7 research outputs found

    Enhancing Urban Mobility: Integrating Ride-sharing and Public Transit

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    Seamless integration of ride-sharing and public transit may offer fast, reliable, and affordable transfer to and from transit stations in suburban areas thereby enhancing mobility of residents. We investigate the potential benefits of such a system, as well as the ride-matching technology required to support it, by means of an extensive computational study

    A Tabu Search Based Metaheuristic for Dynamic Carpooling Optimization

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    International audienceThe carpooling problem consists in matching a set of riders' requests with a set of drivers' offers by synchronizing their origins, destinations and time windows. The paper presents the so-called Dynamic Carpooling Optimization System (DyCOS), a system which supports the automatic and optimal ridematching process between users on very short notice or even en-route. Nowadays, there are numerous research contributions that revolve around the carpooling problem, notably in the dynamic context. However, the problem's high complexity and the real time aspect are still challenges to overcome when addressing dynamic carpooling. To counter these issues, DyCOS takes decisions using a novel Tabu Search based metaheuristic. The proposed algorithm employs an explicit memory system and several original searching strategies developed to make optimal decisions automatically. To increase users' satisfaction, the proposed metaheuristic approach manages the transfer process and includes the possibility to drop off the passenger at a given walking distance from his destination or at a transfer node. In addition, the detour concept is used as an original aspiration process, to avoid the entrapment by local solutions and improve the generated solution. For a rigorous assessment of generated solutions , while considering the importance and interaction among the optimization criteria, the algorithm adopts the Choquet integral operator as an aggregation approach. To measure the effectiveness of the proposed method, we develop a simulation environment based on actual carpooling demand data from the metropolitan area of Lille in the north of France

    Algorithme de jumelage multimodal pour le covoiturage

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    RÉSUMÉ : Le covoiturage multimodal urbain est une solution Ă©conomique pour rĂ©duire les Ă©missions de gaz Ă  effet de serre dans les villes. Le but du projet prĂ©sentĂ© dans ce mĂ©moire est de modĂ©liser et d’implĂ©menter un algorithme de jumelage multimodal permettant de mettre en relation conducteurs et passagers pour effectuer des trajets quotidiens en zone urbaine. Cet algorithme a pour vocation d’ĂȘtre rapide et d’offrir des jumelages de qualitĂ©, en termes de dĂ©tour acceptable et de respect des horaires. Il a Ă©galement pour ambition de coupler le covoiturage avec les transports en commun. Ce projet est en partenariat avec Netlift, startup MontrĂ©alaise, ainsi qu’avec un autre Ă©tudiant en maĂźtrise recherche au dĂ©partement de GĂ©nie Civil de l’École Polytechnique de MontrĂ©al, qui travaille principalement sur les donnĂ©es utilisĂ©es. Les objectifs de ce mĂ©moire sont multiples. Le premier consiste Ă  construire une structure de donnĂ©es permettant de modĂ©liser la ville de MontrĂ©al et de calculer des temps de parcours. Ceci permettrait de comparer les diffĂ©rents trajets des utilisateurs. Aussi, cette structure de donnĂ©es doit permettre le calcul d’itinĂ©raires multimodaux, auto et transports en commun combinĂ©s. Le second objectif est de modĂ©liser et d’implĂ©menter en JAVA un algorithme de jumelage passagers/conducteurs pour le covoiturage dit « classique » (auto uniquement) et pour le covoiturage multimodal. Une revue de littĂ©rature a permis de diriger les travaux Ă  mener. Ce travail est prĂ©sentĂ© dans le premier chapitre. AprĂšs une brĂšve synthĂšse des concepts relatifs au covoiturage, une classification des systĂšmes et algorithmes existants permet d’amener diffĂ©rentes conclusions quant Ă  la structure de donnĂ©es Ă  implĂ©menter, sur laquelle s’appuie l’algorithme envisagĂ©. Elle doit permettre d’accĂ©der rapidement aux donnĂ©es nĂ©cessaires Ă  l’obtention de jumelages pour un passager donnĂ©. La structure du reste du mĂ©moire est influencĂ©e par la chronologie du projet : la dĂ©finition du besoin et les objectifs Ă  atteindre ont Ă©tĂ© dĂ©finis au fur et Ă  mesure avec Netlift et les diffĂ©rents collaborateurs. Le second chapitre du corps du mĂ©moire concerne les premiĂšres avancĂ©es menĂ©es en parallĂšle de la dĂ©finition du besoin, tandis que le troisiĂšme chapitre dĂ©crit l’algorithme et la structure de donnĂ©es retenus pour satisfaire les objectifs fixĂ©s. Le quatriĂšme et dernier chapitre prĂ©sente les conclusions et les perspectives de recherche. Dans le second chapitre, on essaye d’établir des indicateurs de potentiel de covoiturage au moyen d’un score et de diffĂ©rentes rĂ©gressions linĂ©aires. Ce sont ces recherches prĂ©alables qui ont conduit Ă  l’élaboration d’une structure de donnĂ©es plus complexe, prĂ©sentĂ© dans le troisiĂšme chapitre, qui fait appel aux concepts de la thĂ©orie des graphes. L’algorithme dĂ©veloppĂ© dans cette partie fait notamment appel Ă  des calculs de plus courts chemins. Il permet de trier une liste de conducteurs pour un passager donnĂ© en fonction de leur potentiel de covoiturage – notion qui sera expliquĂ©e en dĂ©tail. Son Ă©valuation est rĂ©alisĂ©e Ă  l’aide de diffĂ©rentes mĂ©triques relatives aux donnĂ©es fournies par Netlift (jumelages trouvĂ©s par leur algorithme) et aux donnĂ©es de l’enquĂȘte Origine-Destination de MontrĂ©al pour l’annĂ©e 2008. Les rĂ©sultats sont satisfaisants pour le covoiturage classique (sans transports en commun) puisque l’algorithme implĂ©mentĂ© rĂ©ussit Ă  fournir rapidement des covoitureurs de bonne qualitĂ© pour une grande partie des utilisateurs. Parmi les passagers des donnĂ©es de l’enquĂȘte Origine-Destination, plus d’un passager sur deux possĂšde un conducteur qui peut covoiturer avec lui pour des dĂ©tours de 10min maximum. Le potentiel du covoiturage multimodal pour la pĂ©riode de pointe du matin est Ă©valuĂ© grĂące Ă  une Ă©tude des trajets de l’enquĂȘte OD de 2008. Les jumelages obtenus sont moins bons que pour le covoiturage classique, mais la mĂ©thode employĂ©e prĂ©sente une marge d’amĂ©lioration et une perspective de recherche future. Ce projet permet Ă  Netlift de gagner en pertinence et en rapiditĂ© par rapport aux jumelages proposĂ©s dans leur application actuelle.----------ABSTRACT : Urban multimodal ridesharing is an economical way to reduce greenhouse gases emissions in cities. The goal of the project presented in this thesis is to modelise and implement a multimodal matching algorithm able to match drivers and passengers for everyday short ridesharing. This algorithm aims to be fast and to offer precise matches regarding acceptable detour and schedule respect. It also tries to mix ridesharing with public transportation. This project is led in partnership with Netlift, a Montreal startup, an another master’s student linked to the Civil Engineering department of Polytechnique Montreal, working especially on data. Multiple objectives are targeted in this thesis. The first one consists of making a data structure representing Montreal and enabling travelling time calculation. This could lead to compare user’s paths. Multimodal paths need also to be calculated thanks to this data structure. The second objective is to modelise and implement in JAVA a matching algorithm between riders and drivers for « classic » ridesharing (only car) and multimodal ridesharing (car and public transportation). In the first part of this thesis, a litterature review has been conducted in order to guide the goals to achieve. After a short synthesis of ridesharing concepts, a classification of existing articles about ridesharing leads to conclusions related to the data structure to implement. A need of speed is necessary to propose matched drivers to a given passenger. The structure of this thesis is affected by the chronology of the project : the requirements definition and goals to achieve have been precised all along with Netlift and the other partners. The second chapter of the thesis deals with the initial steps conducted at the same time than the requirements definition. The third chapter describes then the data structure and algorithm selected to achieve goals. In the second chapter, ridesharing potential is represented by two differents indicators : a score and different linear regressions. These preliminary searches led to the development of a data structure more complex, presented in the third chapter. Graph theory is central in this chapter. The final algorithm particularly uses shortest path calculation. It sorts a list of drivers for a given rider according to their ridesharing potential. A dedicated section of the thesis details this notion. The algorithm is evaluated thanks to different metrics related to Netlift data (found matches by Netlift algorithm) and the Origin-Destination Survey of Montreal conducted in 2008. The results are satisfying for classic ridesharing (without public transportation) since the implemented algorithm succeeds to give good drivers for a big amount of passengers fastly. More than one out of two among riders from the OD Survey has a driver to share the ride with for detours less than 10 minuts. The multimodal ridesharing potential for the morning peak period is evaluated by a study of rides from the Montreal 2008 OD Survey. Obtained matches lack of quality compared to classic ridesharing, but the used method deserves improvements and a perspective of future research. This project enables Netlift to gain in relevance and computation speed against the matches proposed by the current algorithm

    Méthodologie de génération de trajets multimodaux dans un contexte de covoiturage

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    RÉSUMÉ Ce projet de recherche consiste Ă  crĂ©er une mĂ©thode de gĂ©nĂ©ration de trajets multimodaux dans un contexte d’outil de covoiturage. À la suite d’une demande de dĂ©placement d’un utilisateur pour se rendre d’une origine O Ă  une destination D, le systĂšme gĂ©nĂšre un ensemble de trajets multimodaux (voiture, bus, train, mĂ©tro) rĂ©pondant Ă  cette demande et se prĂȘtant au covoiturage. Le segment routier est alors considĂ©rĂ© comme rĂ©alisable en covoiturage. Les diffĂ©rents conducteurs ayant proposĂ© leur trajet dans l’outil de covoiturage sont alors classĂ©s en fonction du dĂ©tour que leur entraĂźnerait le covoiturage et les meilleurs jumelages sont alors Ă©tudiĂ©s plus finement grĂące Ă  un calculateur de chemin pour finalement proposer Ă  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

    Vehicle dispatch in high-capacity shared autonomous mobility-on-demand systems

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    Ride-sharing is a promising solution for transportation issues such as traffic congestion and parking land use, which are brought about by the extensive usage of private vehicles. In the near future, large-scale Shared Autonomous Mobility-on-Demand (SAMoD) systems are expected to be deployed with the realization of self-driving vehicles. It has the potential to encourage a car-free lifestyle and create a new urban mobility mode where ride-sharing is widely adopted among people. This thesis addresses the problem of improving the efficiency and quality of vehicle dispatch in high-capacity SAMoD systems. The first part of the thesis develops a dispatcher which can efficiently explore the complete candidate match space and produce the optimal assignment policy when only deterministic information is concerned. It uses an incremental search method that can quickly prune out infeasible candidates to reduce the search space. It also has an iterative re-optimization strategy to dynamically alter the assignment policy to take into account both previous and newly revealed requests. Case studies of New York City using real-world data shows that it outperforms the state-of-the-art in terms of service rate and system scalability. The dispatcher developed in this part can serve as a foundation for the next two parts, which consider two kinds of uncertain information, stochastic travel times and the dynamic distribution of requests in the long-term future, respectively. The second part of the thesis describes a framework which makes use of stochastic travel time models to optimize the reliability of vehicle dispatch. It employs a candidate match search method to generate a candidate pool, uses a set of preprocessed shortest path tables to score the candidates and provides an assignment policy that maximizes the overall score. Two different dispatch objectives are discussed: the on-time arrival probabilities of requests and the proïŹt of the platform. Experimental studies show that higher service rates, reliability and profits can be achieved by considering travel time uncertainty. The third part of the thesis presents a deep reinforcement learning based approach to optimize assignment polices in a more far-sighted way. It models the vehicle dispatch problem as a Markov Decision Process (MDP) and uses a policy evaluation method to learn a value function from the historic movements of drivers. The learned value function is employed to score candidate matches to guide a dispatcher optimizing long-term objective, and will be continually updated online to capture the real-time dynamics of the system. It is shown by experiments that the value function helps the dispatcher to yield higher service rates

    Shared Mobility - Operations and Economics

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    In the last decade, ubiquity of the internet and proliferation of smart personal devices have given rise to businesses that are built on the foundation of the sharing economy. The mobility market has implemented the sharing economy model in many forms, including but not limited to, carsharing, ride-sourcing, carpooling, taxi-sharing, ridesharing, bikesharing, and scooter sharing. Among these shared-use mobility services, ridesharing services, such as peer-to-peer (P2P) ridesharing and ride-pooling systems, are based on sharing both the vehicle and the ride between users, offering several individual and societal benefits. Despite these benefits, there are a number of operational and economic challenges that hinder the adoption of various forms of ridesharing services in practice. This dissertation attempts to address these challenges by investigating these systems from two different, but related, perspectives. The successful operation of ridesharing services in practice requires solving large-scale ride-matching problems in short periods of time. However, the high computational complexity and inherent supply and demand uncertainty present in these problems immensely undermines their real-time application. In the first part of this dissertation, we develop techniques that provide high-quality, although not necessarily optimal, system-level solutions that can be applied in real time. More precisely, we propose a distributed optimization technique based on graph partitioning to facilitate the implementation of dynamic P2P ridesharing systems in densely populated metropolitan areas. Additionally, we combine the proposed partitioning algorithm with a new local search algorithm to design a proactive framework that exploits historical demand data to optimize dynamic dispatching of a fleet of vehicles that serve on-demand ride requests. The main purpose of these methods is to maximize the social welfare of the corresponding ridesharing services. Despite the necessity of developing real-time algorithmic tools for operation of ridesharing services, solely maximizing the system-level social welfare cannot result in increasing the penetration of shared mobility services. This fact motivated the second stream of research in this dissertation, which revolves around proposing models that take economic aspects of ridesharing systems into account. To this end, the second part of this dissertation studies the impact of subsidy allocation on achieving and maintaining a critical mass of users in P2P ridesharing systems under different assumptions. First, we consider a community-based ridesharing system with ride-back guarantee, and propose a traveler incentive program that allocates subsidies to a carefully selected set of commuters to change their travel behavior, and thereby, increase the likelihood of finding more compatible and profitable matches. We further introduce an approximate algorithm to solve large-scale instances of this problem efficiently. In a subsequent study for a cooperative ridesharing market with role flexibility, we show that there may be no stable outcome (a collusion-free pricing and allocation scheme). Hence, we introduced a mathematical formulation that yields a stable outcome by allocating the minimum amount of external subsidy. Finally, we propose a truthful subsidy scheme to determine matching, scheduling, and subsidy allocation in a P2P ridesharing market with incomplete information and a budget constraint on payment deficit. The proposed mechanism is shown to guarantee important economic properties such as dominant-strategy incentive compatibility, individual rationality, budget-balance, and computational efficiency. Although the majority of the work in this dissertation focuses on ridesharing services, the presented methodologies can be easily generalized to tackle related issues in other types of shared-use mobility services.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169843/1/atafresh_1.pd
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