scholarly journals Assessing Preference Heterogeneity for Mobility-on-Demand Transit Service in Low-Income Communities: A Latent Segmentation Based Decision Tree Method

2021 ◽  
Author(s):  
Xilei Zhao ◽  
Xinyi Wang ◽  
Xiang Yan ◽  
Zhuoxuan Cao
Keyword(s):  
Decision Tree ◽  
Low Income ◽  
Latent Class ◽  
Stated Preference ◽  
Important Variable ◽  
Preference Heterogeneity ◽  
Step Method ◽  
Transit System ◽  
Transit Service ◽  
On Demand

Abstract The future of public transit service is often envisioned as Mobility-on-Demand (MOD), i.e., a system that integrates fixed routes and shared on-demand shuttles. The MOD transit system has the potential to provide better transit service with higher efficiency and coverage. However, little research has focused on understanding traveler preferences for MOD transit and preference heterogeneity, especially among the disadvantaged population. This study addresses this gap by proposing a two-step method, called latent segmentation based decision tree (LSDT). This method first uses a latent class cluster analysis (LCCA) that extracts traveler profiles who have similar usage patterns for shared modes. Then, decision trees (DT) are adopted to reveal the associations between various factors with preferences for MOD transit across different clusters. We collected stated-preference data among two low-resource communities, i.e., Detroit and Ypsilanti, Michigan. The LCCA model divides the entire sample into three clusters, i.e., shared-mode users, shared-mode non-users, and transit-only users. We find that job accessibility by transit is the most important variable for all the cluster-specific DT to model the MOD transit preference, and it negatively associated with the MOD transit preference. For transit-only users, gender and car ownership are the second-important variables, but neither of them appears in the DT for the other two clusters. In particular, female transit-only users have lower preference for MOD transit, possibly due to safety concerns. The LSDT method can generate richer insights than a single DT fitted to the overall sample by better accounting for heterogeneity. The findings gained from this approach can inform better-targeted strategies to plan for MOD transit services.

scholarly journals A Simple Stated Preference Tool for Estimating the Value of Travel Time in Rural Africa

2016 ◽  
Vol 7 (2) ◽  
pp. 221-247 ◽  
Author(s):  
Joseph Cook ◽  
Peter Kimuyu ◽  
Annalise G. Blum ◽  
Josephine Gatua
Keyword(s):  
Travel Time ◽  
Low Income ◽  
Rural Areas ◽  
Latent Class ◽  
Stated Preference ◽  
Empirical Support ◽  
Water Source ◽  
Low Income Countries ◽  
Significant Heterogeneity ◽  
Average Value

Despite its importance in benefit-cost analyses in the water supply, transportation, and health care sectors, there are relatively few empirical estimates of the value of travel time savings (VTT) in low-income countries, particularly in rural areas. Analysts instead often rely on a textbook “rule of thumb” of valuing time at 50% of prevailing unskilled wage rates, though these benchmarks have little empirical support in these settings. We estimate the value of travel time through the use of a repeated discrete choice stated preference exercise. We asked 325 rural households in Meru County, Kenya to rank two new hypothetical water sources against their current water source. The two new hypothetical sources were described as safe and reliable to use, but varied only in their distance from the household and the price charged per water container. Results from random-parameters logit models imply an average value of travel time of 18 Ksh/hr, and generally support the 50% rule. These models produce the first individual-level VTT estimates reported in a low-income setting, and indicate statistically-significant heterogeneity in VTTs, though the heterogeneity is not well correlated with observables. A latent-class approach identifies four classes of respondents: one class (about one third of respondents) values time very highly (49 Ksh/hr), one poorer group values time hardly at all (less than 1 Ksh/hr), and two groups value time at approximately 9 Ksh/hr.

scholarly journals On-Site Experience Effect on Stakeholders’ Preferences of Forest Management

2020 ◽  
Vol 12 (19) ◽  
pp. 7845
Author(s):  
Xiaoshu Li ◽  
G. Andrew Stainback
Keyword(s):  
Forest Management ◽  
General Public ◽  
Management Practices ◽  
Latent Class ◽  
Policy Design ◽  
Stated Preference ◽  
Preference Heterogeneity ◽  
Substantial Impact ◽  
Before And After ◽  
Management Policies

An understanding of how public preferences vary among different stakeholders toward forest management policies would be helpful in the forest policy design and administration process. In this study, we investigate the preferences toward forest management policies of three stakeholder groups-woodlot owners, environmentalists, and the general public. We used a stated-preference survey to elicit information about stakeholder preferences for forest management practices at Holt Research Forest in Maine. The survey was administered to each group both before and after an on-site experience at the forest. We specifically investigated how information and experience acquired through the on-site experience would influence the preferences of each group. We also conducted a latent class analysis to further explore the preference heterogeneity among survey participants. The results show differences in preferences for forest management policies between stakeholders with the preferences of woodlot owners differing substantially from environmentalists and the general public both before and after the on-site experience. The on-site experience did not have a substantial impact on woodlot owners. In contrast, it increased the consistency of choice decisions among environmentalists and the public.

A Study on Demand for Micro Insurance for Low-Income Households in Disaster-Prone Areas of Indonesia

2011 ◽  
Author(s):  
Viverita . ◽  
Ririen Setiati Rianti ◽  
Abdurrahman Sunanta ◽  
Ida Ayu Agung Faradynawati
Keyword(s):  
Low Income ◽  
On Demand ◽  
Micro Insurance ◽  
Low Income Households

Measuring Attitudes and Health Beliefs Among Mexican Americans With Diabetes

1994 ◽  
Vol 20 (3) ◽  
pp. 221-227 ◽  
Author(s):  
Terri Schwab ◽  
Julie Meyer ◽  
Rosa Merrell
Keyword(s):  
Health Beliefs ◽  
Low Income ◽  
Cultural Sensitivity ◽  
Mexican Americans ◽  
Mexican American ◽  
Health Belief ◽  
Important Variable ◽  
Patients With Diabetes ◽  
Health Related ◽  
Belief Model

Adherence to the treatment regimen for patients with diabetes is of major concern to healthcare practitioners, particularly when dealing with the high-risk, low-income, Mexican-American population. Assessing the attitudes and beliefs of this group is vital for planning effective and realistic intervention strategies. Therefore, we designed a culturally sensitive instrument to measure health beliefs and attitudes of low-income Mexican Americans with diabetes. The Health Belief Model (HBM) was used as a basis for this study because it is well accepted as a predictor of health-related behaviors. However, we found that the HBM was not an effective tool for assessing the health beliefs or attitudes of this patient population even after rigorous efforts to operationalize the HBM and after conducting extensive statistical analyses. Only two of the five subscales of the traditional HBM, barriers and benefits, were reliable. Scales to measure acculturation and fatalism were added to increase the cultural sensitivity of the tool. These added components were found to be an important variable in interpreting the results for low-income Mexican-American patients.

Attribute Non-Attendance and Consumer Preferences for Online Food Products in Germany

2020 ◽  
Vol 69 (1) ◽  
pp. 31-48
Author(s):  
P. Christoph Richartz ◽  
Lukas Kornher ◽  
Awudu Abdulai
Keyword(s):  
Latent Class ◽  
Consumer Preferences ◽  
Mixed Logit ◽  
Meat Products ◽  
Food Product ◽  
Latent Class Models ◽  
Preference Heterogeneity ◽  
Significant Heterogeneity ◽  
Processing Strategies ◽  
Class Models

In this article, we apply a choice experiment meth-od to examine consumers’ preferences for online food product attributes, using survey data for German consumers for meat products. We use both mixed logit and latent class models to analyze preference heterogeneity and sources of heterogeneity, as well as endogenous attribute attendance models to account for consumers’ attribute processing strategies. The empirical results reveal significant heterogeneity in preferences for online meat attributes among consumers. We also find that consumers’ willingness to pay estimates are highly influenced by their attribute processing strategies.

Regression Analysis of Predictions and Forecasts of Cloud Data Centre KPIs Using the Boosted Decision Tree Algorithm

2021 ◽  
Author(s):  
Thomas Weripuo Gyeera
Keyword(s):  
Decision Tree ◽  
Heterogeneous Computing ◽  
Learning Algorithm ◽  
High Volume ◽  
Dynamic Allocation ◽  
Test Bed ◽  
Cloud Data ◽  
On Demand ◽  
Boosted Decision Tree ◽  
Data Centres

<div>The National Institute of Standards and Technology defines the fundamental characteristics of cloud computing as: on-demand computing, offered via the network, using pooled resources, with rapid elastic scaling and metered charging. The rapid dynamic allocation and release of resources on demand to meet heterogeneous computing needs is particularly challenging for data centres, which process a huge amount of data characterised by its high volume, velocity, variety and veracity (4Vs model). Data centres seek to regulate this by monitoring and adaptation, typically reacting to service failures after the fact. We present a real cloud test bed with the capabilities of proactively monitoring and gathering cloud resource information for making predictions and forecasts. This contrasts with the state-of-the-art reactive monitoring of cloud data centres. We argue that the behavioural patterns and Key Performance Indicators (KPIs) characterizing virtualized servers, networks, and database applications can best be studied and analysed with predictive models. Specifically, we applied the Boosted Decision Tree machine learning algorithm in making future predictions on the KPIs of a cloud server and virtual infrastructure network, yielding an R-Square of 0.9991 at a 0.2 learning rate. This predictive framework is beneficial for making short- and long-term predictions for cloud resources.</div>

Abstract P180: Does Poverty Consistently Affect Food Consumption in Childhood? Evidence from a Longitudinal Study in Children

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Lisa Kakinami ◽  
Marie Lambert ◽  
Lise Gauvin ◽  
Louise Séguin ◽  
Béatrice Nikiéma ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Food Consumption ◽  
Low Income ◽  
Fruits And Vegetables ◽  
Latent Class ◽  
Group Analysis ◽  
High Exposure ◽  
Low Exposure ◽  
Statistics Canada ◽  
The Relationship

Background: Childhood poverty is associated with poorer food consumption patterns but longitudinal data on this association is limited. To assess if the relationship between food consumption and poverty differs depending on the child’s age and pattern of poverty, we analyzed the relationship between consumption of selected foods and poverty trajectories at various ages in a birth cohort. Methods: The 1998-2010 "Quebec Longitudinal Study of Child Development" (n=2,120) cohort was used for these analyses. Household income was measured annually with poverty defined as income below the low-income thresholds established by Statistics Canada adjusted for household size and geographic region. Frequency of children’s consumption of dairy (milk, cheese, yogurt), fruits, and vegetables were reported by parents using a food frequency questionnaire. Analyses were conducted on the 739 children with food consumption data. Trajectories of poverty at 6, 8, 10, and 12 years were characterized with latent class group analysis using maximum likelihood in a semiparametric mixture model. Multivariable logistic regression predicted the likelihood of having less than 2 servings a day of dairy, fruits and vegetables based on poverty trajectories after adjusting for age and sex. Results: The poverty trajectories were stable and fell into 1 lower exposure category (consistently low exposure (73%, n=537)) and 3 higher exposure categories (increasing: 8%, n=61; decreasing: 10%, n=73; or consistently high exposure: 9%, n=68)). Compared to children experiencing low exposure to poverty, children with increasing or high exposure to poverty were less likely to have at least two servings of fruit a day at all ages, but the results were not significant. Compared to children experiencing low exposure to poverty, children with high exposure were 55% (CI: 0.2-0.8, p=0.001), 31% (CI: 0.4-1.2, p=0.23), 67% (CI: 0.2-0.6, p<.0001), and 49% (CI: 0.3-0.8, p=0.001) less likely to have at least two servings of dairy a day at 6, 8, 10, and 12 years, respectively. Compared to children with low exposure to poverty, children with high exposure were 43% (CI: 0.3-0.9, p=0.02), 46% (CI: 0.3-0.9, p=0.02), 55% (CI: 0.3-0.8, p=0.003), and 47% (CI: 0.3-0.9, p=0.02) less likely to have at least two servings of vegetables a day at 6, 8, 10, and 12 years, respectively. Children at all ages with decreasing or increasing exposure to poverty were less likely to have at least two servings of vegetables a day, but the results were not statistically significant. Conclusion: Experiencing high exposure to poverty has consistent effects on food consumption throughout childhood. In addition, compared to children with low exposure to poverty, children with increasing or decreasing exposure were less likely to have at least 2 servings of fruits and vegetables a day, suggesting any exposure to poverty may have detrimental effects on consumption of selected foods.

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