Integrating a Pareto-Distributed Scale into the Mixed Logit Model: A Mathematical Concept

More than 2,000 pedestrians are involved in reported traffic crashes with vehicles in North Carolina every year. Of these, 10% to 20% of these pedestrians were killed or severely injured. Studies are needed to explore the reasons under such situation for improvement. Traditional methods for modeling crash injury severities use multinomial logit (MNL) models, mixed logit (ML) models, or ordered logit/probit models. However, traditional MNL and ML models treat injury severity levels as nonordered, ignoring the inherent hierarchical nature of crash injury severities, whereas the data used in ordered logit models should be strictly subjected to the proportional odds (PO) assumption. A partial proportional odds (PPO) logit model approach is applied here to address these concerns. The predictors in the PPO model can have different effects on different levels of the dependent variable who violates the PO assumption. This study uses police-reported pedestrian crash data collected from 2007 to 2014 in North Carolina. A variety of motorist, pedestrian, environmental, roadway characteristics are examined. Comparisons of different models are also made and results show that PPO outperforms others. Parameter estimates and associated marginal effects are calculated and used to interpret the model and evaluate the significance of each dependent variables, followed by recommendations in the Conclusion.

This paper investigates freight market segmentation to seek the potential market of modal shift from road to rail-based intermodal (only pre- and end- haulage are transported by road), using discrete choice analysis. The Defficient design approach was used to generate SP scenarios. The data, including 2160 SP observations of 135 companies, was collected in China's Yangtze River Delta region. The multinomial logit (MNL) model, the mixed logit (ML) model, and the latent class (LC) model were constructed successively. Freight mode attributes (cost, time and reliability), shipment characteristics (distance, time threshold), cargo characteristics (value and density) were considered. In addition, the logistics elements such as the stages of production process and psychological characteristics such as choice inertia were also introduced in this paper. The results show that the LC model with segment variables performs better than the counterpart without segment variables, the MNL model and the ML model. Using the LC model, shippers are classified into four market segments. There are significant differences in shippers' preference among different classes. Based on the class membership function, the characteristic of each class can be identified by a combination of different attributes. The difficulty of achieving the modal shift from road to rail-based intermodal is ranked according to the values of attributes, further determining the priority of the potential market. This study helps to understand the shippers' freight mode choice better. Rail service suppliers can design transportation products that match the shippers' demand characteristics for different classes to improve the market share.

Although much work has been devoted to developing crash severity models to predict the probabilities of crashes for different severity levels, few studies have considered the underreporting issue in the modeling process. Inferences about a population of interest are biased if crash data are treated as a random sample from the population without consideration of the different unreported rates for each crash severity level. The primary objective of this study was to examine the effects of underreporting for three commonly used traffic crash severity models: multinomial logit (MNL), ordered probit (OP), and mixed logit (ML) models. The objective was accomplished with a Monte Carlo approach that used simulated and observed crash data. The results showed that, to minimize the bias and reduce the variability of a model, fatal crashes should be set as the baseline severity for the MNL and ML models, while for the OP models, the rank for the crash severity should be set from fatal to property damage only in a descending order. None of the three models was immune to this underreporting issue. When full or partial information about the unreported rates for each severity level was known, treatment of the crash data as outcome-based samples in model estimation (through the weighted exogenous sample maximum likelihood estimator) dramatically improved the estimation for all three models as compared with the results produced from the maximum likelihood estimator.

This study investigates discrepancies in offset choices between real-world and hypothetical scenarios by comparing stated preference (SP) and self-reported revealed preference (RP) data. Using multinomial logit (MNL) and mixed logit (MXL) models, key findings are: (1) preferences for offset contributions and prices are consistent across SP and RP, but differ in project types, locations, and certifications. (2) partial data enrichment harmonizes SP and RP within MNL models, outperforming full enrichment. (3) positive state dependence shows previous choices influence stated ones, with individual heterogeneity in MXL models. (4) incorporating unobserved heterogeneity and state-dependence effects is crucial when merging SP and RP.

Like other developed countries, Australia is experiencing considerable growth in air travel demand. This growth is putting tremendous pressure on airports to improve the capacity and level of service of access and egress modes. The main goal of this study was to develop a robust mode choice model for passengers’ access to airports, in the context of Brisbane, Australia. The novelty of this study includes (1) the use of recent techniques to combine passengers’ revealed and stated preferences for mode of access to airports and (2) the development of both traditional multinomial logit (MNL) as well as mixed logit (MXL) models using these data. The data were collected from 1,435 passengers through an online survey of both revealed and stated preferences, with the stated preferences pivoting on their most recent trip to Brisbane Airport. With these data, access mode choice models were estimated. MNL and MXL models were estimated that directly considered the effects of passenger mode choice inertia as well as novel passenger-specific characteristics. These factors had a large, statistically significant effect on the estimated models. The benefit of the MXL model was shown in the results, as the passengers’ value of time from the MXL model was A$105.15/h, which was strikingly lower than the MNL-estimated value of time. In addition, there were notably high direct elasticities for bus and shuttle with respect to travel time, and for car and taxi with respect to travel cost.

Abstract: Econometric models involving a discrete outcome dependent variable abound in the finance and accounting literatures. However, much of the literature to date utilises a basic or standard logit model. Capitalising on recent developments in the discrete choice literature, we examine three advanced (or non‐IID) logit models, namely: nested logit, mixed logit and latent class MNL. Using an illustration from corporate takeovers research, we compare the explanatory and predictive performance of each class of advanced model relative to the standard model. We find that in all cases the more advanced logit model structures, which correct for the highly restrictive IID and IIA conditions, provide significantly greater explanatory power than standard logit. Mixed logit and latent class MNL models exhibited the highest overall predictive accuracy on a holdout sample, while the standard logit model performed the worst. Moreover, the analysis of marginal effects of all models indicates that use of advanced models can lead to more insightful and behaviourally meaningful interpretations of the role and influence of explanatory variables and parameter estimates in model estimation. The results of this paper have implications for the use of more optimal logit structures in future research and practice.

Extending the traditional discrete choice model by incorporating latent psychological factors can help to better understand the individual’s decision-making process and therefore to yield more reliable part-worth estimates and market share predictions. Several integrated choice and latent variable (ICLV) models which merge the conditional logit model with a structural equation model exist in the literature. They assume homogeneity in the part-worths and use latent variables to model the heterogeneity among the respondents. This paper starts from the mixed logit model that describes the heterogeneity in the part-worths and uses the latent variables to decrease the unexplained part of the heterogeneity. The empirical study presented here shows these ICLV models perform very well with respect to model fit and prediction.

Multinomial logit models and nested logit models are limited in that they cannot accommodate unobserved variations in travelers' taste and they do not have flexible substitution patterns among alternatives because of the independence of irrelevant alternatives property. Taking this background into account, traffic demand analysts have recently used the mixed logit model in many studies. Unfortunately, most of the studies in the literature for joint analysis of revealed-preference (RP) and stated-preference (SP) data could not simultaneously resolve the two limitations just mentioned. The mixed logit framework is used to formulate an intercity travel mode choice model for joint RP-SP analysis that accommodates the following behavioral considerations: ( a) observed and unobserved heterogeneity across individuals in response to level-of-service (LOS) factors, ( b) heteroscedasticity across alternatives, and ( c) scale differences between the RP and SP choice contexts. The mixed logit formulation is estimated with the maximum simulated likelihood method that employs quasi-random Halton draws. The formulation is applied to examine the travel behavior responses of users of the HoNam, South Korea, high-speed rail to changes in travel conditions. The empirical results show that there is a significant heteroscedasticity across alternatives and a significant heterogeneity in response to LOS attributes based on both observed and unobserved individual characteristics. There is an improvement in the data-fit statistics when one introduces heterogeneity and heteroscedasticity. These results highlight the need to include heterogeneity and heteroscedasticity within the context of intercity travel mode choice modeling to assist policy decision making.

Market segmentation analysis for airport access mode choice modeling with mixed logit

Inspired by the interactive discrete choice logit models [Aggarwal, 2019], this paper presents the advanced families of discrete choice models, such as nested logit, mixed logit, and probit models to consider the interaction among the attributes. Besides the DM's attitudinal character is also taken into consideration in the computation of choice probabilities. The proposed choice models make use of Choquet integal and the recent attitudinal Choquet integral. The models are termed as Choquet nested logit (CNL), Choquet mixed multinomial logit (CMMNL), and Choquet multinomial probit (CMP). These are further extended to represent the DM's attitudinal character, and termed as attitudinal CNL, attitudinal CMMNL, and attitudinal CMP.

Utility independence versus IIA property in independent probit models

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.

Using vehicular trajectory data to explore risky factors and unobserved heterogeneity during lane-changing

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 purpos...

Examining crash injury severity and barrier-hit outcomes from cable barriers and strong-post guardrails on Alabama’s interstate highways