Analyse du comportement hétérogène des usagers dans un réseau

Using transportation roads enables workers to reach their work facilities. Security and traffic jam issues are all the more important given that the number of vehicles is always increasing and we will focus on merchandise transporters in this study. Dangerous items transportation is under strict control as it is for example forbidden for them to be carried through a tunnel or across a bridge. Some transporters may drive a vehicle that has defects or/and they may be ta\-king some forbidden roads so as to reach their destination faster. Transportation of goods is regulated by the law and there exists a control system, whose purpose is to detect frauds and to make sure controlled vehicles are in order. The strategic deployment of control resources can be based on the knowledge of transporters behaviour, which is going to be studied through their route choice analysis. The number of routes can be unbounded especially if we consider loops, which leads to a complex problem to be solved. We can also mention issues closely related to route choice problem using discrete choice models such as correlation between routes sharing links and point out the fact that human decision process is not considered something easy.  A route choice problem can be modelled based on the random utility theory and as a consequence we will focus on the discrete choice models. We are going to use such model on the real road network of Quebec and we will derive an expression of the probability, for a transporter, to pick one route. We are going to explain the way we did our study. It started first by doing a data description job as we are convinced this is a step that will help other analysts to have a clear view of the data situation. Some data are network related and the corresponding attributes collected will be used to model the road network of Quebec.  We will use some attributes to explain the utility function, which leads to the definition of the function that gives the probability that a user takes a given route. Once this function is fully specified, the behaviour study can be done, except that we have a set of observations that are absolutely incomplete. When observations are a gathering of data collected during a road control, the information they provide us is not enough and thus, the parameters estimation will fail.   We might seem blocked but in fact, we brought the idea of using simulated observations.  We are going to estimate model parameters with firstly complete observations and in order to imitate the real conditions, we then are going to use partial observations. This constitutes a main challenge and we overcome it by using the results presented in (Bierlaire et Frejinger, 2008) combined with those from (Fosgerau, Frejinger et Karlström, 2013). We will demonstrate that even though the observations used are simulated, we will deliver conclusions that can be useful for road network managers. The main results we provide in this work is that estimation can be done with a 0,05 signification level on real road network of Quebec, while the observations are incomplete. Eventually, our results should motivate network managers to improve the set of questions they use to collect data as it would help them to strengthen their knowledge about the merchandise transporters and hopefully, the decision process will lead to optimized resource deployments.

Keyphrases: Recursive logit, Discrete choice, Route choice, Random utility, Maximum likelihood, Unconstrained and convex optimization, Partial observations, Behaviour analysis