scholarly journals Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7431
Author(s):  
Suhaib Alshayeb ◽  
Aleksandar Stevanovic ◽  
B. Brian Park
Keyword(s):  
Prediction Models ◽  
Future Research ◽  
Signal Timing ◽  
Vehicle Type ◽  
Multigene Genetic Programming ◽  
K Factor ◽  
Traffic Signal Timing ◽  
Road Gradient ◽  
The Individual ◽  
Pure Delay

Transportation agencies optimize signals to improve safety, mobility, and the environment. One commonly used objective function to optimize signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimize fuel consumption (FC). The critical component of the PI is the stop penalty “K,” which expresses an FC stop equivalency estimated in seconds of pure delay. This study applies vehicular trajectory and FC data collected in the field, for a large fleet of modern vehicles, to compute the K-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is utilized to develop prediction models for the K-factor. The proposed K-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behavior, idling FC, and the deceleration duration. A parametric analysis is carried out to check the developed models’ quality in capturing the individual impact of the included parameters on the K-factor. The developed models showed an excellent performance in estimating the K-factor under multiple conditions. Future research shall evaluate the findings by using field-based K-values in optimizing signals to reduce FC.

Fuel Consumption Intersection Control Performance Index

2021 ◽  
pp. 036119812110041
Author(s):  
Aleksandar Stevanovic ◽  
Suhaib Al Shayeb ◽  
Satya S. Patra
Keyword(s):  
Fuel Consumption ◽  
Traffic Control ◽  
Performance Index ◽  
Field Data ◽  
Future Research ◽  
Signal Timing ◽  
Data Set ◽  
K Factor ◽  
Accepted Practice ◽  
Intersection Control

The Performance Index (PI), a widely used composite measure of vehicular stops and delays, is one of the most popular traffic signal performance measures. Over the decades it has been used to achieve a proper balance between delays and stops. Its key component, the “stop penalty,” has been used to minimize excess fuel consumption from unnecessary stops caused by traffic control operations. In signal optimization practice this stop penalty, also known as the K factor, has been set as an invariable parameter with a relatively low value ∼10 to 20. This paper questions this widely accepted practice. It first explains the origins and meaning of the PI and the significance of the K factor. Then, it lists various studies, discusses their inconsistencies, and introduces a new Fuel Consumption Intersection Control PI (FCIC-PI). The paper also presents findings from field data collection and compares them with the other studies, including some simulation results. Outcomes of these various findings show some inconsistencies, but all point to the existing practice being wrong: the K factor is a variable dependent on at least one important factor—cruising speed. The outcomes also indicate that K values should, if fuel consumption is to be minimized, be larger than currently used. Future research should confirm these findings with a larger field data set, investigate other factors that affect the stop penalty, and consider a family of other emission-related PIs. Finally, a new methodology should be developed to properly integrate these new PIs into signal timing optimization.

Development of a German-Language Questionnaire to Measure Collective Orientation as an Individual Attitude

2017 ◽  
Vol 76 (3) ◽  
pp. 91-105 ◽  
Author(s):  
Vera Hagemann
Keyword(s):  
Team Performance ◽  
Reliability And Validity ◽  
Future Research ◽  
German Language ◽  
Team Members ◽  
Collective Orientation ◽  
Team Research ◽  
German Speaking ◽  
Individual Variables ◽  
The Individual

Abstract. The individual attitudes of every single team member are important for team performance. Studies show that each team member’s collective orientation – that is, propensity to work in a collective manner in team settings – enhances the team’s interdependent teamwork. In the German-speaking countries, there was previously no instrument to measure collective orientation. So, I developed and validated a German-language instrument to measure collective orientation. In three studies (N = 1028), I tested the validity of the instrument in terms of its internal structure and relationships with other variables. The results confirm the reliability and validity of the instrument. The instrument also predicts team performance in terms of interdependent teamwork. I discuss differences in established individual variables in team research and the role of collective orientation in teams. In future research, the instrument can be applied to diagnose teamwork deficiencies and evaluate interventions for developing team members’ collective orientation.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Academic Events: An Empirically Grounded Typology and Their Academic Impact

2020 ◽  
Vol 24 (4) ◽  
pp. 481-497 ◽  
Author(s):  
Thomas Trøst Hansen ◽  
David Budtz Pedersen ◽  
Carmel Foley
Keyword(s):  
Analytical Framework ◽  
Future Research ◽  
Academic Impact ◽  
Future Research Agenda ◽  
Tourism Studies ◽  
Academic Events ◽  
Business Events ◽  
The Individual ◽  
Analytical Frameworks ◽  
Academic Sector

The meetings industry, government bodies, and scholars within tourism studies have identified the need to understand the broader impact of business events. To succeed in this endeavor, we consider it necessary to develop analytical frameworks that are sensitive to the particularities of the analyzed event, sector, and stakeholder group. In this article we focus on the academic sector and offer two connected analyses. First is an empirically grounded typology of academic events. We identify four differentiating dimensions of academic events: size, academic focus, participants, and tradition, and based on these dimensions we develop a typology of academic events that includes: congress, specialty conference, symposium, and practitioners' meeting. Secondly, we outline the academic impact of attending these four types of events. For this purpose, the concept of credibility cycles is used as an analytical framework for examining academic impact. We suggest that academic events should be conceptualized and evaluated as open marketplaces that facilitate conversion of credibility. Data were obtained from interviews with 22 researchers at three Danish universities. The study concludes that there are significant differences between the events in terms of their academic impact. Moreover, the outcome for the individual scholar depends on the investment being made. Finally, the study calls for a future research agenda on beyond tourism benefits based on interdisciplinary collaborations.

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

2020 ◽  
Vol 27 (5) ◽  
pp. 385-391
Author(s):  
Lin Zhong ◽  
Zhong Ming ◽  
Guobo Xie ◽  
Chunlong Fan ◽  
Xue Piao
Keyword(s):  
Supervised Learning ◽  
Protein Interactions ◽  
Computational Models ◽  
Prediction Models ◽  
Chromatin Modification ◽  
Computational Prediction ◽  
Human Diseases ◽  
Future Research ◽  
Non Coding Rna ◽  
Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

Physiology of Larval Feeding

2018 ◽  
Keyword(s):  
Growth And Development ◽  
Marine Invertebrate ◽  
Larval Growth ◽  
Circulatory System ◽  
Future Research ◽  
Larval Feeding ◽  
Digestive Physiology ◽  
Larval Body ◽  
The Individual ◽  
Invertebrate Larvae

The functional properties of marine invertebrate larvae represent the sum of the physiological activities of the individual, the interdependence among cells making up the whole, and the correct positioning of cells within the larval body. This chapter examines physiological aspects of nutrient acquisition, digestion, assimilation, and distribution within invertebrate larvae from an organismic and comparative perspective. Growth and development of larvae obviously require the acquisition of “food.” Yet the mechanisms where particulate or dissolved organic materials are converted into biomass and promote development of larvae differ and are variably known among groups. Differences in the physiology of the digestive system (secreted enzymes, gut transit time, and assimilation) within and among feeding larvae suggest the possibility of an underappreciated plasticity of digestive physiology. How the ingestion of seawater by and the existence of a circulatory system within larvae contribute to larval growth and development represent important topics for future research.

The Role of Normative Information in Judgments of Others

2019 ◽  
Author(s):  
Katherine H. Rogers
Keyword(s):  
Social Desirability ◽  
Interpersonal Perception ◽  
Future Research ◽  
Average Person ◽  
The Individual ◽  
The Relationship ◽  
Source Of Information

When forming impressions of an other’s personality, people often rely on information not directly related to the individual at hand. One source of information that can influence people’s impressions of others is the personality of the average person (i.e., normative profile). This relationship between the normative profile and an impression is called normative accuracy or normativity. In this chapter, you will learn about the average personality, why it is important, the relationship to social desirability and what it means to have a normative impression, as well as correlates and moderators of normativity. More broadly, you will learn about current research and views regarding the normative profile and normative impressions as well as concrete steps for incorporating this approach into your future research on interpersonal perception.

scholarly journals Opportunities and Challenges of Green-Lean: An Integrated System for Sustainable Construction

2020 ◽  
Vol 12 (11) ◽  
pp. 4460 ◽  
Author(s):  
Mohammadsoroush Tafazzoli ◽  
Ehsan Mousavi ◽  
Sharareh Kermanshachi
Keyword(s):  
Cost Savings ◽  
Integrated Approach ◽  
Integrated System ◽  
Waste Minimization ◽  
Sustainable Construction ◽  
Future Research ◽  
Safety Improvement ◽  
The Individual ◽  
High Level ◽  
Industry Professionals

Although the two concepts of lean and sustainable construction have been developed due to different incentives, and they do not pursue the same exact goals, there exists considerable commonality between them. This paper discusses the potentials for integrating the two approaches and their practices and how the resulting synergy from combining the two methods can potentially lead to higher levels of fulfilling the individual goals of each of them. Some limitations and challenges to implementing the integrated approach are also discussed. Based on a comprehensive review of existing papers related to sustainable and lean construction topics, the commonality between the two approaches is discussed and grouped in five categories of (1) cost savings, (2) waste minimization, (3) Jobsite safety improvement, (4) reduced energy consumption, and (5) customers’ satisfaction improvement. The challenges of this integration are similarly identified and discussed in the four main categories of (1) additional initial costs to the project, (2) difficulty of providing specialized expertise, (3) contractors’ unwillingness to adopt the additional requirements, and (4) challenges to establish a high level of teamwork. Industry professionals were then interviewed to rank the elements in each of the two categories of opportunities and challenges. The results of the study highlight how future research can pursue the development of a new Green-Lean approach by investing in the communalities and meeting the challenges of this integration.

scholarly journals Validity of the Gender Dysphoria diagnosis and incidence trends in Sweden: a nationwide register study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Malin Indremo ◽  
Richard White ◽  
Thomas Frisell ◽  
Sven Cnattingius ◽  
Alkistis Skalkidou ◽  
...  
Keyword(s):  
Gender Dysphoria ◽  
Age Groups ◽  
Medical Intervention ◽  
Future Research ◽  
Advantages And Disadvantages ◽  
Incidence Trends ◽  
Interaction Terms ◽  
Definition Of ◽  
The Individual ◽  
Nationwide Register Study

AbstractThe aim of this study was to examine the validity of the Gender Dysphoria (GD) diagnoses in the Swedish National Patient Register (NPR), to discuss different register-based definitions of GD and to investigate incidence trends. We collected data on all individuals with registered GD diagnoses between 2001 and 2016 as well as data on the coverage in the NPR. We regarded gender confirming medical intervention (GCMI) as one proxy for a clinically valid diagnosis and calculated the positive predictive value (PPV) for receiving GCMI for increasing number of registered GD diagnoses. We assessed crude and coverage-adjusted time trends of GD during 2004–2015 with a Poisson regression, using assigned sex and age as interaction terms. The PPV for receiving GCMI was 68% for ≥ 1 and 79% for ≥ 4 GD-diagnoses. The incidence of GD was on average 35% higher with the definition of ≥ 1 compared to the definition of ≥ 4 diagnoses. The incidence of GD, defined as ≥ 4 diagnoses increased significantly during the study period and mostly in the age categories 10–17 and 18–30 years, even after adjusting for register coverage. We concluded that the validity of a single ICD code denoting clinical GD in the Swedish NPR can be questioned. For future research, we propose to carefully weight the advantages and disadvantages of different register-based definitions according to the individual study’s needs, the time periods involved and the age-groups under study.

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