3,985 research outputs found

    Tour-based Travel Mode Choice Estimation based on Data Mining and Fuzzy Techniques

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    This paper extends tour-based mode choice model, which mainly includes individual trip level interactions, to include linked travel modes of consecutive trips of an individual. Travel modes of consecutive trip made by an individual in a household have strong dependency or co-relation because individuals try to maintain their travel modes or use a few combinations of modes for current and subsequent trips. Traditionally, tour based mode choice models involved nested logit models derived from expert knowledge. There are limitations associated with this approach. Logit models assumes i) specific model structure (linear utility model) in advance; and, ii) it holds across an entire historical observations. These assumptions about the predefined model may be representative of reality, however these rules or heuristics for tour based mode choice should ideally be derived from the survey data rather than based on expert knowledge/ judgment. Therefore, in this paper, we propose a novel data-driven methodology to address the issues identified in tour based mode choice. The proposed methodology is tested using the Household Travel Survey (HTS) data of Sydney metropolitan area and its performances are compared with the state-of-the-art approaches in this area

    New perspectives on the performance of machine learning classifiers for mode choice prediction: An experimental review

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    It appears to be a commonly held belief that Machine Learning (ML) classification algorithms should achieve substantially higher predictive performance than manually specified Random Utility Models (RUMs) for choice modelling. This belief is supported by several papers in the mode choice literature, which highlight stand-out performance of non-linear ML classifiers compared with linear models. However, many studies which compare ML classifiers with linear models have a fundamental flaw in how they validate models on out-of-sample data. This paper investigates the implications of this issue by repeating the experiments of three past papers using two different sampling methods for panel data. The results indicate that using trip-wise sampling with travel diary data causes significant data leakage. Furthermore, the results demonstrate that this data leakage introduces substantial bias in model performance estimates, particularly for flexible non-linear classifiers. Grouped sampling is found to address the issues associated with trip-wise sampling and provides reliable estimates of true Out-Of-Sample (OOS) predictive performance. Whilst the results from this study indicate that there is a slight predictive performance advantage of non-linear classifiers over linear Logistic Regression (LR) models, this advantage is much more modest than has been suggested by previous investigations

    A Random Effect Bayesian Neural Network (RE-BNN) for travel mode choice analysis across multiple regions

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    Travel mode choice modelling plays a critical role in predicting passengers’ travel demand and planning local transportation systems. Researchers commonly adopt classical Random Utility Models to analyse individual decision-making based on the utility theory. Recently, with an increasing interest in applying Machine Learning techniques, a number of studies have used these methods for modelling travel mode preferences for their excellent predictive power. However, none of these studies proposes machine learning models that investigate the regional heterogeneity of travel behaviours. To address this gap, this study develops a Random Effect-Bayesian Neural Network (RE-BNN) framework to predict and explain travel mode choice across multiple regions by combining the Random Effect (RE) model and the Bayesian Neural Networks (BNN). The results show that this model outperforms the plain Deep Neural Network (DNN) regarding prediction accuracy and is more robust across different datasets. In addition, in terms of interpretation, the capability of RE-BNN to learn the travel behaviours across regions has been demonstrated by offset utilities, choice probability functions and local travel mode shares

    Novel Neural Network Applications to Mode Choice in Transportation: Estimating Value of Travel Time and Modelling Psycho-Attitudinal Factors

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    Whenever researchers wish to study the behaviour of individuals choosing among a set of alternatives, they usually rely on models based on the random utility theory, which postulates that the single individuals modify their behaviour so that they can maximise of their utility. These models, often identified as discrete choice models (DCMs), usually require the definition of the utilities for each alternative, by first identifying the variables influencing the decisions. Traditionally, DCMs focused on observable variables and treated users as optimizing tools with predetermined needs. However, such an approach is in contrast with the results from studies in social sciences which show that choice behaviour can be influenced by psychological factors such as attitudes and preferences. Recently there have been formulations of DCMs which include latent constructs for capturing the impact of subjective factors. These are called hybrid choice models or integrated choice and latent variable models (ICLV). However, DCMs are not exempt from issues, like, the fact that researchers have to choose the variables to include and their relations to define the utilities. This is probably one of the reasons which has recently lead to an influx of numerous studies using machine learning (ML) methods to study mode choice, in which researchers tried to find alternative methods to analyse travellers’ choice behaviour. A ML algorithm is any generic method that uses the data itself to understand and build a model, improving its performance the more it is allowed to learn. This means they do not require any a priori input or hypotheses on the structure and nature of the relationships between the several variables used as its inputs. ML models are usually considered black-box methods, but whenever researchers felt the need for interpretability of ML results, they tried to find alternative ways to use ML methods, like building them by using some a priori knowledge to induce specific constrains. Some researchers also transformed the outputs of ML algorithms so that they could be interpreted from an economic point of view, or built hybrid ML-DCM models. The object of this thesis is that of investigating the benefits and the disadvantages deriving from adopting either DCMs or ML methods to study the phenomenon of mode choice in transportation. The strongest feature of DCMs is the fact that they produce very precise and descriptive results, allowing for a thorough interpretation of their outputs. On the other hand, ML models offer a substantial benefit by being truly data-driven methods and thus learning most relations from the data itself. As a first contribution, we tested an alternative method for calculating the value of travel time (VTT) through the results of ML algorithms. VTT is a very informative parameter to consider, since the time consumed by individuals whenever they need to travel normally represents an undesirable factor, thus they are usually willing to exchange their money to reduce travel times. The method proposed is independent from the mode-choice functions, so it can be applied to econometric models and ML methods equally, if they allow the estimation of individual level probabilities. Another contribution of this thesis is a neural network (NN) for the estimation of choice models with latent variables as an alternative to DCMs. This issue arose from wanting to include in ML models not only level of service variables of the alternatives, and socio-economic attributes of the individuals, but also psycho-attitudinal indicators, to better describe the influence of psychological factors on choice behaviour. The results were estimated by using two different datasets. Since NN results are dependent on the values of their hyper-parameters and on their initialization, several NNs were estimated by using different hyper-parameters to find the optimal values, which were used to verify the stability of the results with different initializations

    Challenges and prospects of spatial machine learning

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    The main objective of this thesis is to improve the usefulness of spatial machine learning for the spatial sciences and to allow its unused potential to be exploited. To achieve this objective, this thesis addresses several important but distinct challenges which spatial machine learning is facing. These are the modeling of spatial autocorrelation and spatial heterogeneity, the selection of an appropriate model for a given spatial problem, and the understanding of complex spatial machine learning models.Das wesentliche Ziel dieser Arbeit ist es, die Nützlichkeit des räumlichen maschinellen Lernens für die Raumwissenschaften zu verbessern und es zu ermöglichen, ungenutztes Potenzial auszuschöpfen. Um dieses Ziel zu erreichen, befasst sich diese Arbeit mit mehreren wichtigen Herausforderungen, denen das räumliche maschinelle Lernen gegenübersteht. Diese sind die Modellierung von räumlicher Autokorrelation und räumlicher Heterogenität, die Auswahl eines geeigneten Modells für ein gegebenes räumliches Problem und das Verständnis komplexer räumlicher maschineller Lernmodelle

    Deep Neural Networks for Choice Analysis: Architectural Design with Alternative-Specific Utility Functions

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    Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN, to improve DNN's interpretability and predictive power, and to identify effective regularization methods for specific tasks. This study designs a particular DNN architecture with alternative-specific utility functions (ASU-DNN) by using prior behavioral knowledge. Unlike a fully connected DNN (F-DNN), which computes the utility value of an alternative k by using the attributes of all the alternatives, ASU-DNN computes it by using only k's own attributes. Theoretically, ASU-DNN can dramatically reduce the estimation error of F-DNN because of its lighter architecture and sparser connectivity. Empirically, ASU-DNN has 2-3% higher prediction accuracy than F-DNN over the whole hyperparameter space in a private dataset that we collected in Singapore and a public dataset in R mlogit package. The alternative-specific connectivity constraint, as a domain-knowledge-based regularization method, is more effective than the most popular generic-purpose explicit and implicit regularization methods and architectural hyperparameters. ASU-DNN is also more interpretable because it provides a more regular substitution pattern of travel mode choices than F-DNN does. The comparison between ASU-DNN and F-DNN can also aid in testing the behavioral knowledge. Our results reveal that individuals are more likely to compute utility by using an alternative's own attributes, supporting the long-standing practice in choice modeling. Overall, this study demonstrates that prior behavioral knowledge could be used to guide the architecture design of DNN, to function as an effective domain-knowledge-based regularization method, and to improve both the interpretability and predictive power of DNN in choice analysis
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