Community structures, interactions and dynamics in London’s bicycle sharing network

Bikesharing schemes are transportation systems that not only provide an efficient mode of transportation in congested urban areas, but also improve last-mile connectivity with public transportation and local accessibility. Bikesharing schemes around the globe generate detailed trip data sets with spatial and temporal dimensions, which, with proper mining and analysis, reveal valuable information on urban mobility patterns. In this paper, we study the London bicycle sharing dataset to explore community structures. Using a novel clustering technique, we derive distinctive behavioural patterns and assess community interactions and spatio-temporal dynamics. The analyses reveal self-contained, interconnected and hybrid clusters that mimic London’s physical structure. Exploring changes over time, we find geographically isolated and specialized communities to be relatively consistent, while the remaining system exhibits volatility, especially during and around peak commuting times. By increasing our understanding of the collective behaviour of the bikesharing users, this analysis supports policy appraisal, operational decision-making and motivates improvements in infrastructure design and management

An Online Learning and Optimization Approach for Competitor-Aware Management of Shared Mobility Systems

An important trend in mobility is the consumption of mobility as-a-service heralding in the age of freefloating vehicle sharing (FFVS) systems. In many markets such fleets compete. We investigate how realtime competitor information can create value for operators in this context. We focus on the vehicle supply decision which is a large operational concern. We show empirically that local market shares directly depend on the share of available vehicles in a location, which underlines the value potential of competitor awareness. We leverage this insight by proposing a novel decision support system for optimal management of FFVS systems under competition. We proceed in two phases, (1) a predictive phase and (2) a prescriptive phase. In phase (1), we compile a spatio-temporal dataset based on Car2Go and DriveNow transactions in Berlin, which we supplement with temporal, geographical and weather data. We partition the city into hexagonal tiles and observe vehicle supply per tile at the start of each period. We train machine learning models to predict vehicle inflows and vehicle outflows during the next period to derive total supply and demand. We find that inflows and outflows can be predicted with high accuracy using similar models. We test different temporal and spatial resolutions and find that spatial resolution incurs larger performance penalties. In phase (2), we formulate a myopic mixed integer non-linear programming model with a margin-maximizing objective function. The model trades off additional market share gains against the cost of re-locating vehicles, which enables operators to assign vehicles optimally across the service network. Our numerical studies on the case of Car2Go and DriveNow demonstrate that this competitor-aware model is capable of profitably improving market share by up to 1.4% or 3.4% for human-based and autonomous relocation respectively in a prefect foresight scenario and by up to 0.8% and 1.8% respectively when using predicted values

Combining Analytics and Simulation Methods to Assess the Impact of Shared, Autonomous Electric Vehicles on Sustainable Urban Mobility

Urban mobility is currently undergoing three fundamental transformations with the sharing economy, electrification, and autonomous vehicles changing how people and goods move across cities. In this paper, we demonstrate the valuable contribution of decision support systems that combine data-driven analytics and simulation techniques in understanding complex systems such as urban transportation. Using the city of Berlin as a case study, we show that shared, autonomous electric vehicles can substantially reduce resource investments while keeping service levels stable. Our findings inform stakeholders on the trade-off between economic and sustainability-related considerations when fostering the transition to sustainable urban mobilit

Comparing multiple data streams to assess free-floating carsharing use

ABSTRACT: Passive data streams are a great alternative to traditional travel surveys to assess the use and the change in behavior. Because of accessibility issues, researchers have harvested free-floating carsharing (FFcs) web-services in order to estimate the spatiotemporal demand. This paper presents the comparison of multiple data streams in the assessment of trip type distribution for FFcs service. While a full dataset of GPS traces may be considered a good approximation of the ground truth, harvesting of origin-destination data seems to estimate correctly the general trend of certain trip type distributions, while for other trip type estimations, a more extensive set of data is needed (member & stopover information) to fully assess it

Moving in Time and Space - Location Intelligence for Carsharing Decision Support

In this paper we develop a spatial decision support system that assists free-floating carsharing providers in countering imbalances between vehicle supply and customer demand in existing business areas and reduces the risk of imbalance when expanding the carsharing business to a new city. For this purpose, we analyze rental data of a major carsharing provider in the city of Amsterdam in combination with points of interest (POIs). The spatio-temporal demand variations are used to develop pricing zones for existing business areas. We then apply the influence of POIs derived from carsharing usage in Amsterdam in order to predict carsharing demand in the city of Berlin. The results indicate that predicted and actual usage patterns are very similar. Hence, our approach can be used to define new business areas when expanding to new cities to include high demand areas and exclude low demand areas, thereby reducing the risk of supply-demand imbalance

Understanding and Modeling Taxi Demand Using Time Series Models

The spatio-temporal variations in demand for transportation, particularly taxis, are impacted by various factors such as commuting, weather, road work and closures, disruption in transit services, etc. Identifying the factors that influence taxi demand and understanding its dynamic provide planners with the information necessary to improve the transportation systems and also help drivers to reduce their vacant time. This dissertation focuses on important factors affecting the demand. In the beginning, the impact of price changes on the demand is studied. Chapter One discusses how the seasonal effects and trends are removed from the demand, and then price elasticity for demand is calculated as a measure to quantify the impact of each factor. Furthermore, the first chapter provides elasticity values for the New York City and each of the five boroughs, and studies the relationship between these values and some socio-economic characteristics. The second part of this dissertation studies the demand of taxi and how it is affected by other public transportation modes and weather. This demand modeling technique utilizes a combination of time series and linear regression models. The proposed method is then applied to yellow cab data in New York City. The pick-up points of yellow cab data in April, May, and June of 2014 are considered and aggregated every hour. The results show a significant correlation between taxi demand and demand for other transportation modes, as well as weather conditions. It is shown that combining time series models with linear regression will improve the performance of the model. This study then follows by working on the time series models and considering the spatial variation of the demand. To understand the user demand for taxis through space and time, a generalized spatio-temporal autoregressive (STAR) model is proposed. In order to deal with the high dimensionality of the model, LASSO-type penalized methods are proposed to tackle the parameter estimation. The forecasting performance of the proposed models is measured using the out-of-sample mean squared prediction error (MSPE), and it is found that the proposed models outperform other alternative models such as vector autoregressive (VAR) models. The proposed modeling framework has an easily interpretable parameter structure and can feasibly be applied by taxi operators. The efficiency of the proposed model shows advantages for model estimation in real-time applications. Furthermore, this dissertation studies the demand for e-hailing services which are growing rapidly especially in large cities. Similar to taxi demand, Uber demand is not distributed uniformly, either spatially or temporally, and this study proposes using spatio-temporal models to predict Uber demand as well. Moreover, the prediction performances of several statistical models are compared with each other: a) one temporal model (vector autoregressive (VAR)), b) two proposed spatio-temporal models (spatial-temporal autoregressive (STAR), c) least absolute shrinkage and selection operator applied on STAR (LASSO-STAR)). They are compared in different scenarios (based on the number of time and space lags), and for both peak and off-peak periods (rush hours and non-rush hours). This section additionally proposes different weighting matrices to improve the performance of the model. The results show the need to consider spatial models for e-hailing services and demonstrate significant improvement in the prediction of demand using the two proposed models

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