Councils’ ambulance services department was used as a case study. Two models, feed-forward neural network (FFNN) and seasonal autoregressive integrated moving average, (SARIMA) were developed using monthly historical data from 2010 to 2017 and compared against observed data for 2018. The mean absolute error (MAE), root mean square error (RMSE), and paired sample t-test were used as performance measures. Calculated performance measures for FFNN were MAE (94.0), RMSE (137.19), and the test statistic value p=0.493(>0.05) whilst corresponding values for SARIMA were 105.71, 125.28, and p=0.005(<0.05), respectively. Findings of this study suggest that the FFNN model is inclined to value estimation whilst the SARIMA model is directional with a linear pattern over time. Based on the performance measures, the parsimonious FFNN model was selected to predict short-term annual ambulance demand. Demand forecasts with FFNN for 2019 reflected the expected general trends in Bulawayo. The forecasts indicate high demand during the months of January, March, September, and December. Key ambulance logistic activities such as vehicle servicing, replenishment of essential equipment and drugs, staff training, leave days scheduling, and mock drills need to be planned for April, June, and July when low demand is anticipated. This deliberate planning strategy would avoid a dire situation whereby ambulances are available but without adequate staff, essential drugs, and equipment to respond to public emergency calls.

Principals with and without performance measures means no change?

2014 ◽  
Vol 27 (3) ◽  
pp. 520-531 ◽  
Author(s):  
Erik Lindberg
Keyword(s):  
Student Performance ◽  
Performance Measures ◽  
Cross Sectional Study ◽  
School Development ◽  
Student Outcome ◽  
Short Term ◽  
Cross Sectional ◽  
Content Type ◽  
Educational Issues

Purpose – This paper seeks to explore how principals use their time when the requirement exceeds the activities are desirable. In the scholarly debate it has been pointed out the heads think that too much time is devoted for the financial and administrative issues, or to solve acute problems. This means that there is not enough time to work with educational issues. The purpose of this paper is to clarify how principals use the time they have devoted for the educational area and what activities they prioritize. It will also increase the knowledge of reasons behind their prioritizing and reflect on some of the consequences. Results relate to the question if introduction of performance measures has increased a short-term perspective on student performance or if it works as a suitable tool for the principals to achieve the schools goals and to create more effective schools in the long run. The question if stakeholders can get required insight by the performance measures as they are designed today and if the principals got the right incentives is raised. Design/methodology/approach – A quantitative approach is used and a mail questionnaire was distributed to the principals in all upper secondary schools in Sweden and a comparative cross-sectional study was conducted. Findings – Principals’ perceptions suggest that, their prioritization when working with educational issues is influenced by a more short-term perspective and that they prioritize teaching, which have a much faster impact on student outcome, over long-term school development which facilitate the conditions for the former. These findings increase the insight into the need, for as well stakeholders as principals, to develop performance measures to stimulate change when needed. Practical implications – These findings have implications on the direction of the development of performance measures. The result points out the lack of transparence for stakeholders and uncovers the need to know when change and long-term development is ongoing or not. The study show how principals need incentives for prioritizing these activities and that this can be done by the stakeholder by designing required measurements for as well teaching as long-term school development when change is needed or to maintain a successful process. Originality/value – This paper fulfills an identified need to study how the performance measures of today can be complemented with measures for stakeholders for increased insight in ongoing activities with development and required change for long-term school success.

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