Factors Affecting the Adoption of Autonomous Vehicles for Commute Trips: An Analysis with the 2015 and 2017 Puget Sound Travel Surveys

2019 ◽  
Vol 2673 (2) ◽  
pp. 13-25 ◽  
Author(s):  
Kailai Wang ◽  
Gulsah Akar
Keyword(s):  
Autonomous Vehicles ◽  
Puget Sound ◽  
Probit Model ◽  
Market Penetration ◽  
Factors Affecting ◽  
Bivariate Ordered Probit ◽  
Travel Studies ◽  
Using Data ◽  
Household Travel ◽  
Future Landscape

The emergence of autonomous vehicles (AVs) may shape the future landscape of urban mobility. Although there is a growing literature on public opinions with regard to self-driving, little attention has been explicitly given to the commuters and their preferences for their commute trips. Using data from the 2015 and 2017 Puget Sound Regional Household Travel Studies, this study investigates the factors associated with employees’ propensity for using AVs. We develop a bivariate ordered probit model to jointly estimate the determinants of levels of interest in (i) commuting alone using AVs and (ii) commuting with others using shared AVs. Not surprisingly, it was found that current solo drivers are more likely to commute alone using AVs compared with other mode users. Significant differences were not found between current drivers and other commuters when it comes to the potential use of shared AVs. The results also reveal that, controlling for other factors, commuters surveyed in 2017 are less likely to be interested in shared AVs compared with their 2015 counterparts. The conclusion is that more planning efforts are needed to support the market penetration of shared AVs.

Day Pattern Generation System for Jointly Modeling Tours and Stops: Bi-Level Multiple Discrete Continuous Probit Model

2017 ◽  
Vol 2665 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Annesha Enam ◽  
Karthik C. Konduri
Keyword(s):  
Time Allocation ◽  
Level Structure ◽  
Probit Model ◽  
Primary Objective ◽  
Pattern Generation ◽  
Model Formulation ◽  
Upper Level ◽  
Using Data ◽  
Household Travel ◽  
Allocation Decisions

The primary objective of this study was to contribute to the literature on activity pattern generation. In this paper, a new framework is proposed for simultaneously modeling the following tour and stop-making decisions: the number and purpose of tours conducted in a day, time allocated to different tours, number and purpose of stops conducted within each tour, and time allocated to different stops. The framework represents time as a continuous entity and explicitly considers the time constraints within which an individual operates when generating tours and stops. In addition, the framework is capable of accounting for the interrelationships across different tour- and stop-level decisions. The model formulation that operationalizes the proposed framework imitates a bi-level structure in which the participation (whether to pursue) and time allocation (how much time) decisions for daily tours are modeled at the upper level. Within each tour, participation and time allocation decisions for different stops are modeled at the lower level. The model formulation for the bi-level structure builds on the utility theoretic multiple discrete continuous probit modeling approach. The proposed framework and model formulation are demonstrated with an empirical case study using data from the 2008–2009 National Household Travel Survey. Replication and forecasting results are presented to demonstrate the feasibility and applicability of the proposed framework and model formulation. The results provide evidence in support of the bi-level structure and its ability to capture the various constraints and interrelationships across tour- and stop-level participation and time allocation decisions.

scholarly journals Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

2019 ◽  
Vol 16 (14) ◽  
pp. 2632 ◽  
Author(s):  
Chen ◽  
Song ◽  
Ma
Keyword(s):  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Seat Belt ◽  
Probit Model ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Random Parameters ◽  
Factors Affecting ◽  
Bivariate Ordered Probit ◽  
Bivariate Ordered Probit Model

The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.

scholarly journals Factors affecting farmers’ use of organic and inorganic fertilizers in South Asia

2021 ◽  
Author(s):  
Jeetendra Prakash Aryal ◽  
Tek Bahadur Sapkota ◽  
Timothy J. Krupnik ◽  
Dil Bahadur Rahut ◽  
Mangi Lal Jat ◽  
...  
Keyword(s):  
Global Warming ◽  
Organic Fertilizer ◽  
Nutrient Use Efficiency ◽  
Probit Model ◽  
N Fertilizer ◽  
Nitrous Oxide Emissions ◽  
Inorganic Fertilizers ◽  
Factors Affecting ◽  
Using Data ◽  
Organic And Inorganic Fertilizers

AbstractFertilizer, though one of the most essential inputs for increasing agricultural production, is a leading cause of nitrous oxide emissions from agriculture, contributing significantly to global warming. Therefore, understanding factors affecting farmers’ use of fertilizers is crucial to develop strategies to improve its efficient use and to minimize its negative impacts. Using data from 2528 households across the Indo-Gangetic Plains in India, Nepal, and Bangladesh, this study examines the factors affecting farmers’ use of organic and inorganic fertilizers for the two most important cereal crops – rice and wheat. Together, these crops provide the bulk of calories consumed in the region. As nitrogen (N) fertilizer is the major source of global warming and other environmental effects, we also examine the factors contributing to its overuse. We applied multiple regression models to understand the factors influencing the use of inorganic fertilizer, Heckman models to understand the likelihood and intensity of organic fertilizer (manure) use, and a probit model to examine the over-use of N fertilizer. Our results indicate that various socio-economic and geographical factors influence the use of organic and inorganic fertilizers in rice and wheat. Across the study sites, N fertilizer over-use is the highest in Haryana (India) and the lowest in Nepal. Across all locations, farmers reported a decline in manure application, concomitant with a lack of awareness of the principles of appropriate fertilizer management that can limit environmental externalities. Educational programs highlighting measures to improving nutrient-use-efficiency and reducing the negative externalities of N fertilizer over-use are proposed to address these problems.

scholarly journals Factors Affecting Farmers’ Use of Inorganic Fertilizers and Manure in South Asia

2021 ◽  
Author(s):  
Jeetendra Prakash Aryal ◽  
Tek Bahadur Sapkota ◽  
Timothy J. Krupnik ◽  
Dil Bahadur Rahut ◽  
Mangi Lal Jat ◽  
...  
Keyword(s):  
Global Warming ◽  
Nutrient Use Efficiency ◽  
Probit Model ◽  
N Fertilizer ◽  
Inorganic Fertilizer ◽  
Nitrous Oxide Emissions ◽  
Inorganic Fertilizers ◽  
Factors Affecting ◽  
Using Data ◽  
Study Sites

Abstract Fertilizer, though one of the most essential inputs for increasing agricultural production, is a leading cause of nitrous oxide emissions from agriculture, contributing significantly to global warming. Therefore, understanding factors affecting farmers’ use of fertilizers is crucial to develop strategies to improve its efficient use and to minimize its negative impacts. Using data from 2,558 households across the Indo-Gangetic Plains in India, Nepal, and Bangladesh, this study examines the factors affecting farmers’ use of organic and inorganic fertilizers for the two most important cereal crops – rice and wheat. Together, these crops provide the bulk of calories consumed in the region. As nitrogen (N) fertilizer is the major source of global warming and other environmental effects, we also examine the factors contributing to its overuse. We applied multiple regression models to understand the factors influencing the use of inorganic fertilizer, Heckman models to understand the likelihood and intensity of manure use, and a probit model to examine the over-use of N fertilizer. Our results indicate that various socio-economic and geographical factors influence the use of inorganic fertilizer in rice and wheat. Across the study sites, N fertilizer over-use is the highest in Haryana (India) and the lowest in Nepal. Across all locations farmers reported a decline in manure application, concomitant with a lack of awareness of the principles of appropriate fertilizer management that can limit environmental externalities. Educational programs highlighting measures to improving nutrient-use-efficiency and reducing the negative externalities of N fertilizer over-use are proposed to address these problems.

scholarly journals Ghana’s Fertiliser Subsidy Programme: Assessing Farmer Participation and Perceptions of its Effectiveness

2019 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Benjamin Tetteh Anang ◽  
Solace Kudadze
Keyword(s):  
Household Head ◽  
Probit Model ◽  
Educational Status ◽  
Herd Size ◽  
Northern Ghana ◽  
Extension Agents ◽  
Factors Affecting ◽  
The Government ◽  
Using Data ◽  
The Impact

In 2008, the Government of Ghana introduced a national fertiliser subsidy programme to promote the production of cereals in the country. Documented evidence of the impact of the programme, factors affecting participation, and the perceptions of farmers about its effectiveness remains scanty and hard to find. This study therefore sought to investigate the factors affecting participation in the subsidy programme as well as farmers’ perceptions about its effectiveness using data from a cross-section of 300 farm households in northern Ghana. The study employed a probit model to assess the factors affecting participation in the subsidy programme while descriptive statistics were used to present the findings on farmers’ perceptions. The results indicated that participation in the subsidy programme is significantly influenced by educational status and farming experience of the household head, contact with agricultural extension agents, herd size, degree of specialisation in rice production, use of farm mechanisation and location of the farm. Furthermore, farmers perceived the subsidy programme to be ineffective in terms of timeliness, availability and distribution of subsidised fertiliser, access to coupons (vouchers), and distance to fertiliser depots. The findings underscore the need to ensure adequate and timely supply of subsidised fertiliser, improve communication on the availability of both fertiliser coupons and subsidised fertiliser, as well as increase in the number of extension workers to enhance the effectiveness of the subsidy programme.

scholarly journals Measuring the Efficiency of Football Clubs Using Data Envelopment Analysis: Empirical Evidence From Spanish Professional Football

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402198925
Author(s):  
Isidoro Guzmán-Raja ◽  
Manuela Guzmán-Raja
Keyword(s):  
Data Envelopment Analysis ◽  
High Efficiency ◽  
Sport Performance ◽  
Data Envelopment ◽  
Professional Football ◽  
Efficiency Measure ◽  
Factors Affecting ◽  
Football Clubs ◽  
Main Factors ◽  
Using Data

Professional football clubs have a special characteristic not shared by other types of companies: their sport performance (on the field) is important, in addition to their financial performance (off the field). The aim of this paper is to calculate an efficiency measure using a model that combines performance (sport and economic) based on data envelopment analysis (DEA). The main factors affecting teams’ efficiency levels are investigated using cluster analysis. For a sample of Spanish football clubs, the findings indicate that clubs achieved a relatively high efficiency level for the period studied, and that the oldest teams with the most assets had the highest efficiency scores. These results could help club managers to improve the performance of their teams.

scholarly journals Application of Mathematical and Computational Methods to Identify Women’s Priorities in Transport

2021 ◽  
Vol 13 (5) ◽  
pp. 2845
Author(s):  
Sara Poveda-Reyes ◽  
Ashwani Kumar Malviya ◽  
Elena García-Jiménez ◽  
Gemma Dolores Molero ◽  
Maria Chiara Leva ◽  
...  
Keyword(s):  
Transport System ◽  
Autonomous Vehicles ◽  
Transport Sector ◽  
Sustainable Society ◽  
Analytic Hierarchy ◽  
Family Responsibilities ◽  
Factors Affecting ◽  
Participation Of Women ◽  
Bike Sharing ◽  
Safe Transport

It is well established that the transport sector is not an equalitarian sector. To develop a sustainable society, a more equalitarian and safe transport system for both users and transport sector employees is needed. This work prioritizes the needs and barriers previously identified as relevant among transport system users and employees for four different transport scenarios (railways, autonomous vehicles (AVs), bicycle-sharing services (BSSs), and employment). The aim of this paper is to prioritize the factors affecting women in these four transport scenarios with the help of a survey followed by the application of mathematical and computational algorithms based on the analytic hierarchy process (AHP) methodology. The identification of factors with higher influence in the fair participation of women in the transport sector will help transport planners, bike-sharing system owners, decision-makers, transport companies, and regulatory professionals to develop measures that could plausibly increase the proportion of women as users of BSSs, users of rail public transport, and AVs, as well as employees in the transport sector for a sustainable society. The results indicated that safety and security were the most challenging factors for railways. Weather, topography, and family responsibilities were shown to have a high influence on the use of BSSs. In the case of autonomous vehicles, the simultaneity and trust in the technology are the main opportunities to influence the acceptance of such vehicles. Finally, for transport employment, caring and parenting responsibilities were the factors that had the largest effect. Some differences in priorities were found for different profiles of women.

scholarly journals Association between Dyslipidemia and Mercury Exposure in Adults

2021 ◽  
Vol 18 (2) ◽  
pp. 775
Author(s):  
Purum Kang ◽  
Hye Young Shin ◽  
Ka Young Kim
Keyword(s):  
Behavioral Interventions ◽  
Dietary Habits ◽  
Subjective Health ◽  
Targeted Prevention ◽  
Adult Males ◽  
Subjective Health Status ◽  
Factors Affecting ◽  
Health And Nutrition ◽  
Metal Levels ◽  
Using Data

Background—Dyslipidemia is one of the prominent risk factors for cardiovascular disease, which is the leading cause of death worldwide. Dyslipidemia has various causes, including metabolic capacity, genetic problems, physical inactivity, and dietary habits. This study aimed to determine the association between dyslipidemia and exposure to heavy metals in adults. Methods—Using data from the seventh Korean National Health and Nutrition Examination Survey (2016–2017), 5345 participants aged ≥20 years who were tested for heavy metal levels were analyzed in this study. Multiple logistic regression was conducted to assess the factors affecting the prevalence of dyslipidemia. Results—The risks of dyslipidemia among all and male participants with mercury (Hg) levels of ≥2.75 μg/L (corresponding to the Korean average level) were 1.273 and 1.699 times higher than in those with levels of <2.75 μg/L, respectively. The factors that significantly affected the dyslipidemia risk were age, household income, body mass index, and subjective health status in both males and females. Conclusions—In adult males, exposure to Hg at higher-than-average levels was positively associated with dyslipidemia. These results provide a basis for targeted prevention strategies for dyslipidemia using lifestyle guidelines for reducing Hg exposure and healthy behavioral interventions.

Sign in / Sign up

Export Citation Format

Share Document