Investigating contributing factors to injury severity levels in crashes involving pedestrians and cyclists using latent class clustering analysis and mixed logit models

2021 ◽  
pp. 1-28
Author(s):  
Shaojie Liu ◽  
Zijing Lin ◽  
Wei (David) Fan
Keyword(s):  
Clustering Analysis ◽  
Latent Class ◽  
Injury Severity ◽  
Mixed Logit ◽  
Logit Models ◽  
Contributing Factors ◽  
Mixed Logit Models ◽  
Severity Levels

Implications of Competitor Representation on Optimal Design

2019 ◽  
Author(s):  
Arthur H. C. Yip ◽  
Jeremy J. Michalek ◽  
Kate S. Whitefoot
Keyword(s):  
Optimal Design ◽  
Latent Class ◽  
Mixed Logit ◽  
Profit Maximization ◽  
Large Set ◽  
Logit Models ◽  
Product Representation ◽  
Mixed Logit Models ◽  
The Cost

Abstract We investigate the effect of competitor product representation on optimal design results in profit-maximization studies. Specifically, we study the implications of replacing a large set of product alternatives available in the marketplace with a reduced set of selected competitors or with composite alternatives, as is common in the literature. We derive first-order optimality conditions and show that optimal design (but not price) is independent of competitors under the logit and nested logit models (where preference coefficients are homogeneous), but optimal design results may depend on competitor representation in latent class and mixed logit models (where preference coefficients are heterogeneous). In a case study of automotive powertrain design using mixed logit demand, we find some change in the optimal acceleration performance value when competitors are modeled using a small set of alternatives rather than the larger set. The magnitude of this change depends on the specific form and parameters of the cost and demand functions assumed, ranging from 0% to 3% in our case study. We find that the magnitude of the change in optimal design variables induced by competitor representation in our case study increases with the heterogeneity of preference coefficients across consumers and changes with the curvature of the cost function.

Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

2021 ◽  
pp. 036119812110098
Author(s):  
Miao Yu ◽  
Jinxing Shen ◽  
Changxi Ma
Keyword(s):  
High Speed ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Research Effort ◽  
Random Parameter ◽  
Policy Implications ◽  
Contributing Factors ◽  
Severe Injuries ◽  
Data Set ◽  
Severity Levels

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

Including cost income ratio into utility function as a way of dealing with ‘exploding’ implicit prices in mixed logit models

2012 ◽  
Vol 18 (4) ◽  
pp. 370-380 ◽  
Author(s):  
Marek Giergiczny ◽  
Sviataslau Valasiuk ◽  
Mikolaj Czajkowski ◽  
Maria De Salvo ◽  
Giovanni Signorello
Keyword(s):  
Utility Function ◽  
Mixed Logit ◽  
Logit Models ◽  
Mixed Logit Models ◽  
Income Ratio ◽  
Implicit Prices

scholarly journals A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Kai Lu ◽  
Alireza Khani ◽  
Baoming Han
Keyword(s):  
Open Systems ◽  
Choice Model ◽  
Mixed Logit ◽  
Random Coefficients ◽  
Logit Models ◽  
Transit Systems ◽  
Trip Purpose ◽  
Mixed Logit Models ◽  
Key Factor ◽  
Rich Data

Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information. To estimate a more reasonable utility function for choice modeling, the study uses the trip chaining method to infer the actual destination of the trip. Based on the land use and passenger flow pattern, applying k-means clustering method, stations are classified into 7 categories. A trip purpose labelling process was proposed considering the station category, trip time, trip sequence, and alighting station frequency during five weekdays. We apply multinomial logit models as well as mixed logit models with independent and correlated normally distributed random coefficients to infer passengers’ preferences for ticket fare, walking time, and in-vehicle time towards their alighting station choice based on different trip purposes. The results find that time is a combined key factor while the ticket price based on distance is not significant. The estimated alighting stations are validated with real choices from a separate sample to illustrate the accuracy of the station choice models.

scholarly journals Fitting Mixed Logit Models by Using Maximum Simulated Likelihood

2007 ◽  
Vol 7 (3) ◽  
pp. 388-401 ◽  
Author(s):  
Arne Risa Hole
Keyword(s):  
Mixed Logit ◽  
Logit Models ◽  
Maximum Simulated Likelihood ◽  
Mixed Logit Models ◽  
Simulated Likelihood

This article describes the mixlogit Stata command for fitting mixed logit models by using maximum simulated likelihood.

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