Dynamic Latent Plan Models
نویسندگان
چکیده
Planning is an integral part of many behavioural aspects related to transportation: residential relocation, activity and travel scheduling, route choice, etc. People make plans and then select actions to execute those plans. The plans are inherently dynamic. They evolve due to situational constraints and contextual factors, experience, inertia, or changing preferences. As a result, the chosen actions might be different from those initially planned. In this paper, we present the methodology to model the dynamics of choices using a two-layer decision hierarchy (choice of a plan and choice of action conditional on the plan) and its dynamics. This framework, based on Hidden Markov Model principles, assumes that the plan at every time period depends on the plan at the previous time period and the actions taken in the previous time periods as well as other variables including the characteristics of the decision maker. The dynamics in the observed actions are explained by the dynamics in the underlying latent (unobserved) plans. The methodology is demonstrated by modelling the dynamics associated with the driving decisions as the drivers enter a freeway. The model is estimated using disaggregate trajectory data and validated in a microscopic traffic simulator.
منابع مشابه
Spatial Latent Gaussian Models: Application to House Prices Data in Tehran City
Latent Gaussian models are flexible models that are applied in several statistical applications. When posterior marginals or full conditional distributions in hierarchical Bayesian inference from these models are not available in closed form, Markov chain Monte Carlo methods are implemented. The component dependence of the latent field usually causes increase in computational time and divergenc...
متن کاملExact Decoding on Latent Variable Conditional Models is NP-Hard
Latent variable conditional models, including the latent conditional random fields as a special case, are popular models for many natural language processing and vision processing tasks. The computational complexity of the exact decoding/inference in latent conditional random fields is unclear. In this paper, we try to clarify the computational complexity of the exact decoding. We analyze the c...
متن کاملBayesian Latent Threshold Modeling: Multivariate Time Series and Dynamic Networks
We discuss dynamic network modeling for multivariate time series, exploiting dynamic variable selection and model structure uncertainty strategies based on the recently introduced concept of “latent thresholding.” This dynamic modeling concept addresses a critical and challenging problem in multivariate time series and dynamic modeling: that of inducing formal probabilistic structures that are ...
متن کاملSequential Labeling with Latent Variables: An Exact Inference Algorithm and its Efficient Approximation
Latent conditional models have become popular recently in both natural language processing and vision processing communities. However, establishing an effective and efficient inference method on latent conditional models remains a question. In this paper, we describe the latent-dynamic inference (LDI), which is able to produce the optimal label sequence on latent conditional models by using eff...
متن کاملنقد مدلهای تقاضای نیروی کار در برنامه سوم توسعه و ارائه مدلهای مناسب
The results of performance evaluation in the creation of employment during the recent years indicates that employment goals in the third development plan were not achieved while goals in the area of production and investment growth rates were fulfilled accordingly. The analysis of labor demand models in the third development plan is conducted in order to identify their weaknesses in predicting ...
متن کامل