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Climate change poses a serious challenge to the livestock sector in Indonesia, particularly for beef cattle farmers in East Java who face declining productivity due to droughts, shifting rainfall patterns, and rising temperatures. Adaptation is therefore essential to sustain livestock production and maintain household economic stability. This study analyses the socioeconomic factors influencing the choice of adaptation strategies among beef cattle farmers in response to climate change. A total of 300 farmers were selected using a multistage sampling technique, and data were collected through structured interviews. The analysis employed a Multinomial Logit Model to examine the effects of socioeconomic characteristics on the choice between on-farm, off-farm, and combined strategies. The results show that 63 % of farmers adopted off-farm strategies such as enterprise diversification and secondary employment, while 11 % focused on on-farm strategies, and 26 % combined both approaches. Education, primary occupation, and access to agricultural residues significantly influenced the choice of adaptation strategy. Higher education and better access to agricultural residues increased the likelihood of adopting off-farm strategies, whereas having livestock farming as the primary occupation reduced the tendency toward diversification. These findings highlight the importance of human, natural, and financial capital in shaping farmers' adaptive capacity. Strengthening farmers' skills and optimizing local resource utilization are key to promoting sustainable and inclusive adaptation in the livestock sector.

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.

An empirical study of the parking behaviour of Conventional Vehicles (CVs), Battery Electric Vehicles (BEVs), and Plug-in Hybrid Electric Vehicles (PHEVs) was carried out with the data collected in a paper-based questionnaire survey in Beijing, China. The study investigated the factors that might influence the parking behaviour, with a focus on the maximum acceptable time of walking from parking lot to trip destination, parking fee, the availability of charging posts, the state of charge of EVs and the range anxiety of BEVs. Several Multinomial Logit (MNL) models were developed to explore the relationships between individual attributes and parking choices. The results suggest that (1) the maximum acceptable walking time generally increases with the rise in the amount of saving for parking fee; (2) the availability of charging posts does not influence the maximum acceptable walking time when PHEVs and BEVs have sufficient charge, but the percentage of people willing to walk longer than eight minutes increases from around 35% to 46% when PHEVs are in a low stage of charge; (3) more than half of BEV drivers want the driving range of their vehicles to be one and a half times the driving distance before they depart, given the distance is 50 km. Based on the empirical findings above, a conceptual framework was proposed to explicitly simulate the parking behaviour of both CVs and EVs using agent-based modelling.

The Bali Government has made the implementation of the electric vehicle (EV) policy a high priority considering its attractiveness for emission and air pollution reduction to maintain the sustainability of Bali’s nature and tourism sector. Considering the uniqueness of the tourism sector in Bali and the mobility it generates, this study aims to investigate the factors that influence EV use by tourists based on several scenarios for estimating EV share target and the emission reduction contributed. For those purposes, the stated choice questionnaire was distributed online and offline to tourists in Bali and analyzed using the multinomial logit (MNL) model. While the study done during pandemic times, where the number of the tourist is significantly decreasing and the travel behavior influenced by mobility restriction imposed by the government, the data collection still covered mobility of both international and domestic tourist. The survey found that rental cost and accessibility, as well as the quality of charging stations are factors that affect EV use by tourists. Motorcycle parking cost was also found to influence EV use. These findings align with previous studies, and interventions such as fiscal incentives for rental companies and infrastructure development are suggested similar to EV incentives implemented in China, India, or the US. The development of the low emission zone (LEZ) is also proposed to manage parking fares similar to what was implemented in London, specifically to push the shift from internal combustion engine (ICE) to EV. Based on emission inventory calculation, 1.9 million kg of potential annual CO2 can be prevented with the implementation of these policies by the government.

Suburban development patterns, flexible work hours, and increasing participation in out-of-home activities are making the travel patterns of individuals more complex, and complex trip chaining could be a major barrier to the shift from drive-alone to public transport. This study introduces a cohort-based approach to analyse trip tour behaviors, in order to better understand and model their relationships to socio-demographics, trip attributes, and land use patterns. Specifically, it employs worker population cohorts with homogenous activity patterns to explore differences and similarities in tour frequency, trip chaining, and tour mode choices, all of which are required for travel demand modeling. The paper shows how modeling of these important tour variables may be improved, for integration into an activity-based modeling framework. Using data from the Space–Time Activity Research (STAR) survey for Halifax, Canada, five clusters of workers were identified from their activity travel patterns. These were labeled as extended workers, 8 to 4 workers, shorter work-day workers, 7 to 3 workers, and 9 to 5 workers. The number of home-based tours per day for all clusters were modeled using a Poisson regression model. Trip chaining was then modeled using an Ordered Probit model, and tour mode choice was modeled using a Multinomial logit (MNL) model. Statistical analysis showed that socio-demographic characteristics and tour attributes are significant predictors of travel behavior, consistent with existing literature. Urban form characteristics also have a significant influence on non-workers’ travel behavior and tour complexity. The findings of this study will assist in the future evaluation of transportation projects, and in land-use policymaking.

A comparative study is carried out to investigate the differences among conventional vehicles (CVs), battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) in the maximum acceptable time of diverting to a refuelling station, maximum acceptable time of queueing at a refuelling station, refuelling modes and desirable electric driving ranges, using Beijing, China, as a case study. Here, several multinomial logit (MNL) models are developed to relate the diverting and waiting times to individual attributes. The results suggest that, (1) the diverting time roughly follows a normal distribution for both CVs and electric vehicles (EVs), but the difference between them is slight; (2) EVs tend to bear longer waiting time above 10 min; (3) the MNL models indicate that income and the level of education tend to be more statistically significant to both the diverting and waiting times; (4) the most preferred driving ranges obtained for BEVs and PHEVs are both around 50 km, indicating that EV drivers may just prefer to charge for a specific time ranging from 8 to 10 min. Finally, ways to apply the empirical findings in planning refuelling and charging stations are discussed with specific examples.

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies.

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

This paper provides an analysis of multinomial logit model (MNL) on tourist destination choice. The choice set consisted of the Tropical North Queensland (TNQ), competed with the California Beach, the Cornwall Beach, and \"No Travel\" choice. The MNL model was used to identify the best scenario that gave the TNQ a market leadership in accordance with good budget performances. A scenario was developed by testing variable price combined with dummy variable of increasing family facilities; then testing the scenario based on the combination of willingness to pay (WTP) and elasticity analysis. Findings reported the best scenario was a combination of policy on the price and family facilities which made TNQ lead the markets and gave the highest revenues. Furthermore, the elasticity analysis was conducted to understand the impact of policy changes on the model outputs. The result showed that the market share and revenue of TNQ under the chosen scenario still exceed the performances of other scenarios. It was worth noting that these additional revenues raised from the chosen scenario ($71.662) cannot exceed the cost for family facility improvements; otherwise there was no policy visible than \"do nothing\" scenario.

Property market dynamics depend on changes in long run equilibrium and on impediments to adjustment towards equilibrium. Mortgage termination due to mobility, default and refinancing provides a lens for evaluating property market adjustments. The borrower’s decision to move as an adjustment mechanism in the property market is associated with utility-maximizing decisions to either prepay or default on the mortgage. The optimal choice between these two termination events may depend on unobserved propensities related to change in income, job location, or family size, and substantial inertial forces including search costs, neighborhood change and attachment to an area. We propose a method for modeling variables determining the impact of mobility on mortgage terminations with imperfect household and loan level data. Since omitted variables contribute to moving decisions and therefore to mortgage prepayment and default decisions, utility functions for sale and default are correlated through these unobservable variables; thus, the IIA assumption of the widely used Multinomial Logit Model (MNL) is violated. Under such circumstances, econometric theory suggests that the Nested Logit Model (NMNL) is a better choice, which obviates the limitation of MNL by allowing correlation in unobserved factors across alternatives. Using loan level micro data, we find empirical evidence showing significant correlation between sale and default due to omitted borrower mobility characteristics. Our simulations find that NMNL out performs MNL in out-of-sample prediction. Improved predictions of moves and defaults are applicable to micro and macro analysis of the housing market system.