Transport and Mobility Laboratory: Research projects

TRANSP-OR

Discrete Choice Models

We identify new solutions to transportation problems, on the ground, in the air, or on the sea, transport of people or goods, whatever the mode. We focus on technical solutions, but also on their impact on the system as a whole. We are also interested in the interactions of the transportation systems with the land use, the economy, the environment, etc.

Incorporating advanced behavioral models in mixed integer linear optimizationTop

Incorporating advanced behavioral models in mixed integer linear optimizationSwiss National Science Foundation

Discrete choice models are used for detailed representation of the "demand". However, their complexity makes mathematical formulations highly non convex in the explanatory variables. On the other hand, Mixed Integer Linear Programs (MILP) are optimization problems with discrete variables that are used in many applications to design and configure the "supply". In this project, we propose a new modeling framework that allows to include any random utility model in a mixed integer optimization formulation. The heterogeneity of demand is captured within the general choice model framework and the offers are tailored in a way that is beneficial for users as wells as providers. The main objective of the project is to obtain a framework that is both general, so that it can be applied in many applications, and operational, so that it can be used in practice.

Principal investigator
Michel Bierlaire
Project manager
Shadi Sharif Azadeh
Sponsor
Swiss National Science Foundation
Period
April 01, 2016-March 31, 2019
Collaborator
Meritxell Pacheco
LaTeX description

TRANS-FORM: Smart transfers through unravelling urban form and travel flow dynamicsTop

TRANS-FORM: Smart transfers through unravelling urban form and travel flow dynamicsThe Federal Department of the Environment, Transport, Energy and Communications (DETEC)

TRANS-FORM, a cooperation between universities, industrial partners, public authorities and private operators, will develop, implement and test a data driven decision making tool that will support smart planning, and proactive and adaptive operations. The objective of the project is to better understand transferring dynamics in multi-modal public transport systems and develop insights, strategies and methods to support decision makers in transforming public transport usage to a seamless travel experience by using smart data. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. TRANS-FORM is funded under the European Commission Horizon 2020 ERA-NET program.

Principal investigator
Michel Bierlaire
Project managers
Shadi Sharif Azadeh, Riccardo Scarinci
Sponsor
The Federal Department of the Environment, Transport, Energy and Communications (DETEC)
Period
March 03, 2016-March 03, 2019
Collaborator
Nicholas Molyneaux
LaTeX description

Drive For You: a driving assistant tool to detecting pedestriansTop

Drive For You: a driving assistant tool to detecting pedestriansMINES ParisTech

This project aims to develop an onboard pedestrian tracking system to assist the driver detect them and, ultimately, to increase security. The project, with a duration of four years, will focus on pedestrians detection, tracking and trajectory prediction, and will be will be closely related to vehicle command strategies. The project is part of the automated driving research Chair "Drive for you" led by MINES ParisTech in partnership with French industrialists and three prestigious academic institutions the Ecole Polytechnique Fédérale de Lausanne EPFL (Switzerland), the University of Shanghai Jiao Tong (China) and the University of Berkeley (USA). Supported by the Foundation MINES ParisTech, with Valeo industrial, PSA Peugeot Citroën and Safran contributing 3.7 million euros in funds, the Chair will work for five years on the subject of automated driving. The three main objectives are expand knowledge of self-driving vehicles, develop intelligent onboard systems, get self-driving vehicles on the road in Asia, Europe and the United States.

Principal investigator
Michel Bierlaire
Project manager
Riccardo Scarinci
Sponsor
MINES ParisTech
Period
December 01, 2014-November 30, 2018
External collaboration
Damien Matti (EPFL Signal Processing Laboratory 5 LTS5)
LaTeX description

Electric vehicle adoption dynamics: exploring market potentialsTop

Electric vehicle adoption dynamics: exploring market potentialsNissan

This project proposes innovative methods to identify the determinants of acceptance of alternative vehicles and their impact on everyday mobility.

The greatest challenge faced by the promoters of the transition towards this low carbon engine technology lies in understanding how consumers accept the financial and lifestyle investments associated with the leap from traditional to electric powertrains. This project proposes innovative methods to identify the determinants of acceptance of alternative vehicles and their impact on everyday mobility. A deeper understanding of adoption dynamics is critical to predict who will opt for EVs when and under which conditions.

This project will focus on innovative data-collection and modelling methodologies to uncover the acceptance of EVs at different stages of market-penetration (considering inexperienced/experienced users, early pioneers/late-adopters). A thorough analysis of the consumer decision-making process will lead to uncovering the barriers and success factors related to EV uptake and to forecast buying and usage behaviours related to new vehicle classes.

Principal investigator
Michel Bierlaire
Project manager
Matthieu de Lapparent
Sponsor
Nissan
Period
April 01, 2014-March 31, 2017
Collaborator
Anna Fernandez Antolin
External collaboration
Dr. Amanda Stathopoulos, Northwestern University
LaTeX description

Expertise

  • Transportation Research
  • Operations Research
  • Discrete Choice Models

Methods

Modeling, optimization, simulation