scholarly journals Comparing the State-of-the-Art Efficient Stated Choice Designs Based on Empirical Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Li Tang ◽  
Xia Luo ◽  
Yang Cheng ◽  
Fei Yang ◽  
Bin Ran
Keyword(s):  
Discrete Choice ◽  
State Of The Art ◽  
Choice Model ◽  
Orthogonal Design ◽  
Asymptotic Variance ◽  
Parameter Estimates ◽  
Efficient Design ◽  
Stated Choice ◽  
Reliable Parameter ◽  
Efficient Data

The stated choice (SC) experiment has been generally regarded as an effective method for behavior analysis. Among all the SC experimental design methods, the orthogonal design has been most widely used since it is easy to understand and construct. However, in recent years, a stream of research has put emphasis on the so-called efficient experimental designs rather than keeping the orthogonality of the experiment, as the former is capable of producing more efficient data in the sense that more reliable parameter estimates can be achieved with an equal or lower sample size. This paper provides two state-of-the-art methods called optimal orthogonal choice (OOC) andD-efficient design. More statistically efficient data is expected to be obtained by either maximizing attribute level differences, or minimizing theD-error, a statistic corresponding to the asymptotic variance-covariance (AVC) matrix of the discrete choice model, when using these two methods, respectively. Since comparison and validation in the field of these methods are rarely seen, an empirical study is presented.D-error is chosen as the measure of efficiency. The result shows that both OOC andD-efficient design are more efficient. At last, strength and weakness of orthogonal, OOC, andD-efficient design are summarized.

scholarly journals An Empirical Study of Parameter Estimation for Stated Preference Experimental Design

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Fei Yang ◽  
Lin Chen ◽  
Yang Cheng ◽  
Xia Luo ◽  
Bin Ran
Keyword(s):  
Experimental Design ◽  
Choice Behavior ◽  
Arrival Time ◽  
Choice Model ◽  
Stated Preference ◽  
Orthogonal Design ◽  
Parameters Estimation ◽  
Efficient Design ◽  
Travel Choice ◽  
Better Than

The stated preference experimental design can affect the reliability of the parameters estimation in discrete choice model. Some scholars have proposed some new experimental designs, such as D-efficient, Bayesian D-efficient. But insufficient empirical research has been conducted on the effectiveness of these new designs and there has been little comparative analysis of the new designs against the traditional designs. In this paper, a new metro connecting Chengdu and its satellite cities is taken as the research subject to demonstrate the validity of the D-efficient and Bayesian D-efficient design. Comparisons between these new designs and orthogonal design were made by the fit of model and standard deviation of parameters estimation; then the best model result is obtained to analyze the travel choice behavior. The results indicate that Bayesian D-efficient design works better than D-efficient design. Some of the variables can affect significantly the choice behavior of people, including the waiting time and arrival time. The D-efficient and Bayesian D-efficient design for MNL can acquire reliability result in ML model, but the ML model cannot develop the theory advantages of these two designs. Finally, the metro can handle over 40% passengers flow if the metro will be operated in the future.

scholarly journals Forecasting jobs location choices by Discrete Choice Models: A sensitivity analysis to scale and implications for LUTI models

REGION ◽  
2015 ◽  
Vol 2 (1) ◽  
pp. 67 ◽  
Author(s):  
Jonathan Jones ◽  
Isabelle Thomas ◽  
Dominique Peeters
Keyword(s):  
Land Use ◽  
Discrete Choice ◽  
Land Use Planning ◽  
Choice Model ◽  
Discrete Choice Models ◽  
Parameter Estimates ◽  
Location Choices ◽  
Urban Context ◽  
Household Location ◽  
New Jobs

This paper proposes an empirical analysis of the sensitivity of Discrete Choice Model (DCM) to the size of the spatial units used as choice set (which relates to the well-known Modifiable Areal Unit Problem). Job's location choices in Brussels (Belgium) are used as the case study. DCMs are implemented within different Land Use and Transport Interactions (LUTI) models (UrbanSim, ILUTE) to forecast jobs or household location choices. Nevertheless, no studies have assessed their sensitivity to the size of the Basic Spatial Units (BSU) in an urban context. The results show significant differences in parameter estimates between BSUs. Assuming that new jobs are distributed among the study area proportionally to the utility level predicted by the DCM for each BSU (as in a LUTI model), it is also demonstrated that the spatial distribution of these new jobs varies with the size of the BSUs. These findings mean that the scale of the BSU used in the model can influence the output of a LUTI model relying on DCM to forecast location choices of agents and, therefore, have important operational implications for land-use planning.

Convex Optimization for Bundle Size Pricing Problem

2021 ◽  
Author(s):  
Xiaobo Li ◽  
Hailong Sun ◽  
Chung Piaw Teo
Keyword(s):  
Convex Optimization ◽  
Order Statistics ◽  
Covariance Matrix ◽  
Discrete Choice ◽  
State Of The Art ◽  
Choice Model ◽  
Partial Sums ◽  
Second Moments ◽  
Size Number ◽  
Bundle Size

We study the bundle size pricing (BSP) problem in which a monopolist sells bundles of products to customers and the price of each bundle depends only on the size (number of items) of the bundle. Although this pricing mechanism is attractive in practice, finding optimal bundle prices is difficult because it involves characterizing distributions of the maximum partial sums of order statistics. In this paper, we propose to solve the BSP problem under a discrete choice model using only the first and second moments of customer valuations. Correlations between valuations of bundles are captured by the covariance matrix. We show that the BSP problem under this model is convex and can be efficiently solved using off-the-shelf solvers. Our approach is flexible in optimizing prices for any given bundle size. Numerical results show that it performs very well compared with state-of-the-art heuristics. This provides a unified and efficient approach to solve the BSP problem under various distributions and dimensions. This paper was accepted by David Simchi-Levi, revenue management and market analytics.

Efficient Versus Non-efficient Stated Choice Designs: A Comparison in a Mode Choice Context

2012 ◽  
pp. 31-49
Author(s):  
Concepciňn Romŕn ◽  
Juan Carlos Martěn ◽  
Raquel Espino ◽  
Ana Isabel Arencibia
Keyword(s):  
High Speed ◽  
Parameter Estimates ◽  
High Speed Train ◽  
Efficient Design ◽  
Stated Choice ◽  
Efficiency Gains ◽  
Significance Level ◽  
Choice Context ◽  
Modelling Strategies ◽  
Empirical Experiment

This paper evaluates efficiency gains produced by efficient designs to analyze Stated Choice (SC) data. Based on an empirical experiment, conducted in the context of the choice between taking the plane or the new high speed train on the Madrid-Barcelona route, we compare the performance of this design with that of the efficient design obtained through minimization of the D-error, considering different modelling strategies. The results of the analysis demonstrate that substantial gains in the significance level of the parameter estimates could have been attained if the efficient design had been used to analyze SC data.

The effects of motivations to go to the country on rural accommodation choice: A hybrid discrete choice model

2020 ◽  
pp. 135481662091206
Author(s):  
Isabel P Albaladejo ◽  
M Teresa Díaz-Delfa
Keyword(s):  
Discrete Choice ◽  
Consumer Choice ◽  
Latent Variables ◽  
Choice Model ◽  
Multinomial Logit Model ◽  
Discrete Choice Models ◽  
Stated Choice ◽  
Choice Process ◽  
Explanatory Variables ◽  
Multiple Indicator

Based on the theory of constructive consumer choice process, we propose that the rural accommodation choice process depends on motivationals of tourists to go to the country. Discrete choice models have frequently been used to explain and predict choices from a set of finite alternatives, such as the choice of accommodation, but using only cognitive attributes as explanatory variables. The hybrid discrete choice (HDC) model also allows us to take into account unobservable or latent variables, like the motivations, and incorporate them through a multiple indicator multiple cause (MIMIC) model. Data collected in Murcia (Spain) from a stated choice survey are used to estimate a multinomial logit model and two specifications of the HDC model. Our results find that motivations affect the probability of accommodation rural choice. Furthermore, the effect of the motivations is different depending on the attributes of the accommodation.

scholarly journals Comparing the efficiency and robustness of state-of-the-art experimental designs for stated choice modeling: A simulation analysis

2017 ◽  
Vol 9 (2) ◽  
pp. 168781401769189 ◽  
Author(s):  
Hai Zhu ◽  
Xia Luo ◽  
Yanjin Li ◽  
Ying Zhu ◽  
Qian Huang
Keyword(s):  
Simulation Analysis ◽  
State Of The Art ◽  
Orthogonal Design ◽  
Uniform Design ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Design Methods ◽  
Experimental Designs ◽  
Efficient Design ◽  
Design Efficiency

Among the ways to construct experimental designs having been proposed, orthogonal design, uniform design, and D-efficient design are state-of-the-art methods. This article provides detailed comparisons on the efficiency and robustness among these methods with three case studies in multinomial logit and mixed multinomial logit models. ND-error values and the departures of D-errors corresponding to misspecification of prior information are used as measurements of design efficiency and design robustness, respectively. Design methods are described, and designs with various numbers of runs are constructed. The results indicate that (a) when parameter priors are available, D-efficient design method outperforms the other two methods, in terms of design efficiency, while uniform design and orthogonal design methods are comparable with each other; (b) there will be efficiency loss when D-efficient design that constructed for specific model is implemented in other ones; (c) all three methods have comparable robustness against misspecifications in parameter prior values; however, the effect of misspecification in prior distribution is massive when D-efficient design is used in mixed multinomial logit model; and (d) when parameter priors are unknown, uniform design is suggested to be used in the construction of experimental designs.

Are More Profiles Better Than Fewer?: Searching for Parsimony and Relevance in Stated Choice Experiments

2000 ◽  
Vol 1719 (1) ◽  
pp. 165-174 ◽  
Author(s):  
Peter R. Stopher ◽  
David A. Hensher
Keyword(s):  
New Zealand ◽  
Degrees Of Freedom ◽  
Choice Model ◽  
Fractional Factorial ◽  
Sources Of Information ◽  
Stated Choice ◽  
Complex Design ◽  
Data Collection Instrument ◽  
Main Effects ◽  
Better Than

Transportation planners increasingly include a stated choice (SC) experiment as part of the armory of empirical sources of information on how individuals respond to current and potential travel contexts. The accumulated experience with SC data has been heavily conditioned on analyst prejudices about the acceptable complexity of the data collection instrument, especially the number of profiles (or treatments) given to each sampled individual (and the number of attributes and alternatives to be processed). It is not uncommon for transport demand modelers to impose stringent limitations on the complexity of an SC experiment. A review of the marketing and transport literature suggests that little is known about the basis for rejecting complex designs or accepting simple designs. Although more complex designs provide the analyst with increasing degrees of freedom in the estimation of models, facilitating nonlinearity in main effects and independent two-way interactions, it is not clear what the overall behavioral gains are in increasing the number of treatments. A complex design is developed as the basis for a stated choice study, producing a fractional factorial of 32 rows. The fraction is then truncated by administering 4, 8, 16, 24, and 32 profiles to a sample of 166 individuals (producing 1, 016 treatments) in Australia and New Zealand faced with the decision to fly (or not to fly) between Australia and New Zealand by either Qantas or Ansett under alternative fare regimes. Statistical comparisons of elasticities (an appropriate behavioral basis for comparisons) suggest that the empirical gains within the context of a linear specification of the utility expression associated with each alternative in a discrete choice model may be quite marginal.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

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