Joint Discrete-Continuous Model of Travel Mode and Departure Time Choices

2017 ◽  
Vol 2669 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Ramin Shabanpour ◽  
Nima Golshani ◽  
Sybil Derrible ◽  
Abolfazl (Kouros) Mohammadian ◽  
Mohammad Miralinaghi
Keyword(s):  
Decision Making ◽  
Goodness Of Fit ◽  
Clustering Algorithm ◽  
Joint Modeling ◽  
Continuous Model ◽  
Joint Model ◽  
Travel Mode ◽  
Joint Decision ◽  
Departure Time ◽  
Decision Making Behavior

This paper presents a cluster-based joint modeling approach to investigating heterogeneous travelers’ behavior toward trip mode and departure time choices by considering those choices as a joint decision. First, a two-step clustering algorithm was applied to classify travelers into six distinct clusters to account for the heterogeneity in their decision-making behavior. Then, a joint discrete-continuous model was proposed for each cluster, in which the travel mode and departure time were estimated by a multinomial logit and a log-linear regression model, respectively. These two models were jointly estimated with a copula approach. For an investigation of the performance of the proposed approach, its results were compared with an aggregate joint model on all nonclustered observations to assess the potential benefits of population clustering. The goodness-of-fit measures and prediction accuracy results demonstrated that the proposed cluster-based joint model significantly outperformed the aggregate joint model. Further, the variations in the estimated parameters of different clusters indicated significant behavioral differences across clusters. Hence, the proposed cluster-based joint model, while offering higher accuracy, possesses a significant potential for transportation policy making because it has the capability to target different types of travelers on the basis of their decision-making behavior.

scholarly journals Decision-Making Behavior and Risk Perception of Chinese Female Wildlife Tourists

2021 ◽  
Vol 13 (18) ◽  
pp. 10301
Author(s):  
Li Cong ◽  
Qiqi Wang ◽  
Geoffrey Wall ◽  
Yijing Su
Keyword(s):  
Decision Making ◽  
Risk Perception ◽  
Risk Perceptions ◽  
Western China ◽  
Travel Mode ◽  
Security Assurance ◽  
Wildlife Tourism ◽  
Consumption Decisions ◽  
Destination Selection ◽  
Decision Making Behavior

Prior to the global pandemic, wildlife tourism was increasing rapidly globally but was in the early stages of development in China, where it faces great challenges and opportunities. Women comprise a substantial proportion of the market but their decision-making behavior and their perceptions of risk in wildlife tourism have not yet been explored. This paper explores relationships between risk perception and decision-making in tourism. A survey of female tourists was undertaken at non-captive and semi-captive wildlife sites in western China, as well as through internet website posting, resulting in 415 completed questionnaires. Quantitative methods were used to examine four sequential stages of decision-making in wildlife tourism: destination selection, trip itinerary, travel mode and security assurance, and entertainment consumption. Three dimensions of risk perception in wildlife tourism were identified: physical safety, personal comfort, and quality of experience. Decision-making behavior and risk perceptions are related. Perceived risks greatly impact tourists’ travel mode and security assurance decisions. The higher the perceived risk, the greater the likelihood of female tourists participating in decisions on destination selection, travel methods and other entertainment activities undertaken on their wildlife tourism trips. Concerns regarding personal comfort positively influence destination selection, the trip itinerary, and recreation and consumption decisions. Assurance of acquiring a quality experience influences entertainment consumption decisions. The study contributes to the understanding of risk, decision-making behavior and gender research, and confirms the practical importance of safety considerations at wildlife destinations.

Joint Choice Model for Airport Passengers’ Travel Mode and Departure Time Based on Agent Theory

2020 ◽  
Vol 2674 (6) ◽  
pp. 177-187
Author(s):  
Danwen Bao ◽  
Tianxuan Zhang ◽  
Shijia Tian ◽  
Zhiwei Di
Keyword(s):  
Decision Making ◽  
Public Transportation ◽  
Travel Behavior ◽  
Time Management ◽  
Imperfect Information ◽  
Bayesian Learning ◽  
Choice Model ◽  
Travel Mode ◽  
Agent Theory ◽  
Departure Time

Numerous strategies have been proposed to modify and transform passengers’ travel mode and departure time with the purpose of mitigating landside traffic pressure of airports. A core solution to tackle this problem is to build a travel behavior model so that pertinent predictions about the extent to which passengers shift their patterns of travel can hopefully be obtained. This paper aims at studying the passengers’ behaviors with respect to the travel mode and departure time based on agent theory. What distinguishes this model from traditional utility maximization theory is that it specifically places emphasis on the decision-making process with imperfect information and bounded rationality. Passengers continuously renew their knowledge of time management and their surrounding environment in the duration of the Bayesian learning process. It is evident that decisions about whether to substitute their current travel mode and departure time will be given thoughtful consideration before traveling, in relation to their presumptive gain and cost for searching. When performing additional searches, passengers tend to depend on a range of decision-making conditions to determine the necessity of converting to a new travel pattern. The process of both searching and deciding can be indicated by production (if–then) rules. These rules basically stem from the data gathered from Nanjing Lukou International Airport (NKG). Furthermore, this paper studies and discusses to what extent passengers will change their travel behaviors under variable costs of public transportation. Finally, this paper provides some recommendations on how to formulate appropriate subway fares.

scholarly journals Conversations About Death and Dying, End-of-Life Care Plans and Preferences Between Aging Parents and Adult Children

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 417-417
Author(s):  
Hyo Jung Lee ◽  
Jacobbina Jin Wen Ng
Keyword(s):  
Decision Making ◽  
End Of Life ◽  
Adult Children ◽  
End Of Life Care ◽  
Death And Dying ◽  
Family Members ◽  
Life Care ◽  
Joint Decision ◽  
Aging Parents ◽  
Care Plans

Abstract This study aims to investigate whether attitude and perception on late-life death and dying, end-of-life care plans and preferences could be better understood from current values shared between aging parents and their adult children in the multi-cultural city-bound country, Singapore. We are in the process of interviewing 20 aging parent-adult child dyads. Up to date, six semi-structured interviews were completed and transcribed. We performed Content analysis to analyze the transcripts. Preliminary findings showed that both aging parents and adult children rarely discussed this issue, although parents had their own plans or preferences. The major barriers against open conversations about death and dying of aging parents include: the perception of not-yet time to talk about this issue (without knowing when the right time is) and tendency to have conversations about death in tandem with finances, but not death itself. Although specific end-of-life care plans or arrangements were not thought out thoroughly, aging parents expressed a high level of trust and reliance on close family members’ decisions regarding their end-of-life care. They tended to agree on joint decision-making process within family, even though adult children had no or unmatched ideas about their aging parents’ end-of-life wishes. This did not necessarily align with previous findings in Western countries, underscoring individuals’ control over their own death and dying process. Open conversation within family, family-involved advance care planning, or joint decision-making processes may be warranted to promote quality of life and death in older Singaporeans and well-being of their family members of all ages.

scholarly journals Coupling Fuzzy Multi-Criteria Decision-Making and Clustering Algorithm for MSW Landfill Site Selection (Case Study: Lanzhou, China)

2021 ◽  
Vol 10 (6) ◽  
pp. 403
Author(s):  
Jiamin Liu ◽  
Yueshi Li ◽  
Bin Xiao ◽  
Jizong Jiao
Keyword(s):  
Decision Making ◽  
Clustering Algorithm ◽  
Fuzzy Membership Function ◽  
Multi Criteria Decision Making ◽  
Landfill Site Selection ◽  
Spatial Distribution Characteristics ◽  
Criteria Weights ◽  
Spatial Decision Making ◽  
Complex Decision

The siting of Municipal Solid Waste (MSW) landfills is a complex decision process. Existing siting methods utilize expert scores to determine criteria weights, however, they ignore the uncertainty of data and criterion weights and the efficacy of results. In this study, a coupled fuzzy Multi-Criteria Decision-Making (MCDM) approach was employed to site landfills in Lanzhou, a semi-arid valley basin city in China, to enhance the spatial decision-making process. Primarily, 21 criteria were identified in five groups through the Delphi method at 30 m resolution, then criteria weights were obtained by DEMATEL and ANP, and the optimal fuzzy membership function was determined for each evaluation criterion. Combined with GIS spatial analysis and the clustering algorithm, candidate sites that satisfied the landfill conditions were identified, and the spatial distribution characteristics were analyzed. These sites were subsequently ranked utilizing the MOORA, WASPAS, COPRAS, and TOPSIS methods to verify the reliability of the results by conducting sensitivity analysis. This study is different from the previous research that applied the MCDM approach in that fuzzy MCDM for weighting criteria is more reliable compared to the other common methods.

scholarly journals Survival or Mortality: Does Risk Attribute Framing Influence Decision-Making Behavior in a Discrete Choice Experiment?

2016 ◽  
Vol 19 (2) ◽  
pp. 202-209 ◽  
Author(s):  
Jorien Veldwijk ◽  
Brigitte A.B. Essers ◽  
Mattijs S. Lambooij ◽  
Carmen D. Dirksen ◽  
Henriette A. Smit ◽  
...  
Keyword(s):  
Decision Making ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Decision Making Behavior

scholarly journals Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran)

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
T. Mesbahzadeh ◽  
M. M. Miglietta ◽  
M. Mirakbari ◽  
F. Soleimani Sardoo ◽  
M. Abdolhoseini
Keyword(s):  
Climate Change ◽  
Goodness Of Fit ◽  
Joint Modeling ◽  
Copula Function ◽  
Gaussian Copula ◽  
Climate Change Scenarios ◽  
Climatic Parameters ◽  
Goodness Of Fit Test ◽  
Copula Theory ◽  
Precipitation And Temperature

Precipitation and temperature are very important climatic parameters as their changes may affect life conditions. Therefore, predicting temporal trends of precipitation and temperature is very useful for societal and urban planning. In this research, in order to study the future trends in precipitation and temperature, we have applied scenarios of the fifth assessment report of IPCC. The results suggest that both parameters will be increasing in the studied area (Iran) in future. Since there is interdependence between these two climatic parameters, the independent analysis of the two fields will generate errors in the interpretation of model simulations. Therefore, in this study, copula theory was used for joint modeling of precipitation and temperature under climate change scenarios. By the joint distribution, we can find the structure of interdependence of precipitation and temperature in current and future under climate change conditions, which can assist in the risk assessment of extreme hydrological and meteorological events. Based on the results of goodness of fit test, the Frank copula function was selected for modeling of recorded and constructed data under RCP2.6 scenario and the Gaussian copula function was used for joint modeling of the constructed data under the RCP4.5 and RCP8.5 scenarios.

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