This thesis tackles new challenges associated with the disaggregate modeling of the human behavior. Decision-aid tools help in making decisions, by providing quantitative insights on the decisions and associated consequences. They are useful in complex situations where human actors are involved. Inside decision-aid tools, there is a need for explicitly capturing and predicting the human behavior. The prediction of human actions is done through models. Models are simplified representations of the reality, which provide a better understanding of it and allow to predict its future state. They are often too simplistic, with bad prediction capabilities. This is an issue as they generate the outcome of the decision-aid tools, which influence decisions. Good models are required in order to adequately capture the complexity of human actions. Behavioral models appear to be relevant. They allow to translate behavioral assumptions into equations, which make their strength but also their complexity. They have been mainly used in transportation and marketing. Many advances have been recently achieved. On one hand, emerging technologies allow to collect various and detailed data about the human behavior. On the other hand, new modeling techniques have been proposed to handle complex behaviors. Estimation softwares are now available for their estimation. The combination of these advances open opportunities in the field of the behavioral modeling. The motivations of the proposed work are the investigation of the challenges associated with non-traditional applications of the behavioral modeling, the emphasis of multi-disciplinarity, the handling of the behavioral complexity and the development of operational models. Different applications are considered where these challenges appear. The applications are the investors' behavior, the walking behavior and the dynamic facial expression recognition. Challenges are addressed in the different tasks of the modeling framework, which are the data collection, the data processing, the model specification, estimation and validation. The modeling of the investors' behavior consists in characterizing how individuals are taking financial decisions. It is relevant for predicting monetary gains and regulating the market. We propose an hybrid discrete choice framework for modeling decisions of investors performed on stock markets. We focus on the choice of action (buy or sell) and the duration until the next action. The choice of action is handled with a binary logit model with latent classes, while a Weibull regression model is used for the duration until the next action. Both models account for the risk perception and the dynamics of the phenomenon. They are simultaneously estimated by maximum likelihood using real data. The predictive performance of the models are tested by cross-validation. The forecasting accuracy of the action model is studied more in details. Parameters of both models are interpretable and emphasize interesting behavioral mechanisms related to investors' decisions. The good prediction capabilities of the action model in a real context makes it operational. The modeling of the walk apprehends how a person is choosing her next step. It is useful to simulate the behavior of crowds, which is relevant for the urban planning and the design of infrastructures. We specify, estimate and validate a model for pedestrian walking behavior, based on discrete choice modeling. Two main types of behavior are identified: unconstrained and constrained. By unconstrained, we refer to behavior patterns which are independent from other individuals. The constrained patterns are captured by a leader-follower model and by a collision avoidance model. The spatial correlation between the alternatives is captured by a cross nested logit model. The model is estimated by maximum likelihood on a real data set of pedestrian trajectories, manually tracked from video sequences. The model is successfully validated using another data set of bi-directional pedestrian flows. The dynamic facial expression recognition consists in characterizing the facial expression of a subject in a video. This is relevant in human machine interfaces. We model it using a discrete choice framework. The originality is based on the explicit modeling of causal effects between the facial features and the recognition of the expression. Five models are proposed, based on different assumptions. The first assumes that only the last frame of the video triggers the choice of the expression. In the second model, one frame is supposed to trigger the choice. The third model is an extension of the second model. It assumes that the choice of the expression results from the average of expression perceptions within a group of frames. The fourth and fifth models integrate the panel effect inherent to the estimation data and are respectively based on the first and second models. The models are estimated by maximum likelihood using facial videos. Parameters are interpretable. Labeling data on the videos has been obtained using an internet survey. The prediction capabilities of the models are studied and compared, by cross-validation using the estimation data. The results are satisfactory, emphasizing the relevance of the models in a real context. The thesis contributes to fields. Challenges of the behavioral modeling have been investigated in complex contexts. Original and multi-disciplinary modeling approaches have been successfully proposed for each application. Model specifications have been developed to handle the behavioral complexity, allowing to quantify behavioral mechanisms. Operational models are proposed. Complex behavioral models are used in a predictive context and a detailed validation methodology has been set.