Cité Descartes, Champs sur Marne
77447, Cedex, France.


Université Gustave Eiffel, Batiment Bienvenüe
Ecole des Ponts ParisTech (ENPC)


+331 81 66 87 04

Session prochaine (session hybride: présentiel + visioconférence)

Date: 7 decembre 2023
Lieu: Batiment Bienvenüe, et aussi en visioconférence (Zoom).


14h00 Pravesh BIYANI, Associate Professor at IIIT Delhi, Founder of Anamar Tech.
No Transfers Required: Integrating Last Mile with Public Transit Using Opti-Mile.
Abstract: Public transit is a popular mode of transit due to its affordability, despite the inconveniences due to the necessity of transfers required to reach most areas. For example, in the bus and metro network of New Delhi, only 30% of stops can be directly accessed from any starting point, thus requiring transfers for most commutes. Additionally, last-mile services like rickshaws, tuk-tuks or shuttles are commonly used as feeders to the nearest public transit access points, which further adds to the complexity and inefficiency of a journey. Ultimately, users often face a tradeoff between coverage and transfers to reach their destination, regardless of the mode of transit or the use of last-mile services. To address the problem of limited accessibility and inefficiency due to transfers in public transit systems, we propose ``opti-mile," a novel trip planning approach that combines last-mile services with public transit such that no transfers are required. Opti-mile allows users to customize trip parameters such as maximum walking distance, and acceptable fare range. We analyse the transit network of New Delhi, evaluating the efficiency, feasibility and advantages of opti-mile for optimal multi-modal trips between randomly selected source-destination pairs.
14h45 Benoit MATET, COSYS/GRETTIA lab. University Gustave Eiffel.
Use of Origin-Destination data for calibration and spatialization of activity based models.
Abstract: Mobile phone data are a good source to estimate aggregated travel demand. In this work, we investigate how Origin-Destination (OD) matrices from mobile phone data can be used along with Household Travel Surveys (HTS) to synthesise comprehensive travel demand that is both detailed at the individual leve and calibrated to what is observed from mobile data. We propose a calibration step and a spatialisation step that can be added to the already existing state-of-the-art pipelines, which can improve the realism of synthetic travel demand when the OD matrices are assumed of good quality. Our approach can also measure the incompatibilities between the HTS and the mobile data that are known to exist but hard to quantify.
14h45 Sarah GASMI, COSYS/GRETTIA lab. University Gustave Eiffel.
Multi-class traffic carbon abatement via speed limit optimization.
Abstract: As the urgency to mitigate climate change intersects with the need for efficient transportation systems, innovative traffic management strategies become crucial. This talk will delve into the challenge of devising traffic management strategies aimed at carbon abatement within the complex dynamics of a multi-modal road network. Our objective is to strike a critical balance between environmental sustainability and economic viability. The strategy centers on managing traffic via speed limit control to concurrently optimize the total travel time for users and minimize carbon emissions. Our methodology employs a Mixed-Integer Linear Programming (MILP) formulation to address this multi-class, bi-objective network problem under a series of constraints. Initially, we tackle the User Equilibrium (UE)-based traffic assignment , progressing to incorporate the Boundedly Rational User Equilibrium (BRUE), which introduces a layer of flexibility in the system. A key innovation in our approach is the consideration of free-flow speed as a decision variable within both UE and BRUE frameworks. The practicality of our model is underscored by its application to the transportation network of Tilburg, The Netherlands. This case study exemplifies the real-world implications and potential of our approach. By exploring this model, we aim to contribute to the development of traffic management systems that are both eco-friendly and economically sound, paving the way for smarter, sustainable urban mobility solutions.