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.

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.

scholarly journals Mode choice analysis using discrete choice model from transport user (Case study: Jakarta LRT, Indonesia)

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari
Keyword(s):  
Discrete Choice ◽  
Public Transportation ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Choice Analysis ◽  
Mode Choice Model

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.

scholarly journals A DISCRETE CHOICE MODEL FOR FIRM LOCATION DECISION

2016 ◽  
Vol 14 (4) ◽  
Author(s):  
Noordini Che Man ◽  
Harry Timmerman
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Central Business District ◽  
Statistical Technique ◽  
Discrete Choice Models ◽  
Location Decisions ◽  
Firm Location ◽  
Suitability Analysis ◽  
Choice Data ◽  
Business District

Where to locate? It is one of the most important question in locating a business in a city. In the city center, business or firms are functioning as a dominant attractor of employment and also employment locations which linked the land use and transportation system. The objective of this paper is to describe the location model of firms in Kuala Lumpur area. Two important determinants of location choice model in this study are the accessibility measures and the suitability analysis indicators. The model focuses on the statistical technique for analyzing discrete choice data by using econometric and Geographic Information System software. The findings in this paper show that agriculture, mining, electricity, gas and water, transport and finance firms' type are mostly located outside of Kuala Lumpur's Central Business District area. Meanwhile, manufacturing, construction and wholesale firms' type are located in the Central Business District area. The result of this study will highlight the use of discrete choice models in the analysis of firm location decisions which will be a foundation to facilitate town planners and decision makers to understand the firm location decisions in their region.

scholarly journals Measuring hospital spatial wingspan by using a discrete choice model with utility-threshold

2018 ◽  
Author(s):  
Saley Issa ◽  
Ribatet Mathieu ◽  
Molinari Nicolas
Keyword(s):  
Monte Carlo ◽  
Discrete Choice ◽  
Choice Model ◽  
Discrete Choice Models ◽  
Mcmc Methods ◽  
Hospital Competition ◽  
Policy Makers ◽  
New Approach ◽  
Asthma Patients ◽  
The One

AbstractPolicy makers increasingly rely on hospital competition to incentivize patients to choose high-value care. Travel distance is one of the most important drivers of patients’ decision. The paper presents a method to numerically measure, for a given hospital, the distance beyond which no patient is expected to choose the hospital for treatment by using a new approach in discrete choice models. To illustrate, we compared 3 hospitals attractiveness related to this distance for asthma patients admissions in 2009 in Hérault (France), showing, as expected, CHU Montpellier is the one with the most important spatial wingspan. For estimation, Monte Carlo Markov Chain (MCMC) methods are used.

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.

Pricing Competition Under Specific Discrete Choice Models

2020 ◽  
Vol 37 (02) ◽  
pp. 2050008
Author(s):  
Farhad Etebari
Keyword(s):  
Discrete Choice ◽  
Logit Model ◽  
Choice Model ◽  
Public Information ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Discrete Choice Models ◽  
Nested Logit ◽  
Nested Logit Model ◽  
Substitution Pattern

Recent developments of information technology have increased market’s competitive pressure and products’ prices turned to be paramount factor for customers’ choices. These challenges influence traditional revenue management models and force them to shift from quantity-based to price-based techniques and incorporate individuals’ decisions within optimization models during pricing process. Multinomial logit model is the simplest and most popular discrete choice model, which suffers from an independence of irrelevant alternatives limitation. Empirical results demonstrate inadequacy of this model for capturing choice probability in the itinerary share models. The nested logit model, which appeared a few years after the multinomial logit, incorporates more realistic substitution pattern by relaxing this limitation. In this paper, a model of game theory is developed for two firms which customers choose according to the nested logit model. It is assumed that the real-time inventory levels of all firms are public information and the existence of Nash equilibrium is demonstrated. The firms adapt their prices by market conditions in this competition. The numerical experiments indicate decreasing firm’s price level simultaneously with increasing correlation among alternatives’ utilities error terms in the nests.

Discrete choice model of residential location in Krakow

2017 ◽  
Vol 10 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Michal Gluszak ◽  
Bartlomiej Marona
Keyword(s):  
Discrete Choice ◽  
Urban Economics ◽  
Choice Model ◽  
Stated Preference ◽  
Discrete Choice Model ◽  
Urban Housing ◽  
Location Choices ◽  
Housing Preferences ◽  
Content Type ◽  
Residence Location

Purpose This paper aims to discuss the link between socio-economic characteristics of house buyers and their housing location choices. The major objective of the study is an examination of the role of household socio-economic characteristics. The research addresses the importance of previous residence location and latent housing motives for intra-urban housing mobility. Design/methodology/approach The research examines housing preferences structure and analyzes housing location choices in the city of Krakow (Poland) using discrete choice model (conditional logit model). The research is based on stated preference data from Krakow. Findings The results of this study suggest that demand for housing alternatives is negatively linked to the distance from current residence. Other factors stay equal, the further the distance, the less likely a household is willing to choose a location within the metropolitan area. The study indicates that housing motives can help explain housing location decisions. Practical implications The paper provides an empirical assessment of housing decisions in Krakow, one of the major metropolitan areas in Poland. Originality/value The paper contributes to a better understanding of the nature of housing decision and housing preferences in emerging markets in Central and Eastern Europe. As a result, presented research helps to fill the gap in housing market and urban economics literature.

scholarly journals A DISCRETE CHOICE MODEL FOR FIRM LOCATION DECISION

2016 ◽  
Author(s):  
Noordini Che Man ◽  
Harry Timmerman
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Central Business District ◽  
Statistical Technique ◽  
Discrete Choice Models ◽  
Location Decisions ◽  
Firm Location ◽  
Suitability Analysis ◽  
Choice Data ◽  
Business District

Where to locate? It is one of the most important question in locating a business in a city. In the city center, business or firms are functioning as a dominant attractor of employment and also employment locations which linked the land use and transportation system. The objective of this paper is to describe the location model of firms in Kuala Lumpur area. Two important determinants of location choice model in this study are the accessibility measures and the suitability analysis indicators. The model focuses on the statistical technique for analyzing discrete choice data by using econometric and Geographic Information System software. The findings in this paper show that agriculture, mining, electricity, gas and water, transport and finance firms' type are mostly located outside of Kuala Lumpur's Central Business District area. Meanwhile, manufacturing, construction and wholesale firms' type are located in the Central Business District area. The result of this study will highlight the use of discrete choice models in the analysis of firm location decisions which will be a foundation to facilitate town planners and decision makers to understand the firm location decisions in their region.

Unobserved Heterogeneity in Dynamic Discrete Choice Models

1993 ◽  
Vol 25 (4) ◽  
pp. 495-519 ◽  
Author(s):  
S Reader
Keyword(s):  
Discrete Choice ◽  
Simulation Analysis ◽  
Choice Model ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Estimation Procedure ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Dynamic Discrete Choice ◽  
Cross Sectional

Monte Carlo simulation methods are used to confirm the identifiability of discrete choice models in which unobserved heterogeneity is specified as a random effect and modelled using the nonparametric mass-points approach. This simulation analysis is also used to examine alternative strategies for the estimation of such models by using a quasi-Newton maximum-likelihood estimation procedure, given the apparent sensitivity of model identification to choice of starting values. A mass-point model approach is then applied to a dataset of repeated choice involving household shopping trips between three types of retail centre, and the results from this approach are compared with those obtained from a conventional cross-sectional multinomial logit choice model as well as to results from a model in which a parametric distribution (the Dirichlet) is used to model the unobserved heterogeneity.

Sign in / Sign up

Export Citation Format

Share Document