Markov decision processes for services opportunity pipeline optimization

The dynamics of sales opportunities can be modelled by a Markov Decision Process. The latter can be solved by using dynamic programming and assigns to each state an optimal action. In this project, states are modelled by the number of opportunities at five different maturity levels called ranks, actions are represented by investments and rewards by profits from signed contracts. Transitions are simulated using the probabilities that an opportunity moves from one rank to another. Two different types of policy appear recurrently in the model outcome, i.e. a low-investment policy when the opportunities are rather uniformly distributed across ranks and a high-investment policy, when a larger number of opportunities have reached mature status or have juste entered the pipe

Models capturing latent constructs

In the context of the development of hybrid choice models (HCM), we are interested in understanding better how to design models which can account for the latent information contained in new data types (psychometrics, data from smartphones, etc.) for which there is a growing interest. Conversely, we also focus on establishing learnings from these models in order to better design surveys which are adapted to these new data

Accounting for response behavior heterogeneity in the measurement of attitudes: an application to demand for electric vehicles

Hybrid choice models have proved to be a powerful framework that integrates attitudinal and perceptional data into discrete choice models. However the measurement component of such a framework often fails to exploit individual-specific information that might affect the way subjects answer to indicators of opinion. In this paper we propose an HCM with a measurement model that takes into account heterogeneity in the response behavior. Precisely, we capture effects of exaggeration in answers to psychometrics. We moreover provide an application of this model to the evaluation of the future demand for electric vehicles

The use of word data to measure perception in hybrid choice models

This research aims at developing a hybrid choice model (HCM) where a perceptional variable is measured by means of adjectives reported by individuals. Due to the qualitative nature of adjectives, the main challenges of the study involve their quantification and their integration into HCMs. In order to address these issues, we first obtain measures of the strength of the adjectives on the scale of the perceptional variable by using ratings from external evaluators. Second, an advanced measurement model of the perceptional variable is specified, in order to account for variations occurring in the answers from the evaluators

Capturing response behavior heterogeneity in the quantification of semi-open questions: a mixed discrete-continuous approach

This paper presents a method to account for response behavior heterogeneity in the quantification of adjectives reported in semi-open questions. Semi-open questions are useful to capture psychological constructs such as perceptions. However, due to the qualitative nature of adjectives, it is difficult to assign an objective numerical value to them. The proposed model contributes to (1) account for response behavior heterogeneity in the evaluation of a new data type and (2) investigate the use of both discrete and continuous scales. As application example, the estimated ratings of the adjectives are integrated into a hybrid choice model of transportation mode preferences

Modeling demand for electric vehicles: the effect of car users' attitudes and perceptions

The near arrival of electric vehicles on the car market generates a need for new models in order to understand and predict the impact it has on the current market shares. This research aims at providing contributions regarding several issues related to the evaluation of the demand for electric vehicles, i.e. related to the survey design, demand models and forecasting. In this paper we focus on the first of these three methodological issues. We present the design of a stated preference survey which will enable us to accurately model and predict demand for electric vehicles. Our aim is to propose choice situations involving electric cars and petrol-driven ones and in particular which include the respondents' own cars. An experimental design is set up in order to test the effect of the variation of several characteristics related to electric cars on vehicle preferences. Opinion and perception data are also collected to capture the impact of attitudinal variables on the purchase decision. This document also presents promising preliminary results of the estimation of the logit model with multiple alternatives

Mode choice with attitudinal latent class: a Swiss case-study

Attitudinal attributes play an important role in behavior of individuals in various contexts. In this study we focus on the travel behavior and our main objective is to come up with segments of individuals that have different mode choice preferences with the help of the attitudinal attributes. These segments are important for the design of more efficient public transport policies specific to the characteristics of different customers. In order to identify the segments of individuals, most importantly the potential users of public transport, factor analysis techniques are utilized with the attitudinal attributes and the socio-economic characteristics of individuals. The learnings from this exploratory analysis are exploited in the construction of the class-membership model in our hybrid choice model. Maximum likelihood estimation is done simultaneously for the latent class model, including the measurement equations for the psychometric indicators, and the class-specific choice models. The results for the presented model with two latent classes show that middle-aged individuals with high income who are active in their professional and social life have higher value of time and are less elastic to the changes in the transport offer compared to the rest of the population