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Mode personalization in trip based transit routing
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| Vassilissa Lehoux, Darko Drakulic |
| ATMOS (Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems), Munich, Germany, 9-13 September, 2019 |
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@inproceedings{lehoux2019mode,
title={Mode Personalization in Trip-Based Transit Routing},
author={Lehoux, Vassilissa and Drakulic, Darko},
booktitle={19th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2019)},
year={2019},
organization={Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik}
}We study the problem of finding bi-criteria Pareto optimal journeys in public transit networks. We extend the Trip Based Public Transit Routing (TB) approach to allow for user to select modes of interest at query time. As a first step, we modify the preprocessing of the TB method for it to be correct for any set of selected modes. Then, we change the bi-criteria earliest arrival time queries and the propose an algorithm for latest departure time queries that can handle the definition of the mode set at query time. Experiments are run on networks of different sizes to evaluate the cost of allowing for mode personalization. They show that although preprocessing times are increased, query times are similar when all modes are allowed and lower when some part of the network is removed by mode selection.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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