Technologies respectueuses de la vie privée pour le covoiturage

L'émergence des téléphones mobiles et objets connectés a profondément changé notre vie quotidienne. Ces dispositifs, grâce à la multitude de capteurs qu'ils embarquent, permettent l'accès à un large spectre de services. En particulier, les capteurs de position ont contribué au développement des services de localisation tels que la navigation, le covoiturage, le suivi de la congestion en temps réel... En dépit du confort offert par ces services, la collecte et le traitement des données de localisation portent de sérieuses atteintes à la vie privée des utilisateurs. En effet, ces données peuvent renseigner les fournisseurs de services sur les points d'intérêt (domicile, lieu de travail, orientation sexuelle), les habitudes ainsi que le réseau social des utilisateurs. D'une façon générale, la protection de la vie privée des utilisateurs peut être assurée par des dispositions légales ou techniques. Même si les mesures d'ordre légal peuvent dissuader les fournisseurs de services et les individus malveillants à enfreindre le droit à la vie privée des utilisateurs, les effets de telles mesures ne sont observables que lorsque l'infraction est déjà commise et détectée. En revanche, l'utilisation des technologies renforçant la protection de la vie privée (PET) dès la phase de conception des systèmes permet de réduire le taux de réussite des attaques contre la vie privée des utilisateurs. L'objectif principal de cette thèse est de montrer la viabilité de l'utilisation des PET comme moyens de protection des données de localisation dans les services de covoiturage. Ce type de service de localisation, en aidant les conducteurs à partager les sièges vides dans les véhicules, contribue à réduire les problèmes de congestion, d'émissions et de dépendance aux combustibles fossiles. Dans cette thèse, nous étudions les problèmes de synchronisation d'itinéraires et d'appariement relatifs au covoiturage avec une prise en compte explicite des contraintes de protection des données de localisation (origine, destination). Les solutions proposées dans cette thèse combinent des algorithmes de calcul d'itinéraires multimodaux avec plusieurs techniques de protection de la vie privée telles que le chiffrement homomorphe, l'intersection sécurisée d'ensembles, le secret partagé, la comparaison sécurisée d'entier. Elles garantissent des propriétés de protection de vie privée comprenant l'anonymat, la non-chainabilité et la minimisation des données. De plus, elles sont comparées à des solutions classiques, ne protégeant pas la vie privée. Nos expérimentations indiquent que les contraintes de protection des données privées peuvent être prise en compte dans les services de covoiturage sans dégrader leurs performances.The emergence of mobile phones and connected objects has profoundly changed our daily lives. These devices, thanks to the multitude of sensors they embark, allow access to a broad spectrum of services. In particular, position sensors have contributed to the development of location-based services such as navigation, ridesharing, real-time congestion tracking... Despite the comfort offered by these services, the collection and processing of location data seriously infringe the privacy of users. In fact, these data can inform service providers about points of interests (home, workplace, sexual orientation), habits and social network of the users. In general, the protection of users' privacy can be ensured by legal or technical provisions. While legal measures may discourage service providers and malicious individuals from infringing users' privacy rights, the effects of such measures are only observable when the offense is already committed and detected. On the other hand, the use of privacy-enhancing technologies (PET) from the design phase of systems can reduce the success rate of attacks on the privacy of users. The main objective of this thesis is to demonstrate the viability of the usage of PET as a means of location data protection in ridesharing services. This type of location-based service, by allowing drivers to share empty seats in vehicles, helps in reducing congestion, CO2 emissions and dependence on fossil fuels. In this thesis, we study the problems of synchronization of itineraries and matching in the ridesharing context, with an explicit consideration of location data (origin, destination) protection constraints. The solutions proposed in this thesis combine multimodal routing algorithms with several privacy-enhancing technologies such as homomorphic encryption, private set intersection, secret sharing, secure comparison of integers. They guarantee privacy properties including anonymity, unlinkability, and data minimization. In addition, they are compared to conventional solutions, which do not protect privacy. Our experiments indicate that location data protection constraints can be taken into account in ridesharing services without degrading their performance

Towards the reduction of greenhouse gas emissions : models and algorithms for ridesharing and carbon capture and storage

Avec la ratification de l'Accord de Paris, les pays se sont engagés à limiter le réchauffement climatique bien en dessous de 2, de préférence à 1,5 degrés Celsius, par rapport aux niveaux préindustriels. À cette fin, les émissions anthropiques de gaz à effet de serre (GES, tels que CO2) doivent être réduites pour atteindre des émissions nettes de carbone nulles d'ici 2050. Cet objectif ambitieux peut être atteint grâce à différentes stratégies d'atténuation des GES, telles que l'électrification, les changements de comportement des consommateurs, l'amélioration de l'efficacité énergétique des procédés, l'utilisation de substituts aux combustibles fossiles (tels que la bioénergie ou l'hydrogène), le captage et le stockage du carbone (CSC), entre autres. Cette thèse vise à contribuer à deux de ces stratégies : le covoiturage (qui appartient à la catégorie des changements de comportement du consommateur) et la capture et le stockage du carbone. Cette thèse fournit des modèles mathématiques et d'optimisation et des algorithmes pour la planification opérationnelle et tactique des systèmes de covoiturage, et des heuristiques pour la planification stratégique d'un réseau de captage et de stockage du carbone. Dans le covoiturage, les émissions sont réduites lorsque les individus voyagent ensemble au lieu de conduire seuls. Dans ce contexte, cette thèse fournit de nouveaux modèles mathématiques pour représenter les systèmes de covoiturage, allant des problèmes d'affectation stochastique à deux étapes aux problèmes d'empaquetage d'ensembles stochastiques à deux étapes qui peuvent représenter un large éventail de systèmes de covoiturage. Ces modèles aident les décideurs dans leur planification opérationnelle des covoiturages, où les conducteurs et les passagers doivent être jumelés pour le covoiturage à court terme. De plus, cette thèse explore la planification tactique des systèmes de covoiturage en comparant différents modes de fonctionnement du covoiturage et les paramètres de la plateforme (par exemple, le partage des revenus et les pénalités). De nouvelles caractéristiques de problèmes sont étudiées, telles que l'incertitude du conducteur et du passager, la flexibilité de réappariement et la réservation de l'offre de conducteur via les frais de réservation et les pénalités. En particulier, la flexibilité de réappariement peut augmenter l'efficacité d'une plateforme de covoiturage, et la réservation de l'offre de conducteurs via les frais de réservation et les pénalités peut augmenter la satisfaction des utilisateurs grâce à une compensation garantie si un covoiturage n'est pas fourni. Des expériences computationnelles détaillées sont menées et des informations managériales sont fournies. Malgré la possibilité de réduction des émissions grâce au covoiturage et à d'autres stratégies d'atténuation, des études macroéconomiques mondiales montrent que même si plusieurs stratégies d'atténuation des GES sont utilisées simultanément, il ne sera probablement pas possible d'atteindre des émissions nettes nulles d'ici 2050 sans le CSC. Ici, le CO2 est capturé à partir des sites émetteurs et transporté vers des réservoirs géologiques, où il est injecté pour un stockage à long terme. Cette thèse considère un problème de planification stratégique multipériode pour l'optimisation d'une chaîne de valeur CSC. Ce problème est un problème combiné de localisation des installations et de conception du réseau où une infrastructure CSC est prévue pour les prochaines décennies. En raison des défis informatiques associés à ce problème, une heuristique est introduite, qui est capable de trouver de meilleures solutions qu'un solveur commercial de programmation mathématique, pour une fraction du temps de calcul. Cette heuristique comporte des phases d'intensification et de diversification, une génération améliorée de solutions réalisables par programmation dynamique, et une étape finale de raffinement basée sur un modèle restreint. Dans l'ensemble, les contributions de cette thèse sur le covoiturage et le CSC fournissent des modèles de programmation mathématique, des algorithmes et des informations managériales qui peuvent aider les praticiens et les parties prenantes à planifier des émissions nettes nulles.With the ratification of the Paris Agreement, countries committed to limiting global warming to well below 2, preferably to 1.5 degrees Celsius, compared to pre-industrial levels. To this end, anthropogenic greenhouse gas (GHG) emissions (such as CO2) must be reduced to reach net-zero carbon emissions by 2050. This ambitious target may be met by means of different GHG mitigation strategies, such as electrification, changes in consumer behavior, improving the energy efficiency of processes, using substitutes for fossil fuels (such as bioenergy or hydrogen), and carbon capture and storage (CCS). This thesis aims at contributing to two of these strategies: ridesharing (which belongs to the category of changes in consumer behavior) and carbon capture and storage. This thesis provides mathematical and optimization models and algorithms for the operational and tactical planning of ridesharing systems, and heuristics for the strategic planning of a carbon capture and storage network. In ridesharing, emissions are reduced when individuals travel together instead of driving alone. In this context, this thesis provides novel mathematical models to represent ridesharing systems, ranging from two-stage stochastic assignment problems to two-stage stochastic set packing problems that can represent a wide variety of ridesharing systems. These models aid decision makers in their operational planning of rideshares, where drivers and riders have to be matched for ridesharing on the short-term. Additionally, this thesis explores the tactical planning of ridesharing systems by comparing different modes of ridesharing operation and platform parameters (e.g., revenue share and penalties). Novel problem characteristics are studied, such as driver and rider uncertainty, rematching flexibility, and reservation of driver supply through booking fees and penalties. In particular, rematching flexibility may increase the efficiency of a ridesharing platform, and the reservation of driver supply through booking fees and penalties may increase user satisfaction through guaranteed compensation if a rideshare is not provided. Extensive computational experiments are conducted and managerial insights are given. Despite the opportunity to reduce emissions through ridesharing and other mitigation strategies, global macroeconomic studies show that even if several GHG mitigation strategies are used simultaneously, achieving net-zero emissions by 2050 will likely not be possible without CCS. Here, CO2 is captured from emitter sites and transported to geological reservoirs, where it is injected for long-term storage. This thesis considers a multiperiod strategic planning problem for the optimization of a CCS value chain. This problem is a combined facility location and network design problem where a CCS infrastructure is planned for the next decades. Due to the computational challenges associated with that problem, a slope scaling heuristic is introduced, which is capable of finding better solutions than a state-of-the-art general-purpose mathematical programming solver, at a fraction of the computational time. This heuristic has intensification and diversification phases, improved generation of feasible solutions through dynamic programming, and a final refining step based on a restricted model. Overall, the contributions of this thesis on ridesharing and CCS provide mathematical programming models, algorithms, and managerial insights that may help practitioners and stakeholders plan for net-zero emissions

Using group role assignment to solve Dynamic Vehicle Routing Problem

The Dynamic Vehicle Routing Problem (DVRP) is a more complex problem than the traditional Vehicle Routing Problem (VRP) in the combinatorial optimization of operations research. With more degrees of freedom, DVRP introduces new challenges while judging the merit of a given route plan. This thesis utilized the time slice strategy to solve dynamic and deterministic routing problems. Based on Group Role Assignment (GRA) and two different routing methods (Modified Insertion heuristic routing and Modified Composite Pairing Or-opt routing), a new ridesharing system has been designed to provide services in the real world. Simulation results are presented in this thesis. A qualitative comparison has been made to outline the advantages and performance of our solution framework. From the numerical results, the proposed method has a great potential to put into operation in the real world and provides a new transit option for the public.Master of Science (MSc) in Computational Scienc

Opportunities and Challenges of DLT (Blockchain) in Mobility and Logistics

This report presents the economic potential, legal framework, and technical foundations required to understand distributed ledger (DL) / blockchain technology and llustrates the opportunities and challenges they present, especially in the mobility and logistics sectors. It was compiled by the blockchain laboratory at Fraunhofer FIT on behalf of the German Federal Ministry of Transport and Digital Infrastructure (BMVI). Its intended audience comprises young companies seeking, for example, a legal assessment of data protection issues related to DL and blockchain technologies, decisionmakers in the private sector wishing concrete examples to help them understand how this technology can impact existing and emerging markets and which measures might be sensible from a business perspective, public policymakers and politicians wishing to familiarize themselves with this topic in order to take a position, particularly in the mobility and logistics sectors, and members of the general public interested in the technology and its potential. The report does not specifically address those with a purely academic or scientific interest in these topics, although parts of it definitely reflect the current state of academic discussion

Decentralized task allocation for dynamic, time-sensitive tasks

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 103-110).In time-sensitive and dynamic missions, autonomous vehicles must respond quickly to new information and objectives. In the case of dynamic task allocation, a team of agents are presented with a new, unknown task that must be allocated with their original allocations. This is exacerbated further in decentralized settings where agents are limited to utilizing local information during the allocation process. This thesis presents a fully decentralized, dynamic task allocation algorithm that extends the Consensus-Based Bundle Algorithm (CBBA) to allow for allocating new tasks. Whereas static CBBA requires a full resetting of previous allocations, CBBA with Partial Replanning (CBBA-PR) enables the agents to only partially reset their allocations to efficiently and quickly allocate a new task. By varying the number of existing tasks that are reset during replan, the team can trade-off convergence speed with amount of coordination. By specifically choosing the lowest bid tasks for resetting, CBBA-PR is shown to converge linearly with the number of tasks reset and the network diameter of the team. In addition, limited replanning methods are presented for scenarios without sufficient replanning time. These include a single reset bidding procedure for agents at capacity, a no-replanning heuristic that can identify scenarios that does not require replanning, and a subteam formation algorithm for reducing the network diameter. Finally, this thesis describes hardware and simulation experiments used to explore the effects of ad-hoc, decentralized communication on consensus algorithms and to validate the performance of CBBA-PR.by Noam Buckman.S.M

Multi-robot Coverage and Redeployment Algorithms

In this thesis, we focus on two classes of multi-robot task allocation and deployment problems motivated by applications in ride-sourcing transportation networks and service robots: 1) coverage control with multiple robots, and 2) robots servicing tasks arriving sequentially over time. The first problem considers the deployment of multiple robots to cover a domain. The multi-robot problem consists of multiple robots with sensors on-board observing the spatially distributed events in an environment. The objective is to maximize the sensing quality of the events via optimally distributing the robots in the environment. This problem has been studied extensively in the literature and several algorithms have been proposed for different variants of this problem. However, there has been a lack of theoretical results on the quality of the solutions provided by these algorithms. In this thesis, we provide a new distributed multi-robot coverage algorithm with theoretical guarantees on the solution quality, run-time complexity, and communication complexity. The theoretical bound on the solution quality holds for on-board sensors where the sensing quality of the sensors is a sub-additive function of the distance to the event location in convex and non-convex environments. A natural extension of the multi-robot coverage control problem is considered in this thesis where each robot is equipped with a set of different sensors and observes different event types in the environment. Servicing a task in this problem corresponds to sensing an event occurring at a particular location and does not involve visiting the task location. Each event type has a different distribution over the domain. The robots are heterogeneous in that each robot is capable of sensing a subset of the event types. The objective is to deploy the robots into the domain to maximize the total coverage of the multiple event types. We propose a new formulation for the heterogeneous coverage problem. We provide a simple distributed algorithm to maximize the coverage. Then, we extend the result to the case where the event distribution is unknown before the deployment and provide a distributed algorithm and prove the convergence of the approach to a locally optimal solution. The third problem considers the deployment of a set of autonomous robots to efficiently service tasks that arrive sequentially in an environment over time. Each task is serviced when a robot visits the corresponding task location. Robots can then redeploy while waiting for the next task to arrive. The objective is to redeploy the robots taking into account the next N task arrivals. We seek to minimize a linear combination of the expected cost to service tasks and the redeployment cost between task arrivals. In the single robot case, we propose a one-stage greedy algorithm and prove its optimality. For multiple robots, the problem is NP-hard, and we propose two constant-factor approximation algorithms, one for the problem with a horizon of two task arrivals and the other for the infinite horizon when the redeployment cost is weighted more heavily than the service cost. Finally, we extend the second problem to scenarios where the robots are self-interested service units maximizing their payoff. The payoff of a robot is a linear combination of its relocation cost and its expected revenue from servicing the tasks in its vicinity. In this extension, the global objective is either to minimize the expected time or minimize the maximum time to respond to the tasks. We introduce two indirect control methods to relocate the self-interested service units: 1) an information sharing method, and 2) a method that incentivizes relocation with payments. We prove NP-hardness of finding the optimal controls and provide algorithms to find the near-optimal control. We quantify the performance of the proposed algorithms with analytical upper-bounds and real-world data from ride-sourcing applications

Méthodologie de génération de trajets multimodaux dans un contexte de covoiturage

RÉSUMÉ Ce projet de recherche consiste à créer une méthode de génération de trajets multimodaux dans un contexte d’outil de covoiturage. À la suite d’une demande de déplacement d’un utilisateur pour se rendre d’une origine O à une destination D, le système génère un ensemble de trajets multimodaux (voiture, bus, train, métro) répondant à cette demande et se prêtant au covoiturage. Le segment routier est alors considéré comme réalisable en covoiturage. Les différents conducteurs ayant proposé leur trajet dans l’outil de covoiturage sont alors classés en fonction du détour que leur entraînerait le covoiturage et les meilleurs jumelages sont alors étudiés plus finement grâce à un calculateur de chemin pour finalement proposer à l’utilisateur les meilleures options de covoiturage. Ces options peuvent contenir des segments de transport en commun.----------ABSTRACT This research project aims to create a multimodal trips generation method in a carpooling tool context. In response to a user need to go from an origin O to a destination D, the system has to generate some multimodal trips (car, bus, trains, metro) to respond to this demand. Then, the car segment is considered to be completed by carpooling. All the drivers who have proposed their trips in the carpooling tool are ordered by the deviation which would be added if they would complete the carpooling, and the fastest among them are studied more precisely by a route planner to finally propose to the user the best carpooling options. These options can include public transportation segments

Modeling the per capita ecological footprint for Dallas County, Texas: Examining demographic, environmental value, land-use, and spatial influences

This study addresses factors driving the variation in the per capita Ecological Footprint (EF) in Dallas County, Texas. A main hypothesis was that scientifically estimated demography, environmental values, spatial attributes, and land-use patterns surrounding an individual are significant factors in the size of per capita EF. This study was based on the survey method and GIS routines. Additionally, a multiple regression method was employed to address the study question. The survey measured respondents?? EF using an ??Ecological Footprint Quiz?? consisting of sixteen questions regarding individual food, mobility, housing, and goods/services consumption. GIS technologies were used to objectively measure spatial attributes. The environmental values were measured by selected questions regarding ecological crises. This study found from the descriptive analysis that Dallas County??s average personal EF was 26.4 acres: food (5.1), mobility (3.3), shelter (8.3), and goods and services (9.8). The study indicates that the residents need ecologically productive land more than 105 times the area of the county. Based on the explanatory analysis, the following summary points can be made about the factors driving of the variance, not only in the per capita composite footprint but also in each of the personal footprint components: First, a highly educated, non-married, older male living in a high income household located in a low population density area is more likely to have a larger personal composite footprint. Second, a person with a weak environmental awareness living where the ratio of employment opportunities (places to work) is worse, and living far from freeways and major lakes but close to major malls, is more likely to have a larger personal food footprint. Third, a younger person living in a high income household located close to major malls but far from Dallas/Fort Worth Airport is more likely to have a larger mobility footprint. Fourth, a highly educated non-married older male living in a highly developed area is more likely to have a larger shelter footprint. Fifth, a highly educated non-married older male living in a high income household located in a low population density area is more likely to have a larger goods and services footprint