6,794 research outputs found
Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach
This study presents a semi-nonparametric Latent Class Choice Model (LCCM)
with a flexible class membership component. The proposed model formulates the
latent classes using mixture models as an alternative approach to the
traditional random utility specification with the aim of comparing the two
approaches on various measures including prediction accuracy and representation
of heterogeneity in the choice process. Mixture models are parametric
model-based clustering techniques that have been widely used in areas such as
machine learning, data mining and patter recognition for clustering and
classification problems. An Expectation-Maximization (EM) algorithm is derived
for the estimation of the proposed model. Using two different case studies on
travel mode choice behavior, the proposed model is compared to traditional
discrete choice models on the basis of parameter estimates' signs, value of
time, statistical goodness-of-fit measures, and cross-validation tests. Results
show that mixture models improve the overall performance of latent class choice
models by providing better out-of-sample prediction accuracy in addition to
better representations of heterogeneity without weakening the behavioral and
economic interpretability of the choice models
Joint prediction of travel mode choice and purpose from travel surveys: A multitask deep learning approach
The prediction and behavioural analysis of travel mode choice and purpose are critical for transport planning and have attracted increasing interest in research. Traditionally, the prediction of travel mode choice and trip purpose has been tackled separately, which fail to fully leverage the shared information between travel mode and purpose. This study addresses this gap by proposing a multitask learning deep neural network framework (MTLDNN) to jointly predict mode choice and purpose. We empirically evaluate and validate this framework using the household travel survey data in Greater London, UK. The results show that this framework has significantly lower cross-entropy loss than multinomial logit models (MNL) and single-task-learning deep neural network models (STLDNN). On the other hand, the predictive accuracy of MTLDNN is similar to STLDNN and is significantly higher than MNL. Moreover, in terms of behaviour analysis, the substitution pattern and choice probability of MTLDNN regarding input variables largely agree with MNL and STLDNN. This work demonstrates that MTLDNN is efficient in utilising the information shared by travel mode choice and purpose, and is capable of producing behaviourally reasonable substitution patterns across travel modes. Future research would develop more advanced MTLDNN frameworks for travel behaviour analysis and generalise MTLDNN to other travel behaviour topics
- Case of next-generation transportation market -
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μ.The present dissertation aims to provide insights into the application of different artificial neural network models in the analysis of consumer choice regarding next-generation transportation services (NGT). It categorizes consumers decisions regarding the adoption of new services according to Deweys buyer decision process and then analyzes these decisions using a variety of different methods. In particular, various artificial neural network (ANN) models are applied to predict consumers intentions. Also, the dissertation proposes an attention-based ANN model that identifies the key features that affect consumers choices. Consumers preferences for different types of NGT services are analyzed using a hierarchical Bayesian model. The analyzed consumer preferences are utilized to forecast demand for NGT services, evaluate government policies within the transportation market, and provide evidence regarding the social conflicts among traditional and new transportation services. The dissertation uses the Multiple Discrete-Continuous Extreme Value (MDCEV) model to analyze consumers decisions regarding the use of different transportation modes. It also utilizes this MDCEV model analysis to estimate the effect of NGT services on consumers travel mode selection behavior and the environmental effects of the transportation sector. Finally, the findings of the dissertations analyses are combined to generate marketing and policy insights that will promote NGT services in Korea.λ³Έ μ°κ΅¬λ κΈ°κ³νμ΅ κΈ°λ°μ μΈκ³΅μ§λ₯λ§κ³Ό κΈ°μ‘΄μ ν΅κ³μ λ§μΌν
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μ νλͺ¨νμ΄ κ²°ν©λ κ²½μ° μλΉμλ€μ μ ν μ ν νμλΏλ§ μλλΌ, μ ν μ ν μμ¬κ²°μ κ³Όμ μ λ°μ κ±Έμ³ μλΉμ μ νΈλ₯Ό ν¬κ΄μ μΌλ‘ λΆμν μ μμμ νμΈνμλ€.Chapter 1. Introduction 1
1.1 Research Background 1
1.2 Research Objective 7
1.3 Research Outline 12
Chapter 2. Literature Review 14
2.1 Product and Technology Diffusion Theory 14
2.1.1. Extension of Adoption Models 19
2.2 Artificial Neural Network 22
2.2.1 General Component of the Artificial Neural Network 22
2.2.2 Activation Functions of Artificial Neural Network 26
2.3 Modeling Consumer Choice: Discrete Choice Model 32
2.3.1 Multinomial Logit Model 32
2.3.2 Mixed Logit Model 34
2.3.3 Latent Class Model 37
2.4 Modeling Consumer Heuristics in Discrete Choice Model 39
2.4.1 Consumer Decision Rule in Discrete Choice Model: Compensatory and Non-Compensatory Models 39
2.4.2 Choice Set Formation Behaviors: Semi-Compensatory Models 42
2.4.3 Modeling Consumer Usage: MDCEV Model 50
2.5 Difference between Artificial Neural Network and Choice Modeling 53
2.6 Limitations of Previous Studies and Research Motivation 58
Chapter 3. Methodology 63
3.1 Artificial Neural Network Models for Prediction 63
3.1.1 Multiple Perceptron Model 63
3.1.2 Convolutional Neural Network 69
3.1.3 Bayesian Neural Network 72
3.2 Feature Identification Model through Attention 77
3.3 Hierarchical Bayesian Model 83
3.4 Multiple Discrete-Continuous Extreme Value Model 86
Chapter 4. Empirical Analysis: Consumer Preference and Selection of Transportation Mode 98
4.1 Empirical Analysis Framework 98
4.2 Data 101
4.2.1 Overview of the Survey 101
4.3 Empirical Study I: Consumer Intention to New Type of Transportation 110
4.3.1 Research Motivation and Goal 110
4.3.2 Data and Model Setup 114
4.3.3 Result and Discussion 123
4.4 Empirical Study II: Consumer Choice and Preference for New Types of Transportation 142
4.4.1 Research Motivation and Goal 142
4.4.2 Data and Model Setup 144
4.4.3 Result and Discussion 149
4.5 Empirical Study III: Impact of New Transportation Mode on Consumers Travel Behavior 163
4.5.1 Research Motivation and Goal 163
4.5.2 Data and Model Setup 164
4.5.3 Result and Discussion 166
Chapter 5. Discussion 182
Bibliography 187
Appendix: Survey used in the analysis 209
Abstract (Korean) 241Docto
Evaluating car-sharing switching rates from traditional transport means through logit models and Random Forest classifiers
Positive impacts of car-sharing, such as reductions in car ownership, congestion, vehicle-miles-traveled and greenhouse gas emissions, have been extensively analyzed. However, these benefits are not fully effective if car-sharing subtracts travel demand from existing sustainable modes. This paper evaluates substitution rates of car-sharing against private cars and public transport using a Random Forest classifier and Binomial Logit model. The models were calibrated and validated using a stated-preference travel survey and applied to a revealed-preference survey, both administered to a representative sample of the population living in Turin (Italy). Results of the two models show that the predictive power of both models is comparable, albeit the Logit model tends to estimate predictions with a higher reliability and the Random Forest model produces higher positive switches towards car-sharing. However, results from both models suggest that the substitution rate of private cars is, on average, almost five times that of public transport
Causation versus Prediction: Comparing Causal Discovery and Inference with Artificial Neural Networks in Travel Mode Choice Modeling
This study compares the performance of a causal and a predictive model in
modeling travel mode choice in three neighborhoods in Chicago. A causal
discovery algorithm and a causal inference technique were used to extract the
causal relationships in the mode choice decision making process and to estimate
the quantitative causal effects between the variables both directly from
observational data. The model results reveal that trip distance and vehicle
ownership are the direct causes of mode choice in the three neighborhoods.
Artificial neural network models were estimated to predict mode choice. Their
accuracy was over 70%, and the SHAP values obtained measure the importance of
each variable. We find that both the causal and predictive modeling approaches
are useful for the purpose they serve. We also note that the study of mode
choice behavior through causal modeling is mostly unexplored, yet it could
transform our understanding of the mode choice behavior. Further research is
needed to realize the full potential of these techniques in modeling mode
choice
Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models
Discrete choice models (DCM) are widely employed in travel demand analysis as
a powerful theoretical econometric framework for understanding and predicting
choice behaviors. DCMs are formed as random utility models (RUM), with their
key advantage of interpretability. However, a core requirement for the
estimation of these models is a priori specification of the associated utility
functions, making them sensitive to modelers' subjective beliefs. Recently,
machine learning (ML) approaches have emerged as a promising avenue for
learning unobserved non-linear relationships in DCMs. However, ML models are
considered "black box" and may not correspond with expected relationships. This
paper proposes a framework that expands the potential of data-driven approaches
for DCM by supporting the development of interpretable models that incorporate
domain knowledge and prior beliefs through constraints. The proposed framework
includes pseudo data samples that represent required relationships and a loss
function that measures their fulfillment, along with observed data, for model
training. The developed framework aims to improve model interpretability by
combining ML's specification flexibility with econometrics and interpretable
behavioral analysis. A case study demonstrates the potential of this framework
for discrete choice analysis
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