A Bayesian Estimation of Pedestrian Activities using Sensors Data

Pedestrian modeling is a tool for designing and optimizing infrastructures. In particular, walking in transportation hubs such as railway stations and airports is the key for an efficient multimodal transport systems. Data collections are needed in estimating the demand for these infrastructures. These data can further be used in destination choice models in order to understand pedestrian behavior and forecast demand for future scenarios. Possible applications: railway stations, airports, music festivals. 3rd prize of the ENAC Research Day 2013 Doctoral poster awar

An Empirical Investigation of the Determinants of Attention to Attributes in Choice Experiments

Recent results showed that taking into account attention to attributes in random utility models leads to better fitting models. In this paper, we study a proposition to model attention to attributes instead of monitoring it. We test this model considering attention as a choice. The results show that these factors have a low power of prediction of stated attention to single attributes

Modèles de choix discrets pour la reconnaissance des expressions faciales statiques

Ce projet de semestre présente l’utilisation des modèles de choix discret pour construire un modèle de perception des expressions faciales statiques potentiellement utilisable pour la reconnaissance et la classification de ces expressions. La description de ces expressions s’inspire du système Facial Action Coding System (FACS) de Paul Ekman, basé sur une analyse anatomique de l’action faciale. L’ensemble de choix contient 6 expressions faciales universelles plus l’expression neutre. Chaque alternative est décrite par une fonction d’utilité, définie par une combinaison linéaire de termes exprimant les Action Units (AU) du système FACS et par un terme aléatoire capturant l’incertitude. Les utilités permettent une mesure de la vraisemblance pour une combinaison de ces termes de représenter une certaine expression faciale. Elles sont une formalisation de la connaissance a priori du processus. Les paramètres de la fonction d’utilité sont définis par une estimation du maximum de vraisemblance. La classification pourrait être obtenue en assignant l’expression obtenant l’utilité maximum

WiFi-Based Marauder's Map, or where are members of a campus and why?

Similarly to the Marauder's Map in Harry Potter, we tracked employees and students on campus. We model this output in terms of choice of activity type in time. By discretizing time, this choice is represented as a path in a network. The choice set is generated using a Metropolis-Hasting algorithm based on attractivity measures, and the utility function describes satiation effects and time-of-day preferences. Such a methodology is useful to build an activity-based model for pedestrian infrastructure, estimate the demand and its drivers, and forecast it for different scenarios

A path choice approach to activity modeling with a pedestrian case study

Theses slides present a model about activity choice seen as a route/path choice. An activity network is defined. Choice set generation is described. A route/path choice model is proposed. This model includes a measurement equation for WiFi traces, a correction for importance sampling and a correction for the correlation related to overlapping segments of paths. We call this last element the "activity path size"

Detecting pedestrian destinations from ubiquitous digital footprint

Walking is the key for efficient multimodal transport systems. Pedestrian infrastructures, such as railway stations, face congestion and need to understand flows. Data is required for models that can help managers in their infrastructure planning. In this context, data from communication network are more suitable than mobile phone data. We propose to use existing WiFi traces. Due to the poor quality of WiFi localization, a Bayesian approach is proposed. We generate candidate lists of destinations and compute the likelihood of observing these traces in the pedestrian network. We made a proof of concept on a campus, using class schedule and walking distance