Development of Pedestrian Recall Versus Actuation Guidelines for Pedestrian Crossings at Signalized Intersections

At signalized intersections, pedestrian phases can be configured as recall or pushbutton actuated. While pedestrian recall results in a moderate reduction in pedestrian delay because, with recall, a pedestrian arriving during the time nominally reserved for the Walk interval will be served immediately rather than waiting to be served in the next cycle, it can also lead to longer cycle lengths, increasing delay for all users, including pedestrians. This research explores the impact of pedestrian recall along a coordinated-actuated arterial for pedestrians crossing the mainline (i.e., crossing the coordinated phase) to provide pedestrian recall versus actuation guidelines for agencies. The guidance was developed with the aim of balancing pedestrian delay with operational efficiency for vehicles. Two criteria were considered while developing the guidance: (1) pedestrian demand; and (2) vehicular green time duration for the concurrent vehicle phase that is parallel to the pedestrian crossing. VISSIM microsimulation software was used on a real network in Fairfax County, Virginia to model the effects of pedestrian recall and actuation. Results showed that pedestrian recall should be considered when pedestrian demand is large enough that there is a pedestrian call in most cycles (pedestrian probability in a given cycle is greater than 0.6 or pedestrian volume per cycle is greater than 0.9). The guidance also suggests setting pedestrian phases on recall when the length of the vehicular green for the concurrent phase is long enough in most cycles that a pedestrian phase would fit without constraining the signal cycle length.

Developing English Speaking Skill Indigenously Among the Prospective Teachers

Present research experimentally studied the effectiveness of indigenously developed Content and Language Integrated Modular Approach (CLIMA) especially designed for developing English language ability among university students. CLIMA is a blend of Content and Language Integrated Approach and the Modular Approach. Two equated groups of total 52 students from Bachelor of Education Programme (semester-I) participated in this randomised pre-test post-test control group experiment. The content used herein comprised a purposefully designed module of 5 units. Both groups were taught by the same specifically trained teacher on same days with an interval of one hour between the sessions with the two groups. Experiment was completed in 30 sessions (1.5 hour each) during 10 weeks. For both pre- and post-testing, the researchers used the Analytic Rubric of Fairfax County Public Schools (Virginia, USA). This Analytic Rubric has been termed as the Performance Assessment for Language Students (PALS). The experimental group witnessed (pre-testing = 31.6%, post-testing = 80.8%) a value addition of 49.2%; and the control group witnessed (pre-testing = 31.2%, post-testing = 66.2%) a value addition of 35.0%. Compared with TOEFL and IELTS, conclusively, CLIMA was found highly effective. Results are discussed in detail in the paper.

Developing English Speaking Skill Indigenously Among the Prospective Teachers

Present research experimentally studied the effectiveness of indigenously developed Content and Language Integrated Modular Approach (CLIMA) especially designed for developing English language ability among university students. CLIMA is a blend of Content and Language Integrated Approach and the Modular Approach. Two equated groups of total 52 students from Bachelor of Education Programme (semester-I) participated in this randomised pre-test post-test control group experiment. The content used herein comprised a purposefully designed module of 5 units. Both groups were taught by the same specifically trained teacher on same days with an interval of one hour between the sessions with the two groups. Experiment was completed in 30 sessions (1.5 hour each) during 10 weeks. For both pre- and post-testing, the researchers used the Analytic Rubric of Fairfax County Public Schools (Virginia, USA). This Analytic Rubric has been termed as the Performance Assessment for Language Students (PALS). The experimental group witnessed (pre-testing = 31.6%, post-testing = 80.8%) a value addition of 49.2%; and the control group witnessed (pre-testing = 31.2%, post-testing = 66.2%) a value addition of 35.0%. Compared with TOEFL and IELTS, conclusively, CLIMA was found highly effective. Results are discussed in detail in the paper.

A Multilevel Eigenvector Spatial Filtering Model of House Prices: A Case Study of House Sales in Fairfax County, Virginia

House prices tend to be spatially correlated due to similar physical features shared by neighboring houses and commonalities attributable to their neighborhood environment. A multilevel model is one of the methodologies that has been frequently adopted to address spatial effects in modeling house prices. Empirical studies show its capability in accounting for neighborhood specific spatial autocorrelation (SA) and analyzing potential factors related to house prices at both individual and neighborhood levels. However, a standard multilevel model specification only considers within-neighborhood SA, which refers to similar house prices within a given neighborhood, but neglects between-neighborhood SA, which refers to similar house prices for adjacent neighborhoods that can commonly exist in residential areas. This oversight may lead to unreliable inference results for covariates, and subsequently less accurate house price predictions. This study proposes to extend a multilevel model using Moran eigenvector spatial filtering (MESF) methodology. This proposed model can take into account simultaneously between-neighborhood SA with a set of Moran eigenvectors as well as potential within-neighborhood SA with a random effects term. An empirical analysis of 2016 and 2017 house prices in Fairfax County, Virginia, illustrates the capability of a multilevel MESF model specification in accounting for between-neighborhood SA present in data. A comparison of its model performance and house price prediction outcomes with conventional methodologies also indicates that the multilevel MESF model outperforms standard multilevel and hedonic models. With its simple and flexible feature, a multilevel MESF model can furnish an appealing and useful approach for understanding the underlying spatial distribution of house prices.

The Role of Gang Involvement as a Protective Factor in the Association Between Peer Victimization and Negative Emotionality

Gang involvement is associated with many negative outcomes. However, the social and emotional development of gang-involved youth has received little empirical investigation. This study examines the social and emotional outcomes of gang-involved youth. Data come from the 2009 Fairfax County Youth Survey administered to eighth, 10th, and 12th grade students ( N = 27,869, 50% female, 55% minority). Hierarchical logistic regression was used to test the associations between victimization and negative emotionality, and the potential moderating effect of age and gang involvement. Results showed a positive relationship between victimization and negative emotionality. Youth involved in gangs were more likely to experience victimization. However, the association between peer victimization and negative emotionality was diminished for youth in gangs compared with those not in gangs. In addition, results showed that negative emotional outcomes from victimization were worse for middle school compared with high school students.

Developing a Data-driven Approach to inform Planning in County Health and Human Services Departments in the Context of a Case Study on Obesity

Since the 1970s, the obesity rate has steadily increased due to growing availability of food and declining physical activity. The existing environments within a community, including active recreation opportunities, access to healthy food options, the built environment, and transportation options, can moderate obesity. In Virginia, Fairfax County Health and Human Services (HHS) system is interested in developing the capacity for data-driven approaches to gain insights on current and future issues, such as obesity, to characterize factors at the county and sub-county level, and to use these insights to inform policy options. In exploring these questions, we developed statistical methods to combined data from a multitude of different sources including local administrative data (e.g., tax assessments, land use, student surveys), place-based data, and federal collections. Using synthetic data methods based on imputation, we recomputed American Community Survey statistics for non-Census tract geographic regions for political districts and high school attendance areas. We combined this with environmental factors, such as land dedicated to parks and recreation facilities, as well as measures of the density of healthy and unhealthy food locations to create a map of potentially obesogenic factors. Finally, we combined these data sources with Fairfax County’s youth survey and trained a random forest model to predict the effects of the environment on healthy food consumption and exercise. Our analysis highlights the need for (administrative) data at a fine scale and recommends policy changes concerning the recording and sharing of local data to better inform the policy and program development.