Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

Human versus automated agents: how user preferences affect future mobility systems

Along with rapid advancements in digital, and physical technologies, shared autonomous electric vehicles are forecasted to gradually complement and replace traditional human-based mobility systems. Information systems play a key role in such a deep socio-technical system to pave the path toward a more sustainable future. This study investigates a hybrid ride-hailing platform of automated and human-driven vehicles. Our focus lies on the demand side where we evaluate the influence of user behaviors on economic and environmental system performance. For this, we employ a data-driven agent-based simulation modeling heterogeneous vehicle and user agents calibrated by rental data of a leading vehicle-sharing company. Our findings declare that diverse customer responses to the introduction of shared autonomous electric vehicles yield significantly different fleet performance and ecological costs. We also observe that the status quo customer communication design of ride-hailing platforms need adjustments to maximize the potentials of future hybrid shared mobility systems

Effectiveness of smart charging of electric vehicles under power limitations

This article investigates charging strategies for plug-in hybrid electric vehicles (PHEV) as part of the energy system. The objective was to increase the combined all-electric mileage (total distance driven using only the traction batteries in each PHEV) when the total charging power at each workplace is subject to severe limitations imposed by the energy system. In order to allocate this power optimally, different input variables, such as state-of-charge, battery size, travel distance, and parking time, were considered. The required vehicle mobility was generated using a novel agent-based model that describes the spatiotemporal movement of individual PHEVs. The results show that, in the case of Helsinki (Finland), smart control strategies could lead to an increase of over 5% in the all-electric mileage compared to a no-control strategy. With a high prediction error, or with a particularly small or large battery, the benefits of smart charging fade off. Smart PHEV charging strategies, when applied to the optimal allocation of limited charging power between the cars of a vehicle fleet, seem counterintuitively to provide only a modest increase in the all-electric mileage. A simple charging strategy based on allocating power to PHEVs equally could thus perform sufficiently well. This finding may be important for the future planning of smart grids as limiting the charging power of larger PHEV fleets will sometimes be necessary as a result of grid restrictions.Peer reviewe

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs

Integrated modelling framework for the analysis of demand side management strategies in urban energy systems

Influenced by environmental concerns and rapid urbanisation, cities are changing the way they historically have produced, distributed and consumed energy. In the next decades, cities will have to increasingly adapt their energy infrastructure if new low carbon and smart technologies are to be effectively integrated. In this context, advanced planning tools can become crucial to successfully design these future urban energy systems. However, it is not only important to analyse how urban energy infrastructure will look like in the future, but also how they will be operated. Advanced energy management strategies can increase the operational efficiency, therefore reducing energy consumption, CO2 emissions, operational costs and network investments. However, the design and analysis of these energy management strategies are difficult to perform at an urban scale considering the spatial and temporal resolution and the diversity in users energy requirements. This thesis proposes a novel integrated modelling framework to analyse flexible transport and heating energy demand and assess different demand-side management strategies in urban energy systems. With a combination of agent-based simulation and multi-objective optimisation models, this framework is tested using two case studies. The first one focuses on transport electrification and the integration of electric vehicles through smart charging strategies in an urban area in London, UK. The results of this analysis show that final consumer costs and carbon emissions reductions (compared to a base case) are in the range of 4.3-45.0% and 2.8-3.9% respectively in a daily basis, depending on the type of tariff and electricity generation mix considered. These reductions consider a control strategy where the peak demand is constrained so the capacity of the system is not affected. In the second case study, focused on heat electrification, the coordination of a group of heat pumps is analysed, using different scheduling strategies. In this case, final consumer costs and carbon emissions can be reduced in the range of 4-41% and 0.02-0.7% respectively on a daily basis. In this case, peak demand can be reduced in the range of 51-62% with respect to the baseline. These case studies highlight the importance of the spatial and temporal characterisation of the energy demand, and the level of flexibility users can provide to the system when considering a heterogeneous set of users with different technologies, energy requirements and behaviours. In both studies, trade-offs between the environmental and economic performance of demand-side management strategies are assessed using a multi-objective optimisation approach. Finally, further applications of the integrated modelling framework are described to highlight its potential as a decision-making support tool in sustainable and smart urban energy systems.Open Acces

Modélisation des membres et de leur comportement dans un écosystème de services d’autopartage à Montréal

RÉSUMÉ: Cette thèse s’inscrit dans un effort de modélisation du comportement des usagers de l’autopartage appliquée au cas de Montréal. Communauto, opérateur d’autopartage basé stations depuis les années 90, a procédé à l’intégration d’un service d’autopartage en libre-service intégral en 2013 pour son marché de Montréal. Un des aspects différenciateurs de l’autopartage en libre-service intégral, comparé à la formé basée stations, est sa capacité d’effectuer des emprunts sans retour à l’origine. Avec une structure tarifaire et des politiques opérationnelles encourageant l’utilisation des deux modes, le cas de Montréal est particulier étant donné que les deux services sont interdépendants, ce qui crée la notion d’écosystème de services d’autopartage. Ayant un service basé stations plutôt mature dans la région, le nouveau libre-service intégral a quant à lui connu plusieurs phases d’expansion. Débutant avec une flotte de 24 véhicules électriques et une zone de service couvrant essentiellement le Plateau-Mont-Royal, le service appelé Auto-mobile offre aujourd’hui plus de 600 véhicules et couvre une zone de plus de 100 km2. La croissance importante de la popularité de l’autopartage ces dernières années, particulièrement au niveau des services en sens unique (dont le libre-service intégral), ainsi que les caractéristiques du marché de Montréal créent un environnement propice à la recherche sur le comportement des membres. La venue des services en sens unique a incité la littérature à se tourner vers de nouveaux créneaux de recherche, mais également à revoir les attributs associés à l’autopartage basé stations afin d’assurer leur transférabilité vers l’autopartage en libre-service intégral. Donc, dans un contexte où l’objectif structurant de cette thèse est la modélisation du comportement des membres dans un écosystème de services d’autopartage à Montréal, cinq perspectives sont considérées afin de contribuer à l’objectif principal, sous des angles empiriques, méthodologiques et stratégiques. Pour ce faire, le système d’information est composé de données d’enquêtes de type origine-destination ainsi que de données passives (transactionnelles, géolocalisées, GPS, capturées). La première perspective traite de la comparaison entre les membres de l’autopartage et du vélopartage. Exploitant des données d’enquêtes de l’automne 2013, les membres des deux services sont comparés quant à leurs caractéristiques sociodémographiques, de ménage et de comportement de mobilité. L’utilisation de données transactionnelles provenant des opérateurs Bixi et Communauto permet de segmenter les membres selon leur intensité d’usage du service. Entre autres, les résultats montrent que les membres de l’autopartage basé stations ont un niveau de possession automobile inférieur à celui des membres du vélopartage et qu’en plus, ils intègrent davantage le transport en commun et les modes actifs dans leur mobilité. La seconde perspective met en lumière les différences d’utilisation entre véhicules d’une flotte mixte, c’est-à-dire où des véhicules électriques et conventionnels hybrides sont offerts aux membres. À l’aide de données GPS et transactionnelles, deux analyses descriptives sont conduites. La première compare les distributions des deux types de véhicules selon la distance, tandis que l’autre observe l’utilisation spatiale des véhicules. Dans le premier cas, une forte baisse de l’utilisation des véhicules électriques pour des distances de plus de 24 km est observée. Cette constatation s’est transposée dans l’analyse spatiale où l’espace d’activité est inférieur pour les véhicules électriques. Finalement, un modèle de régression logistique est estimé. Ce modèle prend comme variable dépendante le type de véhicule utilisé dans une situation où le membre est confronté à un choix, c’est-à-dire lorsque les deux types de véhicules sont disponibles à moins de 100 mètres l’un de l’autre. La température ambiante (froid), le genre (femme), le niveau de charge du véhicule (faible) ainsi qu’une forte distance de parcours jouent un rôle inhibiteur quant au choix d’emprunt d’un véhicule électrique. En troisième lieu, la perspective stratégique de l’adoption du service par les membres est étudiée. Étant donné la disponibilité des deux services, la dynamique d’adoption par les membres n’est pas triviale. Cette perspective vise à offrir un moyen aux opérateurs, désirant intégrer un nouveau service de type libre-service intégral à leur offre basée stations actuelle, de comprendre la dynamique d’adoption et d’estimer globalement son ampleur. D’abord, une classification des membres est proposée afin de prendre en compte le lieu du domicile du membre, son type d’abonnement, son expérience antérieure avec le service et la date à laquelle il adopte le service. L’adoption est par la suite caractérisée de façon longitudinale au niveau du système, mais également selon la zone d’expansion. Afin de comparer de façon valide les différentes zones entres elles, un exercice de normalisation a eu lieu. Finalement, les données du marché de la ville de Québec sont utilisées afin de comparer le niveau d’adoption à celui de Montréal. Quoique le niveau d’adoption normalisé entre les deux marchés est similaire, l’adoption des nouveaux membres du libre-service intégral est inférieur à celui de Montréal. Un modèle imbriqué en deux temps est proposé à la fin du chapitre. La quatrième perspective se penche sur le comportement spatio-longitudinal des membres. En effet, étant donné le faible niveau de maturité sur la question, les membres sont évalués selon leur utilisation spatiale. En premier, la relation entre les extrémités des emprunts et le domicile du membre est analysée. Puis, l’analyse est reprise, mais cette fois entre les extrémités d’emprunts et les stations de métro afin d’évaluer le potentiel du service à connecter ses usagers au métro. L’enchaînement des emprunts est ensuite examiné avant de terminer sur le rythme de découverte du service (représenté par l’aire occupée par l’agrégation des origines et destinations) et de la récurrence spatiale des lieux d’activités des membres. Plusieurs résultats sont alors exposés, mais la présence d’emprunts symétriques pour la classe de membres utilisant le plus le service retient davantage l’attention. De plus, l’analyse des temps d’activités entre deux emprunts symétriques montre que seuls les membres ayant la plus grande utilisation du libre-service intégral affichent des durées se rapprochant de celles enregistrées pour motif travail. La dernière perspective se concentre à développer une méthode capable d’exploiter correctement les emprunts déduits d’une capture successive des positions des véhicules en libre-service sur un site Internet. Étant donné la disponibilité publique de l’information sur ces véhicules partagés, plusieurs méthodes différentes ont été recensées dans la littérature afin d’exploiter ces données. Celles-ci n’ont toutefois pas été validées à ce jour. Donc, en ayant accès à trois sources de données passives, une méthode en quatre étapes est proposée. De façon sommaire, les données GPS sont employées pour caractériser les emprunts, les données géolocalisées pour modéliser le comportement de réservation a priori d’un véhicule et les données capturées pour être catégorisées à l’aide du modèle multi-logit développé. Les connaissances sur le comportement de l’usager déduites des travaux de recherche de cette thèse ont permis de mieux comprendre la dynamique d’usage des deux systèmes à l’intérieur de l’écosystème d’autopartage de Communauto. Par contre, les connaissances du domaine sont à un niveau où plusieurs questions restent en suspens et donc de futurs travaux sur la question doivent être entrepris. Notamment, il est encouragé d’évaluer les questionnements stratégiques comme les effets qu’apporte un triplet d’offre (densité de véhicules / configuration de la zone de service / politiques opérationnelles et tarifaires) sur le comportement des membres et ce, tout en gardant une perspective d’expansion de service. Également, les questions traitant des impacts et bénéfices traditionnellement explorés pour l’autopartage basé stations doivent être explorés, mais selon un regard qui prend en compte l’interrelation entre les deux services et non pas selon une approche dichotomique traditionnelle.---------- ABSTRACT: This thesis aims to contribute to carsharing user behaviour body of knowledge by investigating the Montreal case. Communauto, the oldest carsharing operator in North America, proceeded to add a free-floating service to its established station-based solution in 2013. As station-based carsharing is the oldest form of carsharing and the most popular one, one-way solutions as free-floating carsharing stormed the market in the last decade. Those carsharing schemes allow members to perform one-way trips, thus increasing the flexibility of the service. The Montreal market, with both station-based and free-floating solutions integrated under the same operator, has created a unique carsharing ecosystem where both services are interrelated and where members enjoy an increased value proposition. Albeit the fact the station-based service has been pretty well established, the free-floating service, labelled under the name Auto-mobile, started in modest ways with only 24 all-electric vehicles and a service area that covers essentially the Plateau-Mont-Royal borough. Five years later, the service has grown substantially with more than 600 shared vehicles with a service area size of more than 100 km2. Both the increased worldwide carsharing popularity and the Montreal integrated carsharing ecosystem provide unique research opportunities on user behaviour. With the increase of one-way schemes, new field of studies came to light, while fields heavily studied under the station-based paradigm are now meant to be looked over again throught the lense of this new service. Thus, in a context where the main research objective of this thesis has been the modelling of the user behaviour under a unified carsharing ecosystem, five main perspectives have been investigated to contribute to the body of knowledge. To do so, data from origin-destination surveys and passive data streams (transactional, GPS, geo-coded) coming from the carsharing operator Communauto have been used. The first perspective investigates differences between station-based carsharing and bikesharing members. Leveraging two origin-destination travel surveys, members from both services are compared with respect to their socio-demographic features, but also in regards to their household and general mobility behaviours. The use of transactional data from both Communauto and Bixi operators allows to cluster respondents according to their intensity of use. Amongst the results, carsharing members showed a lower car ownership in addition to performing more transit and active trips than bikesharing members. The second perspective compares the differences between electric and hybrid car use inside a mixed free-floating car fleet. With the help of transactional and GPS data, descriptive statistiques are estimated. First, the differences in use between both vehicle types, according to the travelled distance, show a reduction in electric car use for distances above 24 km. On spatial dispersion, activities performed by electric cars are less inclined to be made outside of the service area and also create smaller standard deviational ellipses which means they are more concentrated. Finally, a logit regression model is estimated with the chosen vehicle type as dependent variable in situations where the member has a choice. Cold temperatures, gender (females), car level of charge (low) and expected travel distance (high) are factors lowering the odds ratio to select an electric vehicle. The third perspective investigates the adoption dimension. Being a strategic component for operators, the implementation of a free-floating service in parallel to a station-based service is not trivial. First, a classification of all members is performed to better understand the adoption process. Adoption level is then presented in a longitudinal way showing differences between user types and similarities between zones. Finally, data from the Quebec City market is used to compare the adoption level from both cities. While the adoption is similar, new free-floating member adoption is somewhat underperforming. Potential model components are then suggested to tackle the adoption dimension in regard to insights developed in the study. The fourth perspective aims to better understand the spatio-longitudinal behaviours in a free-floating setting. First, both trip ends are looked over to investigate their relation with the members’ home location. Then, the same analysis is picked up, but this time to evaluate the potential usage of the free-floating service to reach to the metro station network. The proportion of symmetric trips in the system is evaluated. Results show that a significant portion of those trips are being made by members with highest levels of service usage. When looking at the inferred activity duration, symmetric trips are being made for shopping/leisure purposes, and also for commuting but only amongst the highest usage class of users. Finally, members’ service exposure is analyzed. Results show a constant increase in the area covered by trip ends with a slight decay (after a few periods) in the rate at which a member increases its overall service exposure. The last perspective proposes a new method to assess one-way trips in a free-floating carsharing setting based on a limited web-based harvested dataset. With the need for a member to locate an available shared vehicle, operators disclose vehicle position in real-time. Those car positions are then harvested in a continuous way to deduce trips made in the system. Split in four steps, the proposed method uses GPS, geo-coded and harvested passive data streams. In all, the multi-logit model allows to classify harvested trips into one-way trips, round-trips and one-way trips with stopovers. Results show a relatively good model accuracy, but the model tends to overestimate one-way trips while underestimating one-way trips with stopovers. This method can be used to characterize other services and markets where access to proprietary data is not possible. Contributions from all five perspectives contribute to enrich the body of knowledge on the user behaviours in a carsharing ecosystem setting either from an empirical, methodological or strategic point of view. Contributions are useful for the scientific community, but also for Communauto, for other carsharing operators, and for all actors that are engaged in the mobility aspect of our cities. Nevertheless, the body of knowledge on the subject still needs great attention. Thus, it is recommended looking at strategic issues as the impact of supply on demand to help develop new tools for operators willing to expand their service. Finally, the environmental impact assessment of free-floating solutions should be looked over with an integrated point of view considering the various factors affecting user behavior in a dual-mode carsharing ecosystem