Journal articles: 'Random parameters logit'

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Zeng, Tong, and R. Carter Hill. "Shrinkage Estimation in the Random Parameters Logit Model." Open Journal of Statistics 06, no. 04 (2016): 667–74. http://dx.doi.org/10.4236/ojs.2016.64056.

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Bansal, Prateek, Ricardo A. Daziano, and Martin Achtnicht. "Extending the logit-mixed logit model for a combination of random and fixed parameters." Journal of Choice Modelling 27 (June 2018): 88–96. http://dx.doi.org/10.1016/j.jocm.2017.10.001.

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Wei, Fulu, Zhenggan Cai, Zhenyu Wang, Yongqing Guo, Xin Li, and Xiaoyan Wu. "Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model." Applied Sciences 11, no. 17 (August 25, 2021): 7819. http://dx.doi.org/10.3390/app11177819.

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The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas.

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Gong, Hongren, Ting Fu, Yiren Sun, Zhongyin Guo, Lin Cong, Wei Hu, and Ziwen Ling. "Two-vehicle driver-injury severity: A multivariate random parameters logit approach." Analytic Methods in Accident Research 33 (March 2022): 100190. http://dx.doi.org/10.1016/j.amar.2021.100190.

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Rusmevichientong, Paat, David Shmoys, Chaoxu Tong, and Huseyin Topaloglu. "Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters." Production and Operations Management 23, no. 11 (March 13, 2014): 2023–39. http://dx.doi.org/10.1111/poms.12191.

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Jafari, Habib. "On Locally Optimal Criterion for a Logit Model with Random Parameters." Applied Mathematics & Information Sciences 7, no. 2L (June 1, 2013): 723–28. http://dx.doi.org/10.12785/amis/072l50.

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MCCONNELL, KENNETH E., and WEI-CHUN TSENG. "Some Preliminary Evidence on Sampling of Alternatives with the Random Parameters Logit." Marine Resource Economics 14, no. 4 (December 1999): 317–32. http://dx.doi.org/10.1086/mre.14.4.42629276.

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Coppola, Pierluigi, Luigi dell’Olio, and Fulvio Silvestri. "Random-Parameters Behavioral Models to Investigate Determinants of Perceived Safety in Railway Stations." Journal of Advanced Transportation 2021 (August 24, 2021): 1–11. http://dx.doi.org/10.1155/2021/5530591.

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Recent studies have highlighted the existence of a gap between actual and perceived safety and have shown that feelings of insecurity can affect individuals’ travel behavior before and during the journey. In this paper, a methodology is proposed for assessing determinants of travelers’ perception of safety and security in railway stations. The methodological approach includes focus groups, stated preference (SP) surveys, and the estimation of behavioral models with fixed parameters (Binomial Logit) and random parameters (Mixed Logit). The estimation results for a medium-sized railway station (Frosinone, Italy) confirmed that safety and security measures are not equally perceived by individuals and the use of random-parameters models leads to more robust estimates. The proposed modeling approach allows the identification of the interventions that should be prioritized to increase travelers’ perceived levels of safety, highlighting those factors, such as, for the considered case study, the presence of security personnel and the level of decorum and maintenance, which are perceived by users as more important than others (e.g., surveillance cameras).

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Camarena, Dena M., and Ana I. Sanjuán. "Heterogeneidad de preferencias y experimentos de elección: Aplicación de un logit con parámetros aleatorios a la demanda de nueces." Economía Agraria y Recursos Naturales 4, no. 8 (October 21, 2011): 105. http://dx.doi.org/10.7201/earn.2004.08.06.

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Consumers’ stated preferences towards walnuts are studied by means of a choice experiment, with a double objective: first, to identify the main attributes searched by consumer at purchase and second, to analyse the chances for the introduction into the Spanish market of the Pecan variety. From this study, commercial guidelines may be derived, that helps distribution and import companies to commercialise this nut. A mixed or random parameters logit is estimated which relaxes the IIA property (independence of irrelevant alternatives) present in the logit model with fixed parameters. In a mixed logit, coefficients of each attribute/level vary randomly across consumers, reflecting the heterogeneity of individuals’ preferences. This model also allows estimate efficiently the parameters when each individual chooses several times, as in the present study.

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Adanu, Emmanuel Kofi, and Steven Jones. "Effects of Human-Centered Factors on Crash Injury Severities." Journal of Advanced Transportation 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/1208170.

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Factors related to drivers and their driving habits dominate the causation of traffic crashes. An in-depth understanding of the human factors that influence risky driving could be of particular importance to facilitate the application of effective countermeasures. This paper sought to investigate effects of human-centered crash contributing factors on crash outcomes. To select the methodology that best accounts for unobserved heterogeneity between crash outcomes, latent class (LC) logit model and random parameters logit (RPL) model were developed. Model estimation results generally show that serious injury crashes were more likely to involve unemployed drivers, no seatbelt use, old drivers, fatigued driving, and drivers with no valid license. Comparison of model fit statistics shows that the LC logit model outperformed the RPL model, as an alternative to the traditional multinomial logit (MNL) model.

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Guo, Yanyong, Yao Wu, Jian Lu, and Jibiao Zhou. "Modeling the Unobserved Heterogeneity in E-bike Collision Severity Using Full Bayesian Random Parameters Multinomial Logit Regression." Sustainability 11, no. 7 (April 8, 2019): 2071. http://dx.doi.org/10.3390/su11072071.

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Understanding the risk factors of e-bike collisions can improve e-bike riders’ safety awareness and help traffic professionals to develop effective countermeasures. This study investigates risk factors that significantly contribute to the severity of e-bike collisions. Two months of e-bike collision data were collected in the city of Ningbo, China. A random parameters multinomial logit regression (RP-MNL) is proposed to account for the unobserved heterogeneity across observations. A fixed parameters multinomial logit regression (FP-MNL) is estimated and compared with the RP-MNL under the Bayesian framework. The full Bayesian approach based on Markov chain Monte Carlo simulation is employed to estimate the model parameters. Both parameter estimates and odds ratio (OR) are used to interpret the impact of risk factors on the severity of e-bike collisions. The model comparison results show that RP-MNL outperforms FP-MNL, indicating that accommodating the unobserved heterogeneity across observations could improve the model fit. The model estimation results show that age, gender, e-bike behavior, license plate, bicycle type, location, and speed limit are statistically significant and associated with the severity of e-bike collisions. Furthermore, four risk factors, i.e., gender, e-bike behavior, bicycle type, and speed limit, are found to have heterogeneous effects on severity of e-bike collisions, appearing in the form of random parameters in the statistical model.

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Park, Sungho, and Sachin Gupta. "Simulated Maximum Likelihood Estimator for the Random Coefficient Logit Model Using Aggregate Data." Journal of Marketing Research 46, no. 4 (August 2009): 531–42. http://dx.doi.org/10.1509/jmkr.46.4.531.

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The authors propose a simulated maximum likelihood estimation method for the random coefficient logit model using aggregate data, accounting for heterogeneity and endogeneity. The method allows for two sources of randomness in observed market shares: unobserved product characteristics and sampling error. Because of the latter, the method is suitable when sample sizes underlying the shares are finite. In contrast, Berry, Levinsohn and Pakes's commonly used approach assumes that observed shares have no sampling error. The method can be viewed as a generalization of Villas-Boas and Winer's approach and is closely related to Petrin and Train's “control function” approach. The authors show that the proposed method provides unbiased and efficient estimates of demand parameters. They also obtain endogeneity test statistics as a by-product, including the direction of endogeneity bias. The model can be extended to incorporate Markov regime-switching dynamics in parameters and is open to other extensions based on maximum likelihood. The benefits of the proposed approach are achieved by assuming normality of the unobserved demand attributes, an assumption that imposes constraints on the types of pricing behaviors that are accommodated. However, the authors find in simulations that demand estimates are fairly robust to violations of these assumptions.

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Zhu, Tong, Zishuo Zhu, Jie Zhang, and Chenxuan Yang. "Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances." International Journal of Environmental Research and Public Health 18, no. 21 (October 22, 2021): 11131. http://dx.doi.org/10.3390/ijerph182111131.

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Accidents involving electric bicycles, a popular means of transportation in China during peak traffic periods, have increased. However, studies have seldom attempted to detect the unique crash consequences during this period. This study aims to explore the factors influencing injury severity in electric bicyclists during peak traffic periods and provide recommendations to help devise specific management strategies. The random-parameters logit or mixed logit model is used to identify the relationship between different factors and injury severity. The injury severity is divided into four categories. The analysis uses automobile and electric bicycle crash data of Xi’an, China, between 2014 and 2019. During the peak traffic periods, the impact of low visibility significantly varies with factors such as areas with traffic control or without streetlights. Furthermore, compared with traveling in a straight line, three different turnings before the crash reduce the likelihood of severe injuries. Roadside protection trees are the most crucial measure guaranteeing riders’ safety during peak traffic periods. This study reveals the direction, magnitude, and randomness of factors that contribute to electric bicycle crashes. The results can help safety authorities devise targeted transportation safety management and planning strategies for peak traffic periods.

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Ghosh, Santanu, Bhargab Maitra, and Sudhanshu Sekhar Das. "Effect of Distributional Assumption of Random Parameters of Mixed Logit Model on Willingness-to-Pay Values." Procedia - Social and Behavioral Sciences 104 (December 2013): 601–10. http://dx.doi.org/10.1016/j.sbspro.2013.11.154.

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Mohajeri, Fatemeh, and Babak Mirbaha. "Studying the Role of Personality Traits on the Evacuation Choice Behavior Pattern in Urban Road Network in Different Severity Scales of Natural Disaster." Advances in Civil Engineering 2021 (November 23, 2021): 1–16. http://dx.doi.org/10.1155/2021/9174484.

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The study of evacuation behavior in response to disaster is necessary for emergency traffic management. As decision-making is not exclusively dependent on observable variables, in this research, it is attempted to study the evacuation choice behavior pattern in emergency response to earthquake disaster by considering both physical and behavioral factors. Personality traits are measured as behavioral latent factors by confirmatory factor analysis (CFA) of the short form of NEO-Five-Factor Inventory (NEO-FFI). A revealed preference survey with more than 700 samples was conducted in Qazvin city (Iran) which was based on real-life earthquake experience and the stated preference survey was conducted for six designated scenarios with different severities and times of earthquakes. Analysis of evacuation behavior is conducted by 3 types of discrete choice models (traditional binary logit model (TBLM), hybrid binary logit model (HBLM), and random parameters/mixed binary logit model (MBLM)). First, TBLM is estimated to study the effect of observable variables on response of people to earthquake disaster. Then, by adding the personality traits to modeling structure and constructing HBLM, the correct prediction percentage of the model increased. This study also considers heterogeneous mixtures of population in terms of income, family size, and five factors of personality traits by MBLM. The MBLM captures the heterogeneous responses of the respondent. By considering these variables as random parameters, the Log Likelihood function and pseudo square (ρ2) of the model increased.

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Dzik-Walczak, Aneta, and Maciej Odziemczyk. "Modelling cross-sectional tabular data using convolutional neural networks: Prediction of corporate bankruptcy in Poland." Central European Economic Journal 8, no. 55 (January 1, 2021): 352–77. http://dx.doi.org/10.2478/ceej-2021-0024.

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Abstract The paper deals with the topic of modelling the probability of bankruptcy of Polish enterprises using convolutional neural networks. Convolutional networks take images as input, so it was thus necessary to apply the method of converting the observation vector to a matrix. Benchmarks for convolutional networks were logit models, random forests, XGBoost, and dense neural networks. Hyperparameters and model architecture were selected based on a random search and analysis of learning curves and experiments in folded, stratified cross-validation. In addition, the sensitivity of the results to data preprocessing was investigated. It was found that convolutional neural networks can be used to analyze cross-sectional tabular data, especially for the problem of modelling the probability of corporate bankruptcy. In order to achieve good results with models based on parameters updated by a gradient (neural networks and logit), it is necessary to use appropriate preprocessing techniques. Models based on decision trees have been shown to be insensitive to the data transformations used.

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Grand, Alexandra, and Regina Dittrich. "Modelling Assumed Metric Paired Comparison Data - Application to Learning Related Emotions." Austrian Journal of Statistics 44, no. 1 (December 11, 2014): 3–15. http://dx.doi.org/10.17713/ajs.v44i1.25.

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In this article we suggest a beta regression model that accounts for the degree of preference in paired comparisons measured on a bounded metric paired comparison scale. The beta distribution for bounded continuous random variables assumes values in the open unit interval (0,1). However, in practice we will observe paired comparison responses that lie within a fixed or arbitrary fixed interval [-a,a] with known value of a. We therefore transform the observed responses into the interval (0,1) and assume that these transformed responses are each a realization of a random variable which follows a beta distribution. We propose a simple paired comparison regression model for beta distributed variables which allows us to model the mean of the transformed response using a linear predictor and a logit link function -- where the linear predictor is defined by the parameters of the logit-linear Bradley-Terry model. For illustration we applied the presented model to a data set obtained from a student survey of learning related emotions in mathematics.

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Li, Xiaowei, Siyu Zhang, Yao Wu, Yuting Wang, and Wenbo Wang. "Exploring Influencing Factors of Intercity Mode Choice from View of Entire Travel Chain." Journal of Advanced Transportation 2021 (September 9, 2021): 1–13. http://dx.doi.org/10.1155/2021/9454873.

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Exploring the influencing factors of intercity travel mode choice can reveal passengers’ travel decision mechanisms and help traffic departments to develop an effective demand management policy. To investigate these factors, a survey was conducted in Xi’an, China, to collect data about passengers’ travel chains, including airplane, high-speed railway (HSR), train, and express bus. A Bayesian mixed multinomial logit model is developed to identify significant factors and explicate unobserved heterogeneity across observations. The effect of significant factors on intercity travel mode choice is quantitatively assessed by the odds ratio (OR) technique. The results show that the Bayesian mixed multinomial logit model outperforms the traditional Bayesian multinomial logit model, indicating that accommodating the unobserved heterogeneity across observations can improve the model fit. The model estimation results show that ticket purchasing method, comfort, punctuality, and access time are random parameters that have heterogeneous effects on intercity travel mode choice.

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Catalani, Mauro. "A logistic distribution model of new short sea shipping line along a mutimodal corridor in Italy." European Transport/Trasporti Europei 81, ET.2021 (March 2021): 1–10. http://dx.doi.org/10.48295/et.2021.81.5.

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The purpose of the application focuses on an intermodal model (RPL) to simulate the transport choice for freight sending on the most relevant corridor Naples–Milan. In this, operate a rail- road system with the introduction a new short sea shipping (SSS) intermodal line (Naples Sea Genova road Milan). The paper considers a collaboration with a multimodal transport operator, with many logistic platforms in Italy to analyze the degree of competition inside corridor. An application along this very congested route Milan (Segrate interport) - Nola (Naples interport) was used. The econometric models applied to operator choices are a random parameter logit model vs multinomial logit model with frequency, type of load and cost as main parameters.

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Dietz, L. R., and S. Chatterjee. "Logit-normal mixed model for Indian monsoon precipitation." Nonlinear Processes in Geophysics 21, no. 5 (September 12, 2014): 939–53. http://dx.doi.org/10.5194/npg-21-939-2014.

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Abstract. Describing the nature and variability of Indian monsoon precipitation is a topic of much debate in the current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Four GLMM algorithms are described and simulations are performed to vet these algorithms before applying them to the Indian precipitation data. The logit-normal model was applied to light, moderate, and extreme rainfall. Findings indicated that physical constructs were preserved by the models, and random effects were significant in many cases. We also found GLMM estimation methods were sensitive to tuning parameters and assumptions and therefore, recommend use of multiple methods in applications. This work provides a novel use of GLMM and promotes its addition to the gamut of tools for analysis in studying climate phenomena.

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Malík, V., and J. Stuchlý. "Risk factors influencing the probability of browsing by hoofed game on forest trees." Journal of Forest Science 53, No. 8 (January 7, 2008): 359–63. http://dx.doi.org/10.17221/2179-jfs.

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In this paper we analyze how selected risk factors determine the probability of browsing by hoofed game on forest trees. Risk factors covered by the model are: tree species (Norway spruce or Scotch pine), time period (season: spring + summer or autumn + winter) and chemical structure of bark (content of selected nutrients and chemical elements). We use a logit model for these purposes. We formulate the model and perform linear transformation by the natural log. Since the disturbance term in the logit model is heteroscedastic, we cannot use the ordinary least-squares method to estimate the parameters of the model. In this case the maximum likelihood method included in STATGRAPHICS Plus for Windows program is used for its estimation. We use a random sample of data including 59 trees. We do the interpretations of the estimated parameters and other characteristics. We demonstrate how the estimated probabilities depend on the considered factor. The model explains 44.1% of variations of the logits, the model is statistically significant. All regression coefficients are significant at least at 12% confidence level. Among the main explanatory variables (content of P, Ca, NO<sub>3</sub>, tree species and season), the P and Ca contents in the bark of the tree are the most important factors influencing the probability of future damage to the tree.

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Bir, Courtney, Nicole Olynk Widmar, and Candace Croney. "Exploring Social Desirability Bias in Perceptions of Dog Adoption: All’s Well that Ends Well? Or Does the Method of Adoption Matter?" Animals 8, no. 9 (September 13, 2018): 154. http://dx.doi.org/10.3390/ani8090154.

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Dogs are a popular companion animal in the United States; however, dog acquisition is often a contentious subject. Adoption is often cited as an ethical and popular method of acquisition but interpretation of the term ‘adoption’ may vary. In a nationally representative survey of the U.S., 767 respondents were asked questions regarding their opinions of dog acquisition and adoption. Within the sample, 45% had a dog; of those, 40% had adopted a dog, and 47% visited a veterinarian once a year. A best-worst choice experiment, where respondents were asked to choose the most ethical and least ethical method of acquiring a dog from a statistically determined set of choices, was used to elicit respondents’ preferences for the most ethical method of dog adoption. A random parameters logit and a latent class model were used to estimate relative rankings of dog adoption methods. In the random parameters logit model, the largest preference share was for adoption from a municipal animal shelter (56%) and the smallest preference share was for adoption from a pet store (3%). Dog acquisition was further evaluated by creating an index of social desirability bias using how important respondents believed certain dog characteristics were compared to how important respondents believed others would rate/rank the same dog characteristics. The highest incidences of social desirability bias occurred for the dog characteristics of appearance and breed.

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Li, Qianwen, Xiaopeng Li, and Fred Mannering. "Assessment of Discretionary Lane-Changing Decisions using a Random Parameters Approach with Heterogeneity in Means and Variances." Transportation Research Record: Journal of the Transportation Research Board 2675, no. 6 (February 12, 2021): 330–38. http://dx.doi.org/10.1177/0361198121992364.

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Lane-changing maneuvers on highways may cause capacity drops, create shock waves, and potentially increase collision risks. Properly managing lane-changing behavior to reduce these adverse impacts requires an understanding of their determinants. This paper investigates the determinants of lane changing in congested traffic using a next generation simulation dataset. A random parameters binary logit model with heterogeneity in means and variances was estimated to account for unobserved heterogeneity in lane-changing behavior across vehicles. Estimation results show that average headway, the original lane of the vehicle, driver acceleration/deceleration behavior, and vehicle size all significantly influence lane-changing probabilities. It was further found that the effect of vehicle size varied significantly across observations, that the mean of this variation decreased with increasing average headway, and the variance increased with increasing driver acceleration/deceleration. These empirical findings provide interesting new evidence on the determinants of lane changing, which can be used in traffic flow models to better replicate and predict traffic flow.

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Basu, Debasis, and Bhargab Maitra. "VALUING ATTRIBUTES OF ENHANCED TRAFFIC INFORMATION: AN EXPERIENCE IN KOLKATA." TRANSPORT 22, no. 3 (September 30, 2007): 164–73. http://dx.doi.org/10.3846/16484142.2007.9638120.

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Most of the traffic information considers a single item like travel time or delay. In the present work, enhanced traffic information displaying instantaneous travel time and its variation from the previous interval to the present, is considered. An initial investigation is made on the effectiveness of such traffic information on route choice behavior of trip makers by valuation of attributes of the traffic information. Taking a case study of two urban corridors in the Kolkata metro city, India, the valuation is done separately for private car and taxi trip makers. The stated preference (choice based) data collected from trip makers are analyzed using both multinomial logit (MNL) and mixed logit (ML) modeling techniques. Assuming sparsely used constrained triangular distribution of random parameters, two different types of ML model are developed: one with independent choice sets and the other one by accounting heterogeneity around the mean of random parameter(s). Both family income and trip purpose are found to decompose heterogeneity around the mean estimate(s). The values of travel time and their variation presented in the paper encourage further investigation on such type of traffic information for management of congestion on alternative urban corridors both spatially and temporally.

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Waseem, Muhammad, Anwaar Ahmed, and Tariq Usman Saeed. "Factors affecting motorcyclists’ injury severities: An empirical assessment using random parameters logit model with heterogeneity in means and variances." Accident Analysis & Prevention 123 (February 2019): 12–19. http://dx.doi.org/10.1016/j.aap.2018.10.022.

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Fountas, Grigorios, Md Tawfiq Sarwar, Panagiotis Ch Anastasopoulos, Alan Blatt, and Kevin Majka. "Analysis of stationary and dynamic factors affecting highway accident occurrence: A dynamic correlated grouped random parameters binary logit approach." Accident Analysis & Prevention 113 (April 2018): 330–40. http://dx.doi.org/10.1016/j.aap.2017.05.018.

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Hossain, Mohammad Shafayat, John Douglas Hunt, and S. C. Wirasinghe. "Nature of influence of out-of-vehicle time-related attributes on transit attractiveness: a random parameters logit model analysis." Journal of Advanced Transportation 49, no. 5 (December 18, 2014): 648–62. http://dx.doi.org/10.1002/atr.1297.

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LOUZADA, Francisco, Ibrahim ELBATAL, and Daniele Cristina Tita GRANZOTTO. "THE BETA EXPONENTIATED WEIBULL GEOMETRIC DISTRIBUTION: MODELING, STRUCTURAL PROPERTIES, ESTIMATION AND AN APPLICATION TO A CERVICAL INTRAEPITHELIAL NEOPLASIA DATASET." REVISTA BRASILEIRA DE BIOMETRIA 36, no. 4 (December 27, 2018): 942. http://dx.doi.org/10.28951/rbb.v36i4.329.

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A new distribution, the so called beta exponentiated Weibull geometric (BEWG) distribution is proposed. The new distribution is generated from the logit of a beta random variable and includes the exponentiated Weibull geometric distribution as particular case. Various structural properties including explicit expressions for the moments, moment generating function, mean deviation of the new distribution are derived. The estimation of the model parameters is performed by maximum likelihood method. The usefulness of the model was showed by using a real dataset. In order to validate the results a simulation bootstrap is presented in this paper.

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Chintagunta, Pradeep K., Dipak C. Jain, and Naufel J. Vilcassim. "Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data." Journal of Marketing Research 28, no. 4 (November 1991): 417–28. http://dx.doi.org/10.1177/002224379102800404.

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In analyzing panel data, the issue of heterogeneity across households is an important consideration. If heterogeneity is present but is ignored in the analysis, it will result in biased and inconsistent estimates of the effects of marketing mix variables on brand choice. The authors propose the use of a random effects specification to account for heterogeneity in brand preferences across households in a logit framework. The model parameters are estimated by both parametric and semiparametric approaches. The authors also compare their results with those obtained from logit models in which observed past choice behavioir is used to capture such heterogeneity. The different models are estimated with the IRI saltine crackers dataset. A formal statistical test of the model specifications shows that the semiparametric specification is the most preferred in terms of the overall fit of the model to the data. In addition, that specification predicts best when the models are validated in a holdout sample of households.

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Chang, Fangrong, Pengpeng Xu, Hanchu Zhou, Alan H. S. Chan, and Helai Huang. "Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model." Accident Analysis & Prevention 131 (October 2019): 316–26. http://dx.doi.org/10.1016/j.aap.2019.07.012.

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Anderson, Jason C., Salvador Hernandez, Eric L. Jessup, and Eric North. "Perceived safe and adequate truck parking: A random parameters binary logit analysis of truck driver opinions in the Pacific Northwest." International Journal of Transportation Science and Technology 7, no. 1 (March 2018): 89–102. http://dx.doi.org/10.1016/j.ijtst.2018.01.001.

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Hamed, Mohammad M., and Basel M. Al-Eideh. "An exploratory analysis of traffic accidents and vehicle ownership decisions using a random parameters logit model with heterogeneity in means." Analytic Methods in Accident Research 25 (March 2020): 100116. http://dx.doi.org/10.1016/j.amar.2020.100116.

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Anam, Salwa, Ghazaleh Azimi, Alireza Rahimi, and Xia Jin. "Severity Analysis of Large-Truck Wrong-Way Driving Crashes in the State of Florida." Vehicles 4, no. 3 (July 30, 2022): 766–79. http://dx.doi.org/10.3390/vehicles4030043.

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Wrong-way driving (WWD) crashes lead to severe injuries and fatalities, especially when a large truck is involved. This study investigates the factors associated with crash-injury severity in large-truck WWD crashes in Florida. Various driver, roadway, weather, and traffic characteristics were explored as explanatory variables through a random parameter ordered logit model. The study also accounted for heterogeneity by identifying random parameters in the model and introducing interaction effects as potential sources of such heterogeneity. The findings indicate that not using a seatbelt, driving under the influence of drugs, and a driving speed of 50–74 mph were more likely to result in fatal crashes. On the contrary, female drivers, private roadways, and sideswipe collisions showed negative impacts on crash-injury severity. The model identified two random parameters, including a speed of 25–49 mph and early-morning crashes. The interaction effects showed that when driving at a speed of 25–49 mph, young drivers (under 20 years old) and middle-aged drivers (36–50 years old) were the sources of heterogeneity, decreasing crash-injury severity. Understanding the contributing factors of large-truck WWD crashes can help policymakers develop safety countermeasures to reduce the associated injury severity and improve truck safety.

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Mariel, Petr, Simona Demel, and Alberto Longo. "Modelling welfare estimates in discrete choice experiments for seaweed-based renewable energy." PLOS ONE 16, no. 11 (November 29, 2021): e0260352. http://dx.doi.org/10.1371/journal.pone.0260352.

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We explore what researchers can gain or lose by using three widely used models for the analysis of discrete choice experiment data—the random parameter logit (RPL) with correlated parameters, the RPL with uncorrelated parameters and the hybrid choice model. Specifically, we analyze three data sets focused on measuring preferences to support a renewable energy programme to grow seaweed for biogas production. In spite of the fact that all three models can converge to very similar median WTP values, they cannot be used indistinguishably. Each model is based on different assumptions, which should be tested before their use. The fact that standard sample sizes usually applied in environmental valuation are generally unable to capture the outcome differences between the models cannot be used as a justification for their indistinct application.

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Huang, Dongling, and Christian Rojas. "Eliminating the Outside Good Bias in Logit Models of Demand with Aggregate Data." Review of Marketing Science 12, no. 1 (January 1, 2014): 1–36. http://dx.doi.org/10.1515/roms-2013-0016.

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AbstractThe logit model is the most popular tool in estimating demand for differentiated products. In this model, the outside good plays a crucial role because it allows consumers to stop buying the differentiated good altogether if all brands simultaneously become less attractive (e.g. if a simultaneous price increase occurs). But practitioners lack data on the outside good when only aggregate data are available. The currently accepted procedure is to assume a “market potential” that implicitly defines the size of the outside good (i.e. the number of consumers who considered the product but did not purchase); in practice, this means that an endogenous quantity is approximated by a reasonable guess thereby introducing the possibility of an additional source of error and, most importantly, bias. We provide two contributions in this paper. First, we show that structural parameters can be substantially biased when the assumed market potential does not approximate the outside option correctly. Second, we show how to use panel data techniques to produce unbiased structural estimates by treating the market potential as an unobservable in both the simple and the random coefficients logit demand model. We explore three possible solutions: (a) controlling for the unobservable with market fixed effects, (b) specifying the unobservable to be a linear function of product characteristics, and (c) using a “demeaned regression” approach. Solution (a) is feasible (and preferable) when the number of goods is large relative to the number of markets, whereas (b) and (c) are attractive when the number of markets is too large (as in most applications in Marketing). Importantly, we find that all three solutions are nearly as effective in removing the bias. We demonstrate our two contributions in the simple and random coefficients versions of the logit model via Monte Carlo experiments and with data from the automobile and breakfast cereals markets.

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Liu, Pengfei, and Wei (David) Fan. "Modeling head-on crash severity on NCDOT freeways: a mixed logit model approach." Canadian Journal of Civil Engineering 46, no. 4 (April 2019): 322–28. http://dx.doi.org/10.1139/cjce-2018-0262.

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This study employs a mixed logit model approach to evaluate contributing factors that significantly affect the severity of head-on crashes. The head-on crash data are collected from Highway Safety Information System (HSIS) from 2005 to 2013 in North Carolina. The effects that vehicle, driver, roadway, and environmental characteristics have on the injury severity of head-on crashes are examined. The results of this research demonstrate that adverse weather, young drivers, rural roadways, and pickups are found to be better modeled as random-parameters at specific injury severity levels, while others should remain fixed. Also, the model results indicate that driving under the influence of alcohol or drugs, grade or curve roadway configuration, old drivers, high speed limit, motorcycles will increase the injury severity of head-on crashes. Adverse weather condition, two-way divided road, traffic control, young drivers, and pickups will decrease the injury severity of head-on crashes.

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Damsere-Derry, James, Emmanuel Kofi Adanu, Thomas Kolawole Ojo, and Enoch F. Sam. "Injury-severity analysis of intercity bus crashes in Ghana: A random parameters multinomial logit with heterogeneity in means and variances approach." Accident Analysis & Prevention 160 (September 2021): 106323. http://dx.doi.org/10.1016/j.aap.2021.106323.

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Ye, Fei, Wen Cheng, Changshuai Wang, Haoxue Liu, and Jiping Bai. "Investigating the severity of expressway crash based on the random parameter logit model accounting for unobserved heterogeneity." Advances in Mechanical Engineering 13, no. 12 (December 2021): 168781402110672. http://dx.doi.org/10.1177/16878140211067278.

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The present study utilized a random parameter logit (RPL) model to explore the nonlinear relationship between explanatory variables and the likelihood of expressway crash severity. The potential unobserved heterogeneity of data brought by China’s road traffic characteristics was fully considered. A total of 1154 crashes happened on Hang-Jin-Qu Expressway from 2013 to 2018 were analyzed. In addition to the conventional impact factors considered in the past, variables related to road geometry were also introduced, which contributed to expressway accidents significantly. The overall stability of the model estimation was examined by likelihood ratio test. Then, the average elastic coefficient of the significant factors at each severity level was also calculated. Several factors that significantly increase the fatal crash probability were highlighted: rainy/snowy/cloudy weather condition, low visibility (100– m), night without light, wet-skid road surface, being female, aged 41+ years, collision with a rigid barrier and some other obstacles, radius and length of horizontal curve, and longitudinal gradient. The parameters of four factors were random and obeyed normal distribution: night without light, being female, driving experience with 10 + years and with large vehicle responsible. These findings provide insights for better understanding of expressway crash severity. Some countermeasures were proposed about driver education, traffic law enforcement, vehicle and road design, environmental improvement, and so on.

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DeCarlo, Lawrence T. "A Signal Detection Model for Multiple-Choice Exams." Applied Psychological Measurement 45, no. 6 (May 25, 2021): 423–40. http://dx.doi.org/10.1177/01466216211014599.

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A model for multiple-choice exams is developed from a signal-detection perspective. A correct alternative in a multiple-choice exam can be viewed as being a signal embedded in noise (incorrect alternatives). Examinees are assumed to have perceptions of the plausibility of each alternative, and the decision process is to choose the most plausible alternative. It is also assumed that each examinee either knows or does not know each item. These assumptions together lead to a signal detection choice model for multiple-choice exams. The model can be viewed, statistically, as a mixture extension, with random mixing, of the traditional choice model, or similarly, as a grade-of-membership extension. A version of the model with extreme value distributions is developed, in which case the model simplifies to a mixture multinomial logit model with random mixing. The approach is shown to offer measures of item discrimination and difficulty, along with information about the relative plausibility of each of the alternatives. The model, parameters, and measures derived from the parameters are compared to those obtained with several commonly used item response theory models. An application of the model to an educational data set is presented.

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Wang, Chenzhu, Yangyang Xia, Fei Chen, Jianchuan Cheng, and Zeng’an Wang. "Assessment of Two-Vehicle and Multi-Vehicle Freeway Rear-End Crashes in China: Accommodating Spatiotemporal Shifts." International Journal of Environmental Research and Public Health 19, no. 16 (August 18, 2022): 10282. http://dx.doi.org/10.3390/ijerph191610282.

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Accounting for the growing numbers of injuries, fatalities, and property damage, rear-end crashes are an urgent and serious topic nowadays. The vehicle number involved in one crash significantly affected the injury severity outcomes of rear-end crashes. To examine the transferability and heterogeneity across crash types (two-vehicle versus multi-vehicle) and spatiotemporal stability of determinants affecting the injury severity of freeway rear-end crashes, this study modeled the data of crashes on the Beijing-Shanghai Freeway and Changchun-Shenzhen Freeway across 2014–2019. Accommodating the heterogeneity in the means and variances, the random parameters logit model was proposed to estimate three potential crash injury severity outcomes (no injury, minor injury, and severe injury) and identify the determinants in terms of the driver, vehicle, roadway, environment, temporal, spatial, traffic, and crash characteristics. The likelihood ratio tests revealed that the effects of factors differed significantly depending on crash type, time, and freeway. Significant variations were observed in the marginal effects of determinants between two-vehicle and multi-vehicle freeway rear-end crashes. Then, spatiotemporal instability was reported in several determinants, including trucks early morning. In addition, the heterogeneity in means and variances of the random parameters revealing the interactions of random parameters and other insignificant variables suggested the higher risk of determinants including speeding indicators, early morning, evening time, and rainy weather conditions. The current finding accounting for spatiotemporal instability could help freeway designers, decision-makers, management strategies to understand the contributing mechanisms of the factors to develop effective management strategies and measurements.

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Savolainen, Peter T. "Examining driver behavior at the onset of yellow in a traffic simulator environment: Comparisons between random parameters and latent class logit models." Accident Analysis & Prevention 96 (November 2016): 300–307. http://dx.doi.org/10.1016/j.aap.2016.01.006.

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Boeri, Marco, Daniel Saure, Alexander Schacht, Elisabeth Riedl, and Brett Hauber. "Modeling Heterogeneity in Patients’ Preferences for Psoriasis Treatments in a Multicountry Study: A Comparison Between Random-Parameters Logit and Latent Class Approaches." PharmacoEconomics 38, no. 6 (March 4, 2020): 593–606. http://dx.doi.org/10.1007/s40273-020-00894-7.

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Vermunt, Jeroen K. "Latent class and finite mixture models for multilevel data sets." Statistical Methods in Medical Research 17, no. 1 (February 2008): 33–51. http://dx.doi.org/10.1177/0962280207081238.

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An extension of latent class (LC) and finite mixture models is described for the analysis of hierarchical data sets. As is typical in multilevel analysis, the dependence between lower-level units within higher-level units is dealt with by assuming that certain model parameters differ randomly across higher-level observations. One of the special cases is an LC model in which group-level differences in the logit of belonging to a particular LC are captured with continuous random effects. Other variants are obtained by including random effects in the model for the response variables rather than for the LCs. The variant that receives most attention in this article is an LC model with discrete random effects: higher-level units are clustered based on the likelihood of their members belonging to the various LCs. This yields a model with mixture distributions at two levels, namely at the group and the subject level. This model is illustrated with three rather different empirical examples. The appendix describes an adapted version of the expectation—maximization algorithm that can be used for maximum likelihood estimation, as well as providing setups for estimating the multilevel LC model with generally available software.

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Rezapour, Mahdi, and Khaled Ksaiabti. "Factors Impacting the Choice of Seatbelt Use, Accounting for Complexity of Travelers’ Behaviors." Future Transportation 2, no. 1 (February 16, 2022): 237–48. http://dx.doi.org/10.3390/futuretransp2010012.

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Wyoming has one of the highest fatality rates, and a significantly lower rate of seatbelt use in the United States. Thus, this study was conducted with the objective to investigate contributory factors to the choice of drivers’ seatbelt use. Various environmental factors and drivers’ characteristics were considered as it is expected that they account for unseen factors that impact drivers’ choice of buckling up. Although the mixed model has been used extensively for studying the impacts of seatbelt use on the severity of crashes, not many studies have been conducted regarding factors contributing to the choice of seatbelt use itself. In this study, the standard logit model is extended to the mixed model to account for heterogeneity across drivers’ observations. In addition, the standard mixed model was extended to incorporate the random parameters’ heterogeneity in taste based on the means of other observed variables. The results highlighted that moving from the standard logit model to the mixed model, considering heterogeneity in tastes, results in a gain in the model fit, and also an adjustment for the model’s parameters’ estimates. The findings indicated that some of factors impacting the choice of wearing seatbelt include gender, road classification, weather condition, vehicle types, time of driving, vehicle registration and day of the week. Those factors are mainly related to unobserved factors impacting the drivers’ behaviors. For instance, drivers with particular characteristics are expected to own particular vehicle types or drive their vehicles under a particular weather condition.

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Tay, Richard, and Jaisung Choi. "Differences in Rental and Nonrental Car Crashes." Journal of Advanced Transportation 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/8757891.

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Although rental cars experience a higher collision rate per registered vehicle compared to nonrental cars, little research has been conducted to understand the differences in the factors contributing to crashes involving rental cars and nonrental cars, especially driver-related factors. This study develops a conceptual framework to compare the driver-related factors contributing to crashes involving rental cars and nonrental cars and tests the hypotheses developed using data from South Korea and applying the binary logistics, rare event logistics, Firth logistic models, and random parameters logit models. We found a significantly higher contribution of several risky driving behaviors but no differences in roadway, vehicle, and environmental factors. We also found that rental car crashes involve more males and drivers under 25 years of age.

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Mesa-Arango, Rodrigo, Víctor G. Valencia-Alaix, Raul A. Pineda-Mendez, and Taleb Eissa. "Influence of Socioeconomic Conditions on Crash Injury Severity for an Urban Area in a Developing Country." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 31 (May 14, 2018): 41–53. http://dx.doi.org/10.1177/0361198118758684.

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This paper includes macroeconomic conditions in an econometric framework to understand urban crash injury severity (CIS) in a developing country, and identify its distinctive socioeconomic conditions. The work combines classic variables from a unique data set of crashes in Medellín, Colombia, with macroeconomic indicators. A multinomial logit (MNL) model with random parameters mines valuable information from the data. Numerical results support the following CIS mitigation policies: upgrading intersections with traffic signals; incorporating forgiving roadway designs; providing better conditions for motorcyclists and non-motorized users; prioritizing education, outreach, and enforcement campaigns during periods of good macroeconomic conditions (for some segments of the population), high motorization rates, and regarding specific periods, that is, times within the day, the week, and the year.

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Dobra, Rebecca Anne, Marco Boeri, Stuart Elborn, Frank Kee, Susan Madge, and Jane C. Davies. "Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials." BMJ Open 11, no. 3 (March 2021): e045803. http://dx.doi.org/10.1136/bmjopen-2020-045803.

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IntroductionEngaging people with cystic fibrosis (CF) in clinical trials is critical to improving outcomes for this fatal disease. Following extensive exploration of engagement in CF trials we believe six key concepts require a quantitative understanding of their influence in the current CF trials landscape including how controversial issues like placebos, washouts, stipend provision and location of trial visits are viewed by the CF community and how these might be modified depending on the type of medicine being investigated and the mechanism of access to the drug on trial completion.Methods and analysisWe have designed and will administer an online discrete choice experiment to elicit and quantify preferences of people with CF for these trials’ attributes and estimate the relative importance of an attribute when choosing to participate in a trial. The cross-sectional data generated will be explored using conditional multinomial logit model. Mixed logit models such as the random-parameters logit and a latent class models will be used to explore preference heterogeneity. To determine the relative importance of an attribute, the difference between the attribute level with the highest preference weight and the level with the lowest preference weight will be calculated.Ethics and disseminationImperial College London Joint Research Compliance Office has granted ethical approval for this study. Patient consent will be sought following full explanation. No identifying information will be collected. Dissemination will be via international conferences, peer-review publication and patient accessible forums. Major CF trials networks have agreed to incorporate our findings into their review process, meaning our results can realistically influence and optimise CF trial delivery.PROSPERO registration numberCRD42020184886.

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Li, Ye, Ruifeng Gu, Jaeyoung Lee, Min Yang, Qinghong Chen, and Yinggui Zhang. "The dynamic tradeoff between safety and efficiency in discretionary lane-changing behavior: A random parameters logit approach with heterogeneity in means and variances." Accident Analysis & Prevention 153 (April 2021): 106036. http://dx.doi.org/10.1016/j.aap.2021.106036.

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Esmaili, Arsalan, Kayvan Aghabayk, and Nirajan Shiwakoti. "Latent Class Cluster Analysis and Mixed Logit Model to Investigate Pedestrian Crash Injury Severity." Sustainability 15, no. 1 (December 22, 2022): 185. http://dx.doi.org/10.3390/su15010185.

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Traffic crashes involving pedestrians have a high frequency in developing countries. Among road users, pedestrians are the most vulnerable, as their involvement in traffic crashes is usually followed by severe and fatal injuries. This study aims to identify pedestrian crash patterns and reveal the random parameters in the dataset. A three-year (2015–2017) pedestrian crash dataset in Mashhad, Iran, was employed to investigate the influence of a rich set of factors on pedestrian injury severity, some of which have been less accounted for in previous studies (e.g., the vicinity to overpasses, the existence of vegetated buffers, and park lanes). A two-step method integrating latent class cluster analysis (LCA) and the mixed logit model was utilized to consider unobserved heterogeneity. The results demonstrated that various factors related to the pedestrian, vehicle, temporal, environmental, roadway, and built-environment characteristics are associated with pedestrian injuries. Furthermore, it was found that integrated use of LCA and mixed logit models can considerably reduce the unobserved heterogeneity and uncover the hidden effects influencing severity outcomes, leading to a more profound perception of pedestrian crash causation. The findings of this research can act as a helpful resource for implementing effective strategies by policymakers to reduce pedestrian casualties.

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Dong, Bowen, Xiaoxiang Ma, Feng Chen, and Suren Chen. "Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model." Journal of Advanced Transportation 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/2702360.

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Road traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount importance to understand how these factors affect the occurrences of the crashes. Existing studies have suggested that the mechanisms of single-vehicle (SV) accidents and multivehicle (MV) accidents can be very different. Few studies were conducted to examine the difference of SV and MV accident probability by addressing unobserved heterogeneity at the same time. To investigate the different contributing factors on SV and MV, a mixed logit model is employed using disaggregated data with the response variable categorized as no accidents, SV accidents, and MV accidents. The results indicate that, in addition to speed gap, length of segment, and wet road surfaces which are significant for both SV and MV accidents, most of other variables are significant only for MV accidents. Traffic, road, and surface characteristics are main influence factors of SV and MV accident possibility. Hourly traffic volume, inside shoulder width, and wet road surface are found to produce statistically significant random parameters. Their effects on the possibility of SV and MV accident vary across different road segments.

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Journal articles on the topic 'Random parameters logit'

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