Analysis on the Factors Affecting Housing Tenure of Single-Person Households of Young Generation Employing the Multinomial Logit Model

This article identifies consumer characteristics associated with preferences toward fluid milk alternatives. Using consumer survey data from Samsun province of Turkey and Multinomial Logit model, unpacked and packed fluid milk preferences were analyzed. Based on the results, 14.1% of respondents consumed only unpacked fluid milk, 58.2% consumed only packed fluid milk and 27.7% of respondents consumed both unpacked and packed fluid milk at least once a weak. Multinomial Logit model results indicated that better educated household head, higher income households, younger and female household head and people who agree with “unpacked milk is not healthy” consume more packed fluid milk than do others. Moreover, consumers who agree with statement “price of packed milk is expensive compare to unpacked milk” were less likely to consume packed fluid milk than do others.

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Investigating hazardous factors affecting freeway crash injury severity with real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors

Journal of Safety Research ◽

10.1016/j.jsr.2020.12.014 ◽

2021 ◽

Author(s):

Xuan Zhang ◽

Huiying Wen ◽

Toshiyuki Yamamoto ◽

Qiang Zeng

Keyword(s):

Real Time ◽

Logit Model ◽

Injury Severity ◽

Multinomial Logit Model ◽

Multinomial Logit ◽

Weather Data ◽

Factors Affecting ◽

Conditional Autoregressive ◽

Crash Injury Severity

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Investigation of Contributing Factors to Traffic Crashes and Violations: A Random Parameter Multinomial Logit Approach

Journal of Advanced Transportation ◽

10.1155/2021/2836657 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Jaeyoung Lee ◽

Xing Li ◽

Suyi Mao ◽

Wen Fu

Keyword(s):

Logit Model ◽

Random Parameter ◽

Multinomial Logit Model ◽

Multinomial Logit ◽

Contributing Factors ◽

Traffic Crashes ◽

Factors Affecting ◽

Effective Strategies ◽

Traffic Violations ◽

Afternoon Peak

This study investigates contributing factors to traffic violations by seriousness. The traffic violations are divided into four categories by seriousness (unintentional violation, minor violation, serious violation, and crash with violation). The results of the random parameter multinomial logit model indicate that various factors potentially affect the severity of traffic violations. The key findings include the following: (1) female drivers are more likely to commit minor violations; (2) drivers from an area with a longer travel time to work and a higher proportion of driving to work are more likely to have minor violations and serious violations, while those from the high-income area are less likely; (3) drivers are more likely to be associated with a more minor infraction during the afternoon peak (4 p.m.–6 p.m.). The results from this study are expected to be beneficial for policymakers and traffic police to comprehend the factors affecting violations and implement effective strategies to minimize the number and seriousness of traffic violations.

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