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The optimisation of inter-island transportation systems constitutes a critical determinant of regional economic development and the efficacy of mobility infrastructure. This study presents a comparative analysis of passenger mode selection between short-sea shipping (SSS) and road transport alternatives through stated preference surveys conducted via anonymised questionnaires. Employing advanced discrete choice modelling techniques – specifically the multinomial logit (MNL), random parameter logit (RPL) and latent class (LC) frameworks – we quantitatively disentangle the complex determinants influencing modal preferences. Our systematic sensitivity analysis reveals distinct behavioural patterns: passengers opting for SSS prioritise journey convenience, whereas road transport users exhibit stronger cost sensitivity. These findings provide actionable insights for formulating evidence-based policies to enhance intermodal transportation networks in the Zhoushan Archipelago of China. Beyond its immediate geographical focus, this research contributes methodological innovations by applying finite mixture models to capture unobserved heterogeneity in maritime transport decisions. The framework demonstrates significant transferability potential for island territories globally and urban freight corridor optimisation challenges, particularly in contexts requiring trade-off analyses between maritime efficiency and terrestrial logistics constraints.

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension-reduction structure in the model. An efficient Monte Carlo expectation-maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.

Background Consumption of sugar-sweetened beverages (SSBs) or sugary drinks may reduce or even eliminate the household income allocation for other essential commodities. Reducing expenditure for consumption of other household commodities is known as the crowding-out effect of SSB. We aimed to determine the crowding-out effect of SSB expenditure on other household commodities. In addition, we also identified the factors influencing the household's decision to purchase of SSBs. Methods We used the logistic regression (logit and multinomial logit models) and the Seemingly Unrelated Regression (SUR) models. In order to find the probability of a given change in the socio-demographic variables, we also estimated the average marginal effects from the logistic regression. In addition, we regressed the SUR model by gender differences. We used Household Income and Expenditure Survey (HIES) 2016 data to estimate our chosen econometric models. HIES is nationally representative data on the household level across the country and is conducted using a multistage random sampling method by covering 46,075 households. Results The findings from the logit model describe that the greater proportion of male members, larger household size, household heads with higher education, profession, having a refrigerator, members living outside of the house, and households with higher income positively affect the decision of purchasing SSB. However, the determinants vary with the various types of SSB. The unadjusted crowding out effect shows that expenditure on SSB or sugar-added drinks crowds out the household expenditure on food, clothing, housing, and energy items. On the other hand, the adjusted crowding out effect crowds out the spending on housing, education, transportation, and social and state responsibilities. Conclusion Although the household expenditure on beverages and sugar-added drinks is still moderate (around 2% of monthly household expenditure), the increased spending on beverages and sugar-added drinks is a concern due to the displacement of household expenditure for basic commodities such as food, clothing, housing, education, and energy. Therefore, evidence-based policies to regulate the sale and consumption of SSB are required for a healthy nation.

Parking around university campuses has become a major issue in recent decades because of nearby congestion impacts. Objective: To determine the factors influencing parking lot selection, which is crucial to propose adequate parking demand management strategies. Materials and Methods: We evaluate different attributes using a best-worst scaling survey applied at Universidad de la Costa (CUC), Colombia. Using discrete choice modeling techniques, we identified the extent to which selected infrastructure attributes influence parking behavior. Results: Security and cover (roof) availability are the most relevant attributes of parking choice in the case study. Conclusions: Based on our results, we strongly recommend implementing a dynamic pricing rate, roof pricing, removing “reserved spots” and investing in security.

Place or residence (POR) and place of work (POW) are two spatial pivots defining patterns of travel behavior. These choices are considered part of long-term choice influencing short-term daily travel choices. Hence, POR-POW distributions are input into almost all daily travel demand models. However, in many cases, POW-POR is modelled in an ad-hoc way considering the gravity-based or entropy is maximizing aggregate modelling approach. Lack of data on the sequence of choices related to POR and POW is often blamed for avoiding using disaggregate choice model. Recognizing such data limitation, this paper presents an alternative methodology of modelling joint distribution of POW-POW that uses disaggregate choice models without necessarily knowing the sequence of POR and POW choices. It uses the conditional probability break downs of joint POR-POW choice probabilities as depicted in the Gibbs sampling approach. This allows capturing effects of household socioeconomic characteristics, zonal land-use characteristics, and modal accessibility factors in the POR-POW models. The model is applied for a case study in the city of Ottawa. Results reveal that the proposed methodology can replicate observed patterns of POR-POW with a high degree of accuracy.

Anthropogenic pressures have exacerbated self-sustaining river services, and growing concerns over sustaining river system become global problematic issues that lead us to implement river restoration projects. Of those projects, governing diverse needs and desires from stakeholders for those who have various water values are key elements of identifying the success of the project. In fact, the Korean government has had concern over restoring the rivers which brings to construct 16 weirs in four major rivers and may fail to achieve main goal of the project, which is to ameliorate water quality. In this study, principle component analysis and multinomial logit model were executed to investigate major socioeconomic variables to influence water values in terms of sustainability in Korea. Evitable evidences have been found that age, income, education level, and city dwelling are the most effective variables to estimate water values. In addition, a monotonous water development project and a myopic view could cause major dejection across the nation and may lead to the failure of water governance. Unfortunately, the latter may be observed in Korea as one of the reasons for the recent amplification of major conflicts.

This article proposes a model of technology adoption that integrates demand for individual traits of new technologies with the potential for heterogeneity based on farm and farmer characteristics. The model is applied to recent genetically modified corn adoption data from Minnesota and Wisconsin farmers, using a mixed-multinomial logit (MMNL) model to estimate the effects of traits and farm and farmer characteristics on adoption outcomes. This approach allows explicit recovery of estimates of farmers' shadow prices for individual technology traits. Results show the importance of producer and regional heterogeneity in preferences for seed traits.

In recent decades, transportation planning researchers have used diverse types of machine learning (ML) algorithms to research a wide range of topics. This review paper starts with a brief explanation of some ML algorithms commonly used for transportation research, specifically Artificial Neural Networks (ANN), Decision Trees (DT), Support Vector Machines (SVM) and Cluster Analysis (CA). Then, these different methodologies used by researchers for modeling travel mode choice are collected and compared with the Multinomial Logit Model (MNL) which is the most commonly-used discrete choice model. Finally, the characterization of ML algorithms is discussed and Random Forest (RF), a variant of Decision Tree algorithms, is presented as the best methodology for modeling travel mode choice. En décadas recientes, los investigadores de planificación de transporte han usado diversos tipos de algoritmos de Machine Learning (ML, por sus siglas en inglés) para investigar un amplio rango de temas. Este artículo de revisión inicia con una breve explicación de algunos algoritmos de Machine Learning comúnmente utilizados para la investigación en transporte, específicamente Redes Neuronales Artificiales (ANN), Árboles de Decisión (DT), Máquinas de Vector de Soporte (SVM) y Análisis de Grupos (CA). Luego, estas diferentes metodologías usadas por investigadores para modelar la elección de modo de viaje son recogidos y comparados con el Modelo Logit Multinomial (MNL) el cual es el modelo de elección discreta más comúnmente utilizado. Finalmente, la caracterización de los algoritmos de ML es discutida y el Bosque Aleatorio (RF), una variante de los algoritmos de Árboles de Decisión, es presentado como la mejor metodología para modelar la elección de modo de viaje

We consider a dynamic assortment selection problem where in every round the retailer offers a subset (assortment) of N substitutable products to a consumer, who selects one of these products according to a multinomial logit (MNL) choice model. The retailer observes this choice, and the objective is to dynamically learn the model parameters while optimizing cumulative revenues over a selling horizon of length T . We refer to this exploration–exploitation formulation as the MNL-Bandit problem . Existing methods for this problem follow an explore-then-exploit approach, which estimates parameters to a desired accuracy and then, treating these estimates as if they are the correct parameter values, offers the optimal assortment based on these estimates. These approaches require certain a priori knowledge of “separability,” determined by the true parameters of the underlying MNL model, and this in turn is critical in determining the length of the exploration period. (Separability refers to the distinguishability of the true optimal assortment from the other suboptimal alternatives.) In this paper, we give an efficient algorithm that simultaneously explores and exploits, without a priori knowledge of any problem parameters. Furthermore, the algorithm is adaptive in the sense that its performance is near optimal in the “well-separated” case as well as the general parameter setting where this separation need not hold.

El concepto pueblos mágicos fue ideado para incrementar el turismo. Se concentra en la difusión de actividades atractivas en pequeñas localidades con atributos histórico-culturales que representan la identidad nacional. El objetivo de esta investigación fue estimar la probabilidad de seleccionar un determinado pueblo mágico del noroeste mexicano a partir de factores tales como el perfil del usuario, su experiencia, percepción y satisfacción de los servicios y el gasto efectuado durante su estancia. La metodología utilizada se basa en el modelo econométrico Logit Multinomial y estadística descriptiva con variables de control para su análisis a partir de la aplicación de una encuesta en cuatro pueblos mágicos: Todos Santos y Loreto, de Baja California Sur, y Cosalá y El Rosario, de Sinaloa. Los resultados muestran que las probabilidades condicionadas de escoger uno de ellos son mayores para Todos Santos con 29 %, seguido de Cosalá con 28 %, Loreto con 27 % y, finamente, El Rosario con 16 %. La variación de la elección está en función de las especificidades y características de los destinos. Los resultados del modelo permitirán la planeación de estrategias para mejorar la atención y la promoción turística en los cuatro pueblos mágicos estudiados, basándose en las preferencias de quienes eligen estos destinos, para atraer a más visitantes. The magical towns concept was devised to increase tourism. It focuses on the dissemination of attractive activities in small towns with historical-cultural attributes that represent the national identity. The objective of this research was to estimate the probability of selecting a certain magical town in northwestern Mexico based on factors such as the user’s profile, their experience, perception and satisfaction of the services and the expenditure made during their stay. The methodology used is based on the Multinomial Logit econometric model and descriptive statistics with control variables was used for its analysis. The survey was administered in four magical towns: Todos Santos and Loreto, from Baja California Sur, and Cosalá and El Rosario, from Sinaloa. Results show that the probabilities of choosing one of them are higher for Todos Santos with 29 %, followed by Cosalá with 28 %, Loreto with 27 % and, finally, El Rosario with 16 %. The variation of the choice is a function of the specificities and characteristics of the destinations. The results of the model will allow the planning and strategies to improve tourist service and promotion in the four magical towns studied, based on the preferences of those who choose these destinations, to attract more visitors.