Networking Transportation

Networking Transportation looks at how the digital revolution is changing Greater Philadelphia's transportation system. It recognizes several key digital transportation technologies: Artificial Intelligence, Big Data, connected and automated vehicles, digital mapping, Intelligent Transportation Systems, the Internet of Things, smart cities, real-time information, transportation network companies (TNCs), unmanned aerial systems, and virtual communications. It focuses particularly on key issues surrounding TNCs. It identifies TNCs currently operating in Greater Philadelphia and reviews some of the more innovative services around the world. It presents four alternative future scenarios for their growth: Filling a Niche, A Tale of Two Regions, TNCs Take Off, and Moore Growth. It then creates a future vision for an integrated, multimodal transportation network and identifies infrastructure needs, institutional reforms, and regulatory recommendations intended to help bring about this vision

Divergent Paths: An Analysis of the Autonomous Future in McLean County

Autonomous vehicles (AVs) are expected to arrive on public roads in the mid-term future, but will vary in their uses and level of self-driving capabilities. On the heels of the rise of shared mobility services from transportation network companies like Uber and Lyft, the combination of these technologies has generated the anticipation of a diminishing need for private car ownership. The promises of when AVs will arrive has been somewhat tempered in recent years, allowing the public and stakeholders valuable time to more adequately plan for their arrival. A yet undetermined outcome is the influence these new technologies will have on traveler behavior, which impacts nearly every aspect of transportation planning. This report highlights two divergent paths that the autonomous future is likely to usher in: One scenario is marked by a new mobility ecosystem which enables people and things to move faster, cleaner, cheaper, and safer than today. The other possibility is that the autonomous future is marked by a decrease in overall safety, increased congestion, abandonment of public transport systems, lack of privacy, and transportation deserts. Which of these futures comes to fruition is dependent on various competing forces from public entities and the private sector. This discussion aims to provide a ten-thousand-foot view of the myriad of changes that self-driving vehicles are likely to generate. This report was written for multiple purposes, both for the formal needs of the McLean County Regional Planning Commission (MCRPC), as well as a brief introduction for Bloomington-Normal-McLean County stakeholders to start planning for the autonomous future. The author hopes it will be utilized as a resource for ongoing intergovernmental discussion of smart cities, intelligent transportation systems, and public technology currently being conducted by MCRPC and local governments. In addition, it will serve as a supplement to the 2045 Long Range Metropolitan Transportation Plan for the Bloomington-Normal urbanized area

Operations Management in the Sharing Economy: Essays on the Integrated Perspective of Item-sharing and Crowdshipping

The sharing economy subsumes various concepts in which members of a sharing community render services to each other. Two of these concepts are item-sharing and crowdshipping. In item-sharing, members grant others a temporary access to the items they own, such as tools or leisure equipment. In crowdshipping, private drivers offer to execute delivery jobs for other people on trips they would make anyway. These two concepts are so far dealt with in practice on separate platforms that operate in complete isolation. However, they could complement each other well when the available transportation capacity in crowdshipping is utilized to support the cumbersome peer-to-peer item exchanges in item-sharing. The goal of this thesis is to investigate possible benefits of integrating item-sharing and crowdshipping on a single platform. To this end, we address the operations management of such a platform and we derive managerial insights from numerical experiments. The thesis is a cumulative dissertation that is mainly composed of four individual essays. The first essay analyses the potential of a concept integration for a basic scenario. The benefit of having crowdshippers execute more than one delivery per trip is investigated in the second essay. In the third essay, we propose an innovative concept in which the shared items are forwarded from one consumer directly to the next. The work concludes with a sustainability comparison between alternative ways of serving requests. The results show strong evidence that the two concepts are mutually beneficial and that a well-designed operations management allows to effectively serve total demand with the available items. Furthermore, it is demonstrated in the fourth essay that a shared use of items can lower the environmental impact of consumption substantially

Algorithme de jumelage multimodal pour le covoiturage

RÉSUMÉ : Le covoiturage multimodal urbain est une solution économique pour réduire les émissions de gaz à effet de serre dans les villes. Le but du projet présenté dans ce mémoire est de modéliser et d’implémenter 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’être rapide et d’offrir des jumelages de qualité, en termes de détour acceptable et de respect des horaires. Il a également pour ambition de coupler le covoiturage avec les transports en commun. Ce projet est en partenariat avec Netlift, startup Montréalaise, ainsi qu’avec un autre étudiant en maîtrise recherche au département de Génie Civil de l’École Polytechnique de Montréal, qui travaille principalement sur les données utilisées. Les objectifs de ce mémoire sont multiples. Le premier consiste à construire une structure de données permettant de modéliser la ville de Montréal et de calculer des temps de parcours. Ceci permettrait de comparer les différents trajets des utilisateurs. Aussi, cette structure de données doit permettre le calcul d’itinéraires multimodaux, auto et transports en commun combinés. Le second objectif est de modéliser et d’implémenter en JAVA un algorithme de jumelage passagers/conducteurs pour le covoiturage dit « classique » (auto uniquement) et pour le covoiturage multimodal. Une revue de littérature a permis de diriger les travaux à mener. Ce travail est présenté dans le premier chapitre. Après une brève synthèse des concepts relatifs au covoiturage, une classification des systèmes et algorithmes existants permet d’amener différentes conclusions quant à la structure de données à implémenter, sur laquelle s’appuie l’algorithme envisagé. Elle doit permettre d’accéder rapidement aux données nécessaires à l’obtention de jumelages pour un passager donné. La structure du reste du mémoire est influencée par la chronologie du projet : la définition du besoin et les objectifs à atteindre ont été définis au fur et à mesure avec Netlift et les différents collaborateurs. Le second chapitre du corps du mémoire concerne les premières avancées menées en parallèle de la définition du besoin, tandis que le troisième chapitre décrit l’algorithme et la structure de données retenus pour satisfaire les objectifs fixés. Le quatrième et dernier chapitre présente 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 différentes régressions linéaires. Ce sont ces recherches préalables qui ont conduit à l’élaboration d’une structure de données plus complexe, présenté dans le troisième chapitre, qui fait appel aux concepts de la théorie des graphes. L’algorithme développé dans cette partie fait notamment appel à des calculs de plus courts chemins. Il permet de trier une liste de conducteurs pour un passager donné en fonction de leur potentiel de covoiturage – notion qui sera expliquée en détail. Son évaluation est réalisée à l’aide de différentes métriques relatives aux données fournies par Netlift (jumelages trouvés par leur algorithme) et aux données de l’enquête Origine-Destination de Montréal pour l’année 2008. Les résultats sont satisfaisants pour le covoiturage classique (sans transports en commun) puisque l’algorithme implémenté réussit à fournir rapidement des covoitureurs de bonne qualité pour une grande partie des utilisateurs. Parmi les passagers des données de l’enquête Origine-Destination, plus d’un passager sur deux possède un conducteur qui peut covoiturer avec lui pour des détours de 10min maximum. Le potentiel du covoiturage multimodal pour la période de pointe du matin est évalué grâce à une étude des trajets de l’enquête OD de 2008. Les jumelages obtenus sont moins bons que pour le covoiturage classique, mais la méthode employée présente une marge d’amélioration et une perspective de recherche future. Ce projet permet à Netlift de gagner en pertinence et en rapidité par rapport aux jumelages proposés 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

Ridesourcing and the Taxi Marketplace

Thesis advisor: Joseph QuinnThe creation of ridesourcing firms Uber and Lyft greatly disrupted the taxicab marketplace in the United States over the past four years. By examining the taxicab marketplace, as well as the ridesourcing firm’s aspects of creative destruction, the marketplace’s drastic changes become apparent. Thus, 21st century technology disrupts the marketplace, and creates a real time market based on supply and demand factors. Furthermore, disruption impacts all actors within the previous taxicab marketplace as well as the newly created ridesourcing marketplace; therefore, ridesourcing’s widespread effects are examined in detail.Thesis (BS) — Boston College, 2015.Submitted to: Boston College. College of Arts and Sciences.Discipline: Departmental Honors.Discipline: Economics

Ten Year Planning Report 2009-2018

https://digitalmaine.com/mta_docs/1001/thumbnail.jp

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

An Inquiry into Supply Chain Strategy Implications of the Sharing Economy for Last Mile Logistics

As the prevalence of e-commerce and subsequent importance of effective and efficient omnichannel logistics strategies continues to rise, retail firms are exploring the viability of sourcing logistics capabilities from the sharing economy. Questions arise such as, “how can crowdbased logistics solutions such as crowdsourced logistics (CSL), crowdshipping, and pickup point networks (PPN) be leveraged to increase performance?” In this dissertation, empirical and analytical research is conducted that increases understanding of how firms can leverage the sharing economy to increase logistics and supply chain performance. Essay 1 explores crowdsourced logistics (CSL) by employing a stochastic discrete event simulation set in New York City in which a retail firm sources drivers from the crowd to perform same day deliveries under dynamic market conditions. Essay 2 employs a design science paradigm to develop a typology of crowdbased logistics strategies using two qualitative methodologies: web content analysis and Delphi surveys. A service-dominant logic theoretical perspective guides this essay and explains how firms co-create value with the crowd and consumer markets while presenting a generic design for integrating crowdbased models into logistics strategy. In Essay 3, a crowdsourced logistics strategy for home delivery is modeled in an empirically grounded simulation optimization to explore the logistics cost and responsiveness implications of sharing economy solutions on omnichannel fulfillment strategies