Latent Class Multinomial Logit Model Research Articles

Is it more important to increase carbon sequestration, biodiversity, or jobs? A case study of citizens' preferences for forest policy in Finland

Forest management plays an important role in mitigating the most serious threats to the well-being of current and future generations: climate change and biodiversity loss. We examine citizens' preferences for alternative forest management policy goals, including the provision of carbon sinks, biodiversity, and forest sector jobs, using a discrete choice experiment in Finland. We explore preference heterogeneity using latent class multinomial logit models finding the highest willingness to pay for biodiversity-oriented forest policy. Comparing three forest policy scenarios, focusing on “Biodiversity,” “Climate,” or “Bioeconomy,” citizens preferred the overall environmental goals over economic ones. We observe considerable heterogeneity in respondents' preferences identifying five latent preference classes. The results provide valuable background information on citizens' values and preferences on tradeoffs built into future revisions of forest policy.

Alternative unobserved heterogeneity models to analyze injury severity of expressway crashes in different tunnel types

This study examines the non-transferability of the determinants affecting injury severities of tunnels of three grades classified by different lengths. Based on tunnel crash data in Shanxi Province during (2012–2018), three possible crash injury severity categories (no injury, minor injury, and severe injury) are estimated. Two alternate models were used to account for unobserved heterogeneity: a random parameters multinomial logit (RPML) model with heterogeneity in means and variances and a latent-class multinomial logit (LCML) model with class probability functions. Non-transferability in the effects of explanatory variables is confirmed using two series of likelihood ratio tests based on the two alternate models. A wide variety of variables is observed to determine pedestrian-injury severities. The estimation results show significant non-transferability across the three grades in both RPML and LCML models. In addition, several explanatory variables produce relatively transferable effects, which can provide some valuable insights to implement effective countermeasures. Moreover, out-of-sample predictions are simulated to confirm the non-transferability, while the LC models produce higher differences for the non-transferability than RP models. According to estimation results, several practical measurements concerning education programs, traffic enforcement, emergency response systems, and traffic safety measurements are implemented to eliminate tunnel crash outcomes. Understanding and depth comparing the estimation results, likelihood ratio tests and out-of-sample predictions using alternate models is a promising direction for future research to explore how the observed and unobserved heterogeneity can be estimated.

Patient Preferences for Multi-Cancer Early Detection (MCED) Screening Tests.

Emerging blood-based multi-cancer early detection (MCED) tests can detect a variety of cancer types across stages with a range of sensitivity, specificity, and ability to predict the origin of the cancer signal. However, little is known about the general US population's preferences for MCED tests. To quantify preferences for MCED tests among US adults aged 50-80 years using a discrete choice experiment (DCE). To quantify preferences for attributes of blood-based MCED tests, an online DCE was conducted with five attributes (true positives, false negatives, false positives, likelihood of the cancer type unknown, number of cancer types detected), among the US population aged 50-80 years recruited via online panels and social media. Data were analyzed using latent class multinomial logit models and relative attribute importance was obtained. Participants (N = 1700) were 54% female, mean age 63.3 years. Latent class modeling identified three classes with distinct preferences for MCED tests. The rank order of attribute importance based on relative attribute importance varied by latent class, but across all latent classes, participants preferred higher accuracy (fewer false negatives and false positives, more true positives) and screenings that detected more cancer types and had a lower likelihood of cancer type unknown. Overall, 72% of participants preferred to receive an MCED test in addition to currently recommended cancer screenings. While there is significant heterogeneity in cancer screening preferences, the majority of participants preferred MCED screening and the accuracy of these tests is important. While the majority of participants preferred adding an MCED test to complement current cancer screenings, the latent class analyses identified a small (16%) and specific subset of individuals who value attributes differently, with particular concern regarding false-negative and false-positive test results, who are significantly less likely to opt-in.

Purchase preferences-based air passenger choice behavior analysis from sales transaction data

Travel providers such as airlines are becoming more and more interested in understanding how passengers choose among alternative products, especially the purchasing preferences of passengers. Getting information of air passenger choice behavior helps them better display and adapt their offer. Discrete choice models are appealing for airline revenue management (RM). In this paper, we apply latent class multinomial logit model (LC-MNL) to passenger choice behavior. The analysis based on actual sales transaction data reveals the purchase preferences of different passenger types. According to the distribution of the market, we divide passengers into three groups: low-price oriented, high-price oriented and no specific price preference. The low-price oriented passengers only choose products from the set which consists of the lowest price cabin classes of each flight, while the high-price oriented passengers do the opposite. Considering that the passenger types in the transaction sales data are unknown, the latent class passenger choice model can better represent their heterogeneous purchasing preference. An improved EM algorithm is applied to solve the LC-MNL. In the improved EM algorithm, an indicator function containing both the type of passengers and the first choice information in period t is devised, the iterative process of the EM algorithm is more effective consequently. The proposed model and algorithm are evaluated on actual aviation sales transaction data in China. Experimental results show that the passenger choice behavior analysis based on the specific purchasing preferences performs well on actual aviation sales transaction data.

Examining the who, what, and how of risky driving related crashes in residential areas

This study contributes to efforts towards creating livable communities, by identifying and proposing countermeasures for factors that are associated with residential area crashes. Three risky driving pre-crash actions: driving under the influence of alcohol or drug, aggressive driving, and speeding were considered. The study used residential area crash data from Alabama, USA and developed a latent class multinomial logit model to identify factors associated with the pre-crash actions. The results showed that male drivers had 0.44% and 2.09% higher chances to be at fault in driving under the influence of alcohol or drug and aggressive driving crashes, respectively, while female drivers were found to have a 1.82% higher likelihood to be at fault in speeding-related crashes but less likely to be at fault in aggressive driving or driving under the influence of alcohol or drug-related crashes in residential areas. Speeding and driving under the influence of were found to be the leading contributing factors in rural area residential crashes whereas aggressive driving was more pronounced in urban areas. These findings provide a basis for the implementation of appropriate crash countermeasures to reduce residential area crashes, as part of efforts geared towards promoting community safety and livability across the state.

A Multi-Country Comparison of Consumers’ Preferences for Imported Fruits and Vegetables

Within Asia, imported fruits and vegetables are often considered as a delicacy and of high value, and are increasingly demanded compared to local products. There are numerous significant factors involved with consumers’ characteristics and their corresponding values towards these products. This study investigates potential consumers and their preferences towards imported fruits and vegetables in three Asian countries: Taiwan, Japan, and Indonesia. A total of 1350 survey responses collected from Taiwan, Japan, and Indonesia are examined by a best–worst scaling method with a latent class multinomial logit model. Results show that consumers tend to choose imported fruits that are not commonly provided by domestic producers. While a food safety certified label and freshness are consistently identified as the most and second most important food values for Taiwanese, Japanese, and Indonesian consumers, price is still an important factor for certain consumer groups. The majority of Taiwanese and Japanese consumers (i.e., female, higher education, and from an urban area) prefer imported fruits and vegetables, while the majority of Indonesian consumers do not pay much attention to imported fruits and vegetables. While Taiwan, Japan, and Indonesia are island countries, the novelty of this study shows that consumer preferences do not behave the same. The implications of this study should be of interest to producers and exporters who wish to positively impact the design of their international marketing strategies.

The Latent Class Multinomial Logit Model for Modeling Front-Seat Passenger Seatbelt Choice, Considering Seatbelt Status of Driver

The literature review highlighted the impacts of drivers’ behavior on passengers’ attitudes in the choice of seatbelt usage. However, limited studies have been done to determine those impacts. Studying the passengers’ seatbelt use is especially needed to find out why passengers choose not to buckle up, and consequently it helps decision makers to target appropriate groups. So, this study was conducted to find drivers’ characteristics that might impact the passenger’s seatbelt use, in addition to other passengers’ characteristics themselves. While performing any analysis, it is important to use a right statistical model to achieve a less biased point estimate of the model parameters. The latent class multinomial logit model (LC-MNL) can be seen as an alternative to the mixed logit model, replacing the continuous with a discrete distribution, by capturing possible heterogeneity through membership in various clusters. In this study, instead of a response to the survey or crash observations, we employed a real-life observational data for the analysis. Results derived from the analysis reveal a clear indication of heterogeneity across individuals for almost all parameters. Various socio-demographic variables for class allocation and models with different latent numbers were considered and checked in terms of goodness of fit. The results indicated that a class membership with three factors based on vehicle type would result in a best fit. The results also highlighted the significant impacts of driver seatbelt status, time of a day, distance of traveling, vehicle type, and driver gender, instead of passenger gender, as some of the factors impacting the passengers’ choice of seatbelt usage. In addition, it was found that the belting status of passengers is positively associated with the belting condition of drivers, highlighting the psychological behavioral impact of drivers on passengers. Extensive discussion has been made regarding the implications of the findings.

Development of Population Tariffs for the ICECAP-A Instrument for Hungary and their Comparison With the UK Tariffs

ObjectivesThe ICEpop CAPability measure for Adults (ICECAP-A) was developed to assess the capability well-being of adults for use in economic evaluations. Currently, ICECAP-A tariffs are available only for the UK population. The objectives of this study were to develop a Hungarian tariff set for the ICECAP-A instrument and to explore intercountry differences between the Hungarian and the UK value sets. MethodsA survey was conducted by computer-assisted personal interviews on a sample representative of the Hungarian adult population (N = 1000) to elicit their preferences regarding ICECAP-A attributes with the use of a best-worst scaling choice task. A latent class multinomial logit model with continuous variance scale was used to estimate the weights for each of the 4 capability levels of all 5 ICECAP-A attributes, namely, attachment, stability, achievement, enjoyment, and autonomy. ResultsThe model identified 2 preference classes with approximately equal share. The first class had a stronger relative preference for autonomy and achievement, whereas the second class had a strong preference for attachment. Multivariate analysis of the classes revealed that women, pensioners, people who are married or living in a partnership, and people with poorer health status are characteristics associated with the latter class membership (preference for attachment). Population tariffs were estimated from the model. Overall, attachment was found to be the most important attribute, followed by stability, enjoyment, achievement, and autonomy. ConclusionsHungarian tariffs are largely consistent with those found for the United Kingdom; nevertheless, autonomy seems to be less important in Hungary compared with the United Kingdom.

Heterogeneity in physical activity participation of older adults: A latent class analysis

Participation in leisure and transport-related physical activities is generally viewed as the main means of increasing overall physical activity levels of older adults. The success of any intervention strategy depends on how well the strategy fits older people's physical activity patterns. This study therefore examines heterogeneity in physical activity patterns of older adults and the relationships between these patterns and socio-demographic and neighborhood environmental characteristics. This is done using a latent class multinomial logit model based on one-week diary data collected on 363 Dalian Chinese older adults aged 60 and over in 2017. The results show that two segments of physical activity patterns of older adults can be identified: leisure physical activities oriented and both transport-related and leisure physical activities oriented. Age, physical limitation and having grandchildren in the household have an important influence on physical activity patterns of older people. However, there are no significant relationships between physical activity patterns and various environmental characteristics, except accessibility of local shops, condition of footpaths and safety from crime. The empirical findings have implications for the development of tailored interventions to maintain or promote physical activity of older adults.

Comparison of food values for consumers’ preferences on imported fruits and vegetables within Japan, Taiwan, and Indonesia

International agricultural trade in fruits and vegetables has received much attention as an effective method to create a sustainable agriculture industry for a country. This study focuses on consumers’ perceptions of food values toward imported fruits and vegetables in Japan, Taiwan, and Indonesia by using the Best-Worst Scaling method. Seven food values were examined in this study: labeling product origin, food safety certification, high quality appearance, domestic rarity, price, how the product was grown, and freshness. An online survey was conducted and 1,350 total valid respondents were collected (500 Japanese, 333 Taiwanese, and 517 Indonesian). The Latent Class Multinomial Logit model was used to analyze consumers for each country. Results revealed that food safety certification and freshness were found as the most important and second most important food values for the majority group of consumers in each of these three countries, respectively. However, the remainder of food values do not have the same importance for each country. The study should help governments and international marketers enhance their agricultural trade policies and marketing strategies for these targeted markets.

Role of Passengers in Single-Vehicle Drunk-Driving Crashes: An Injury-Severity Analysis

Background: Drunk-driving is a major crash risk factor, and crashes resulting from this risky behavior tend to be serious and have significant economic and societal impacts. The presence of passengers and their demographics and activities can influence risky driving behaviors such as drunk-driving. However, passengers could either be an “enabling” factor to take more risks or could be an “inhibiting” factor by ensuring safe driving by a drunk-driver. Objective: This study examines whether the presence of passengers affects the contributing factors of single-vehicle (SV) drunk-driving crashes, by presenting a severity analysis of single- and multi-occupant SV drunk-driving crashes, to identify risk factors that contribute to crash severity outcomes, for the effective implementation of relevant countermeasures. Method: A total of 7407 observations for 2012–2016 from the crash database of the State of Alabama was used for this study. The variables were divided into six classes: temporal, locational, driver, vehicle, roadway, and crash characteristics and injury severities into three: severe, minor, and no injury. Two latent class multinomial logit models—one each for single- and multi-occupant crashes—were developed, to analyze the effects of significant factors on injury severity outcomes using marginal effects. Results: The estimated results show that collision with a ditch, run-off road, intersection, winter season, wet roadway, and interstate decreased the probability of severe injuries in both single- and multi-occupant crashes, whereas rural area, road with downward grade, dark and unlit roadway, unemployed driver, and driver with invalid license increased the likelihood of severe injuries for both single- and multi-occupant crashes. Female drivers were more likely to be severely injured in single-occupant crashes, but less likely in multi-occupant crashes. A significant association was found between severe injuries and weekends, residential areas, and crash location close (<25 mi ≈40.23 km) to the residence of the at-fault driver in multi-occupant crashes. Sport utility vehicles were found to be safer when driving with passengers. Conclusions: The model findings show that, although many correlates are consistent between the single- and multi-occupant SV crashes that are associated with locational, roadway, vehicle, temporal, and driver characteristics, their effect can vary across the single- and multi-occupant driving population. The findings from this study can help in targeting interventions, developing countermeasures, and educating passengers to reduce drunk-driving crashes and consequent injuries. Such integrated efforts combined with engineering and emergency response may contribute in developing a true safe systems approach.

Airline itinerary choice modeling using machine learning

Understanding how customers choose between different itineraries when searching for flights is very important for the travel industry. This knowledge can help travel providers, either airlines or travel agents, to better adapt their offer to market conditions and customer needs. This has a particular importance for pricing and ranking suggestions to travelers when searching for flights. This problem has been historically handled using Multinomial Logit (MNL) models. While MNL models offer the dual advantage of simplicity and readability, they lack flexibility to handle collinear attributes and correlations between alternatives. Additionally, they require expert knowledge to introduce non-linearity in the effect of alternatives’ attributes and to model individual heterogeneity. In this work, we present an alternative modeling approach based on non-parametric machine learning (ML) that is able to automatically segment the travelers and to take into account non-linear relationships within attributes of alternatives and characteristics of the decision maker. We test the models on a dataset consisting of flight searches and bookings on European markets. The experiments show our approach outperforming the standard and the latent class Multinomial Logit model in terms of accuracy and computation time, with less modeling effort.

Locations that Support Social Activity Participation of the Aging Population.

Social activities are an important aspect of health and quality of life of the aging population. They are key elements in the prevention of loneliness. In order to create living environments that stimulate older adults to engage in social activities, more insight is needed in the social activity patterns of the aging population. This study therefore analyzes the heterogeneity in older adults’ preferences for different social activity location types and the relationship between these preferences and personal and mobility characteristics. This is done using a latent class multinomial logit model based on two-day diary data collected in 2014 in Noord-Limburg in the Netherlands among 213 respondents aged 65 or over. The results show that three latent classes can be identified among the respondents who recorded social activities in the diary: a group that mainly socializes at home, a group that mainly socializes at a community center and a group that is more likely to socialize at public ‘third’ places. The respondents who did not record any interactions during the two days, are considered as a separate segment. Relationships between segment membership and personal and mobility characteristics were tested using cross-tabulations with chi-square tests and analyses of variance. The results suggest that both personal and mobility characteristics play an important role in social activity patterns of older adults.

Social Interaction Location Choice: A Latent Class Modeling Approach

Social contacts are an important aspect of an individual's quality of life. Social contacts take place at a certain time and location: Geography matters, for instance, at home or a work location or at different types of (local) facilities such as schools, shops, sports, and catering facilities. For urban planners, it is essential to know which locations provide opportunities for social interaction. As this knowledge is currently largely lacking, more empirical research is needed. The purpose of this study, therefore, is to analyze the diversity of participation in social activities at different locations and the relationship among social interaction location, sociodemographic characteristics, and characteristics of the residential environment. The analyses are based on two-day social interaction diary data that were collected in 2008 among 747 respondents living in the Eindhoven region in The Netherlands. A latent class multinomial logit model is used to segment respondents in terms of their social activity location choices. The article reports findings of several descriptive analyses and the latent class model. Four latent classes are identified, showing different patterns in choices for social activity locations. Latent class membership can be explained by household and personal time-use characteristics (e.g., gender, age, household type, number of face-to-face social interactions, frequency of contact with neighbors), as well as characteristics of the residential environment (e.g., urban density, distance to several facilities, and satisfaction with local facilities). The findings could provide useful information for local governments and planners regarding the importance of public facilities for social interaction of various segments of the population to support individual well-being and neighborhood livability.

A latent class analysis of single-vehicle motorcycle crash severity outcomes

Unobserved heterogeneity has been recognized as a critical issue in traffic safety research that has not been completely addressed or often overlooked, and can lead to biased estimates and incorrect inferences if inappropriate methods are used. This paper uses a latent class approach to investigate the factors that affect crash severity outcomes in single-vehicle motorcycle crashes. Motorcycle crash data from 2001 to 2008 in Iowa were collected with a total of 3644 single-vehicle motorcycle crashes occurring during that time period. A latent class multinomial logit model is estimated that addresses unobserved heterogeneity by identifying two distinct crash data classes with homogeneous attributes. The estimation results show a significant relationship between severe crash injury outcomes and crash-specific factors (such as speeding, run-off road, collision with fixed object and overturn/rollover), riding on high-speed roads, riding on rural roads, riding on dry road surface, riding without a helmet, age (riders older than 25 years old) and impaired riding (riders under the influence of drug, alcohol or medication). The model fit and estimation results underline the need for segmentation of crashes, and suggest that the latent class approach can be a promising tool for modeling motorcycle crash severity outcomes.

The effect of waiting time and distance on hospital choice for English cataract patients

This paper applies latent-class multinomial logit models to the choice of hospital for cataract operations in the UK NHS. We concentrate on the effects of travel time and waiting time and especially on estimating the waiting time elasticity of demand. Models including hospital fixed effects rely on changes over time in waiting time to indentify coefficients. We show how using a latent-class multinomial logit model characterises the unobserved heterogeneity in GP practices' choice behaviour and affects the estimated elasticities of travel time and waiting time. The models estimate waiting time elasticities of demand of approximately -0.1, comparable with previous waiting time-demand models. For the average waiting time elasticity, the simple multinomial logit models are good approximations of the latent-class logit results.

Evaluating off‐site environmental mitigation using choice modelling*

Evaluation of off‐site mitigation entails comparison of utility changes between two sites. Choice modelling has been used to identify community willingness to trade‐off attributes for two different types of stream in New Zealand. Estimated utility functions are used to derive marginal rates of substitution and stream attribute part worths which can be used to design or evaluate both on‐site and off‐site mitigation policy. Latent class multinomial logit models identified classes of citizens who valued stream attributes quite differently. Significant differences in values for some attributes on different stream types imply heterogeneous mitigation ratios across environmental attributes.

A Sequential Method for Combining Random Utility Model and Fuzzy Inference Model

In the analysis of choice behavior problem, uncertainty can be divided into two different types: randomness and vagueness. Random utility model and fuzzy inference model have been widely used to consider the randomness and the vagueness, respectively. Despite the necessity of simultaneously considering both uncertainties in choice behavior analysis, few literatures have tried to combine the two types of choice behavior models. Therefore, the aim of this paper is to suggest a model combining the randomness and the vagueness in the context of driver’s route choice behavior under traffic information. To estimate the combined model, a sequential method is suggested as follows: First, a latent class multinomial logit model (LCML) is developed to consider the randomness of route choice behaviors and to analyze the heterogeneity among drivers. Second, a fuzzy inference model is developed to consider the vagueness. Finally, the combined model is established by combining the estimation results of the LCML and the fuzzy inference models. The empirical results in this paper show that the combined model can contribute to enhance the explanatory power of the LCML model by effectively incorporating the randomness and the vagueness uncertainty in the choice behavior model.

Optimizing Television Program Schedules Using Choice Modeling

The authors examine the rescheduling of television programs to maximize the total ratings for one network across a week. The key idea is to design a choice experiment in which television programs are rescheduled and presented to respondents. Respondents read these program schedules (much like the regular TV Guide listings) and give their preferences, including not watching any of the listed programs. Because there are potentially billions of possible schedules, the authors give a procedure for designing a fractional factorial experiment that can accommodate both programs of varying length and constraints on eligible program times. The authors also develop a latent class multinomial logit model for modeling program preferences and present a validation of our experimental procedure and the model. They also present an empirical test of the procedure in which they use the model to predict ratings for all the possible program schedules, not just those constituting the choice sets. In this example, the optimum schedule increases the predicted total weekly ratings during prime time by 18% for a network. The projected increase in total weekly ratings is achieved without the network needing to purchase any new programs; all it needs to do is reschedule eight programs in its existing prime-time lineup.