A Branch-and-Cut Algorithm for the Latent Class Logit Assortment Problem

2010 ◽  
Vol 36 ◽  
pp. 383-390 ◽  
Author(s):  
Isabel Méndez-Díaz ◽  
Juan José Miranda-Bront ◽  
Gustavo Vulcano ◽  
Paula Zabala
Keyword(s):  
Latent Class ◽  
Branch And Cut ◽  
Latent Class Logit

scholarly journals A branch-and-cut algorithm for the latent-class logit assortment problem

2014 ◽  
Vol 164 ◽  
pp. 246-263 ◽  
Author(s):  
Isabel Méndez-Díaz ◽  
Juan José Miranda-Bront ◽  
Gustavo Vulcano ◽  
Paula Zabala
Keyword(s):  
Latent Class ◽  
Branch And Cut ◽  
Latent Class Logit

scholarly journals Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Juan Li ◽  
Boyu Jiang ◽  
Chunjiao Dong ◽  
Jue Wang ◽  
Xuan Zhang
Keyword(s):  
High Risk ◽  
Reliability Index ◽  
Latent Class ◽  
Signalized Intersections ◽  
Low Risk ◽  
New Classification ◽  
Intersection Safety ◽  
Dangerous Driving ◽  
Latent Class Logit ◽  
Risk Categories

Drivers’ decisions to either slow and stop or go at the onset of yellow signal impact on intersection safety. This novel study contributes to the new classification scheme for drivers. Two driving style indexes (i.e., the driving reliability index and dangerous driving index) are adopted, along with other known factors to analyze stop/go decision-making. Initially, the driving reliability index is extracted using a Hidden Markov Model (HMM). The dangerous driving index is calculated based on statistics extracted from dangerous driving records. A latent class logit model is then proposed to explore the factors which influence drivers’ decisions. Drivers are classified for analytical purposes into “low-risk” and “high-risk” categories according to driving styles and age. Results indicate that those considering “low-risk” tend to stop, while drivers considering “high-risk” are inclined to pass intersections. Furthermore, distractions from cell phones have different influences on each group of drivers. These findings help to determine driver preferences and may be used to formulate strategies to reduce unsafe driving occurring at signalized intersections.

scholarly journals Drivers and Barriers Influencing the Willingness to Adopt Technologies for Variable Rate Application of Fertiliser in Lower Austria

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1965
Author(s):  
Julia Blasch ◽  
Francesco Vuolo ◽  
Laura Essl ◽  
Bianca van der Kroon
Keyword(s):  
Choice Experiment ◽  
Latent Class ◽  
Mixed Logit ◽  
Farming System ◽  
Farm Size ◽  
Variable Rate ◽  
Variable Rate Application ◽  
Willingness To Adopt ◽  
Technological Level ◽  
Latent Class Logit

Even though a broad range of technologies for variable rate application of nitrogen fertiliser is available, there are hardly any documented cases of their use in Austria. In this study, the drivers and barriers of adoption have been investigated. A survey of 242 farmers in Lower Austria was conducted. The survey covered the farmers’ economic situation, concerns, and expectations regarding the future of their farms and their interest in precision farming technologies. A choice experiment was included in the survey to elicit farmers’ preferences for different features of variable rate application technologies. A series of multinomial logit, mixed logit and latent class logit models were run to analyze the choice experiment. Most farmers were interested in variable rate application, whereas technology costs, yield and environmental improvements were found to be important drivers of adoption. Also, farm size, farming system, technological level and network activities seem to play an important role in the uptake of variable rate application technologies.

scholarly journals Farmer preferences for adopting precision farming technologies: a case study from Italy

2020 ◽  
Author(s):  
J Blasch ◽  
B van der Kroon ◽  
P van Beukering ◽  
R Munster ◽  
S Fabiani ◽  
...  
Keyword(s):  
Environmental Impact ◽  
Choice Experiment ◽  
Latent Class ◽  
Precision Farming ◽  
Logit Models ◽  
Willingness To Adopt ◽  
Latent Class Logit ◽  
Fertiliser Use

Abstract Precision farming (PF) technologies can help to mitigate the environmental impact of agriculture by reducing fertiliser use and irrigation while saving cost for the farmer. However, these technologies are not widely adopted in Europe. We study farmers’ willingness to adopt PF technologies based on a choice experiment. Among other determinants, we explore the role of social influence for the valuation of PF technology features. The data are analysed using mixed and latent class logit models. Our results show that knowledge of fellow farmers who adopted the technology positively influences the valuation of PF technology features, stressing the importance of networks.

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