Since the beginning of the 21st Century, the world has seen the rapid development of the so-called "sharing economy" or collaborative consumption (Botsman, 2010). One of the first areas affected by the shared economy is vehicle ownership. With the emergence of several new providers of mobility services, such as Uber and car2go, there has been the promise of changes to the traditional way of owning and using a vehicle (Wong, Hensher, & Mulley, 2017). One potential consequence of shared mobility services is the reduction in vehicle ownership. At the same time, cities are trying to anticipate these changes by reducing the amount of space dedicated to parking, including parking requirements for residential developments. This thesis aims to assess the extent to which new shared mobility services (specifically, carsharing, bikesharing, and ridehailing) and travel demand management strategies (especially parking requirements and transit pass availability) relate to vehicle ownership among residents of multifamily dwellings. To do this, we use a web-based survey targeted to residents of multifamily apartments from Portland, Oregon. With these data, we built a multinomial logistic of the number of the vehicles owned as a function of socio-demographics, built environment, parking supply, transit passes, and three forms of shared mobility services. Results suggest that there is a strong association between shared mobility use and car ownership. However, it is not as significant as the effects of income, household size, distance to work, transit pass ownership, or even parking availability. Carshare use was negatively associated with the number of household vehicles, suggesting that it may be a useful tool in reducing car ownership. For respondents with higher education and income levels, increased carshare use was associated with fewer cars. Ridehail use, however, was not as clearly associated with reducing vehicle ownership and the effect was much smaller than that of carsharing. Parking availability in the building also has a significant and positive association with vehicle ownership. In sites with no parking available, there is an increased chance of the household owning less than two or more vehicles. However, this effect seems to disappear with the increased use of shared mobility. For all income levels, monthly use of ridehail and carshare between two and three times may decrease the odds of owning two or more vehicles. The use of both options, relaxing parking requirements and shared mobility availability, seems the best strategy to reduce vehicle ownership. In the short term, it is an alternative to those residents that decide to get rid of one or all cars but still are not ready to give up using cars. For the long term, a new relationship with vehicle ownership can be built now for the younger generation.

Thesis (M.Geom.M.)--University of Melbourne, Dept. of Geomatics, 2007. / Typescript. Includes bibliographical references (leaves 100-104).

Orientador: Eduardo Candido Xavier / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-26T21:57:37Z (GMT). No. of bitstreams: 1 Santos_DouglasOliveira_M.pdf: 1370671 bytes, checksum: 41f9ee952e593c7ed8fa83d738c343d5 (MD5) Previous issue date: 2014 / Resumo: Nesse trabalho é apresentado um framework que tem como objetivo facilitar o compartilhamento de veículos no dia a dia de uma grande cidade. O framework apresenta uma arquitetura cliente-servidor. O lado cliente é usado por passageiros para requerer uma viagem compartilhada e por motoristas, que podem ser donos de veículos privados ou taxistas, os quais estão dispostos a compartilharem seu veículo para redução de custos ou obtenção de lucro. O lado servidor precisa resolver um problema dinâmico de otimização que provamos ser NP-difícil. O problema em questão, denominado Ride-sharing Problem with Money Incentive (RSPMI), é modelado da seguinte forma: em cada instante de tempo, temos um conjunto de pessoas, as quais necessitam de uma viagem a partir de um ponto de origem até um ponto de destino, e um conjunto de veículos, onde cada um tem uma origem e um destino. É necessário considerar algumas restrições que os passageiros possam ter, que são: o horário mínimo de saída da origem, o horário máximo de chegada até o destino, o número de passageiros que devem viajar juntos e também o valor máximo que estão dispostos a pagar. Os veículos também apresentam restrições, já que estes podem ter um horário mínimo de saída e um horário máximo de chegada. O motorista define a capacidade máxima do veículo e o preço por quilômetro rodado. Dado todas as informações e restrições, o problema consiste em formar uma rota para cada veículo com o objetivo de maximizar o número de passageiros atendidos e de minimizar os custos. O RSPMI é um problema novo na literatura e difere dos demais problemas de compartilhamento de veículos por ser o único a considerar custos compartilhados, calculando o valor total a ser pago por cada passageiro e possibilitando cada um escolher o valor máximo a ser pago. O foco do trabalho se deu no estudo e desenvolvimento de métodos que possam resolver a versão dinâmica do RSPMI, em tempo real, e em larga escala. O método proposto necessita de uma heurística que resolva o problema estático e de um algoritmo que resolva, eficientemente, o Many to Many Shortest Path Problem. Desenvolvemos heurísticas GRASP para o problema estático e usamos um algoritmo baseado em Contraction Hierarchies, o qual é muito eficiente, para lidar com os caminhos mínimos. Experimentos computacionais foram realizados usando instâncias que simulam, a partir de dados reais, uma atividade de compartilhamento de táxis na cidade de São Paulo. Em nossas simulações, os passageiros pagaram, em média, quase 30% menos do que pagariam em uma viagem privada / Abstract: In this work, we present a framework for dynamic ride-sharing. The framework has a client-server architecture. The client is used by passengers to request rides and by drivers, including vehicle owners and taxi drivers, who are willing to share their vehicles in order to reduce costs or to earn money. The server needs to solve a dynamic optimization problem which is proved to be NP-Hard. The problem, called Ride-sharing Problem with Money Incentive (RSPMI), is modeled in the following manner: at each instant of time, there are a set of passengers needing to travel from a source to a destination point and a set of vehicles, each one having a source and a destination. Passengers have constraints that need to be considered, which are: an earliest departure time, a latest arrival time, the number of passengers that will travel together and the maximum value they are willing to pay for the ride. Vehicles can have an earliest departure time and a latest arrival time, as well. They also have a maximum capacity and a price per kilometer. The problem is to compute a route for each vehicle, with the goal of maximizing the number of attended requests and minimizing the total paid by passengers. RSPMI is a new problem in the literature, differing from others ride-sharing problems, because it is the only one that considers shared costs, having a constraint which allows people to set the maximum value for the ride. The main focus of the work is to develop methods that can solve the dynamic version of the RSPMI, in real time and large scale. The proposed method needs an heuristic to solve the static problem and an algorithm to solve the Many to Many Shortest Path Problem. We developed GRASP heuristics for the static problem and used Contraction Hierarchies to deal with the shortest path problem. Computational experiments were made to evaluate our method and heuristics. We used instances based on real data that simulates a day of taxis activity in the city of Sao Paulo. In our experiments, passengers paid, on average, almost 30% less than a private ride / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

Comment résoudre les problèmes de congestion liés au développement urbain ? Les investissements massifs dans les infrastructures et le traitement monétaire et coercitif des comportements ayant montré leurs limites, nous proposons d'explorer la possibilité de favoriser les comportements positifs (covoiturage, vélo, télétravail). Nous commençons par élargir le modèle comportemental de l'individu en posant comme préalable à l'étude des motivations la prise en compte du cadre dans lequel il se situe. Nous justifions théoriquement cet apport principalement par les travaux du psychologue Daniel Kahneman et du sociologue Ervin Goffman. Nous précisions ensuite notre démarche méthodologique : en montrant combien la démarche des expérimentations de laboratoire est reliée à l'hypothèse comportementale de l'homo œconomicus, nous montrons la cohérence de notre hypothèse de cadre avec celle des expériences de terrain. Nous décrivons ensuite trois expériences visant à montrer (i) comment le cadre correspond à une représentation confortée par un discours dominant (ii) l’importance de la mesure rétroactive de cette représentation et (iii) comment les motivations agissent à l’intérieur de ce cadre. Nous en déduisons plusieurs principes susceptibles de favoriser un changement de comportements de mobilité à même de traiter le problème de congestion : (i) la place de l’individu dans les schémas de transports, (ii) le temps ou le bien être comme indicateur de mesure et (iii) les représentations collectives comme support de coordination. / How to solve congestion problems related to urban development? As the massive investment in infrastructure and the monetary and coercive treatment of behaviors have shown their limits, we propose instead to explore the promotion of positive behavior (carpooling, biking, telecommuting). We start by expanding the behavioral model of the individual, by taking into account the context (frame) in which it happens. We justify this contribution primarily through the work of the psychologist Daniel Kahneman and the sociologist Ervin Goffman . Then we clarify our methodological approach: by showing how the process of laboratory experiments is connected to the behavioral factors of the homo oeconomicus, we show the consistency of our frame hypothesis with field experiments. We then describe three experiments to show (i) how the frame is underpinned by a dominant discourse (ii) the importance of the retroactive measure of this representation and (iii) how motivations acts within that frame. We derive several principles to promote a change of mobility behavior able to treat congestion: (i) the place of the individual in transport schemes, (ii) using time or well-being as a measurement indicator and (iii)collective representations as coordination enablers.

Two-sided matching markets are a cornerstone of modern economics. They model a wide range of applications such as ride-sharing, online dating, job positioning, school admissions, and many more. In many of those markets, monetary exchange does not play a role. For instance, the New York City public high school system is free of charge. Thus, the decision on how eighth-graders are assigned to public high schools must be made using concepts of fairness rather than price. There has been therefore a huge amount of literature, mostly in the economics community, defining various concepts of fairness in different settings and showing the existence of matchings that satisfy these fairness conditions. Those concepts have enjoyed wide-spread success, inside and outside academia. However, finding such matchings is as important as showing their existence. Moreover, it is crucial to have fast (i.e., polynomial-time) algorithms as the size of the markets grows. In many cases, modern algorithmic tools must be employed to tackle the intractability issues arising from the big data era. The aim of my research is to provide mathematically rigorous and provably fast algorithms to find solutions that extend and improve over a well-studied concept of fairness in two-sided markets known as stability. This concept was initially employed by the National Resident Matching Program in assigning medical doctors to hospitals, and is now widely used, for instance, by cities in the US for assigning students to public high schools and by certain refugee agencies to relocate asylum seekers. In the classical model, a stable matching can be found efficiently using the renowned deferred acceptance algorithm by Gale and Shapley. However, stability by itself does not take care of important concerns that arose recently, some of which were featured in national newspapers. Some examples are: how can we make sure students get admitted to the best school they deserve, and how can we enforce diversity in a cohort of students? By building on known and new tools from Mathematical Programming, Combinatorial Optimization, and Order Theory, my goal is to provide fast algorithms to answer questions like those above, and test them on real-world data. In Chapter 1, I introduce the stable matching problem and related concepts, as well as its applications in different markets. In Chapter 2, we investigate two extensions introduced in the framework of school choice that aim at finding an assignment that is more favorable to students -- legal assignments and the Efficiency Adjusted Deferred Acceptance Mechanism (EADAM) -- through the lens of classical theory of stable matchings. We prove that the set of legal assignments is exactly the set of stable assignments in another instance. Our result implies that essentially all optimization problems over the set of legal assignments can be solved within the same time bound needed for solving it over the set of stable assignments. We also give an algorithm that obtains the assignment output of EADAM. Our algorithm has the same running time as that of the deferred acceptance algorithm, hence largely improving in both theory and practice over known algorithms. In Chapter 3, we introduce a property of distributive lattices, which we term as affine representability, and show its role in efficiently solving linear optimization problems over the elements of a distributive lattice, as well as describing the convex hull of the characteristic vectors of the lattice elements. We apply this concept to the stable matching model with path-independent quota-filling choice functions, thus giving efficient algorithms and a compact polyhedral description for this model. Such choice functions can be used to model many complex real-world decision rules that are not captured by the classical model, such as those with diversity concerns. To the best of our knowledge, this model generalizes all those for which similar results were known, and our paper is the first that proposes efficient algorithms for stable matchings with choice functions, beyond classical extensions of the Deferred Acceptance algorithm. In Chapter 4, we study the discovery program (DISC), which is an affirmative action policy used by the New York City Department of Education (NYC DOE) for specialized high schools; and explore two other affirmative action policies that can be used to minimally modify and improve the discovery program: the minority reserve (MR) and the joint-seat allocation (JSA) mechanism. Although the discovery program is beneficial in increasing the number of admissions for disadvantaged students, our empirical analysis of the student-school matches from the 12 recent academic years (2005-06 to 2016-17) shows that about 950 in-group blocking pairs were created each year amongst disadvantaged group of students, impacting about 650 disadvantaged students every year. Moreover, we find that this program usually benefits lower-performing disadvantaged students more than top-performing disadvantaged students (in terms of the ranking of their assigned schools), thus unintentionally creating an incentive to under-perform. On the contrary, we show, theoretically by employing choice functions, that (i) both MR and JSA result in no in-group blocking pairs, and (ii) JSA is weakly group strategy-proof, ensures that at least one disadvantaged is not worse off, and when reservation quotas are carefully chosen then no disadvantaged student is worse-off. We show that each of these properties is not satisfied by DISC. In the general setting, we show that there is no clear winner in terms of the matchings provided by DISC, JSA, and MR, from the perspective of disadvantaged students. We however characterize a condition for markets, that we term high competitiveness, where JSA dominates MR for disadvantaged students. This condition is verified, in particular, in certain markets when there is a higher demand for seats than supply, and the performances of disadvantaged students are significantly lower than that of advantaged students. Data from NYC DOE satisfy the high competitiveness condition, and for this dataset our empirical results corroborate our theoretical predictions, showing the superiority of JSA. We believe that the discovery program, and more generally affirmative action mechanisms, can be changed for the better by implementing the JSA mechanism, leading to incentives for the top-performing disadvantaged students while providing many benefits of the affirmative action program.

Avec l’augmentation progressive de la population dans les grandes villes, comme Paris, nous prévoyons d’ici 2050 une augmentation de 50% du trafic routier. En considérant les embouteillages et la pollution que cette augmentation va générer, on voit clairement la nécessité de nouveaux système de mobilité plus durables, comme le covoiturage, ou plus généralement toute la mobilité partagée. En parlant de mobilité partagée, ce n’est pas seulement le partage de trajets de personnes qui ont le même itinéraire au même temps, elle inclut aussi les marchandises.Cette thèse aborde le défi de la synchronisation des flux de passagers et de marchandises dans les systèmes de mobilité urbaine et elle vis à développer des méthodes d’optimisation pour que cette synchronisation dans la mobilité partagée soit faisable. Plus précisément, elle aborde les questions de recherche suivantes:*Q1: Quelles sont les variantes des systèmes de mobilité partagée et comment les optimiser?*Q2: Comment synchroniser les déplacements de personnes et quels gains cette synchronisation peut-elle générer?*Q3: Comment combiner les flux de passagers et de fret et quels sont les avantages attendus?*Q4: Quels sont les effets de l'incertitude sur la planification et l'exploitation de systèmes de mobilité partagée?Dans un premier temps, nous étudions les différentes variantes des systèmes de mobilité partagée et nous les classifions en fonction de leurs modèles, caractéristiques, approches de résolution et contexte d'application. En se basant sur cette revue de littérature, nous identifions deux problèmes de mobilité partagés, que nous considérons en détails dans cette thèse et nous développons des méthodes d'optimisation pour les résoudre.Pour synchroniser les flux de passagers, nous étudions un modèle de covoiturage en utilisant les véhicules autonomes, personnels et partagés, et des points de rencontre où la synchronisation entre passagers peut avoir lieu. Pour cela, une méthode heuristique en deux phases est proposée et une étude de cas sur la ville de New York est présentée.Ensuite, nous développons un modèle d’optimisation qui combine les flux de passagers et de marchandises dans une région urbaine. Le but de ce modèle est d’utiliser les capacités disponibles sur une ligne de transport fixe pour transporter les passagers et des robots transportant des petits colis à leurs destinations finales en considérant que la demande de passagers est stochastique. Les résultats obtenus montrent que les solutions proposées par ces deux modèles peuvent conduire à une meilleure utilisation des systèmes de transport dans les régions urbaines. / The rise of research into shared mobility systems reflects emerging challenges, such as rising urbanization rates, traffic congestion, oil prices and environmental concerns. The operations research community has turned towards more sharable and sustainable systems of transportation. Although shared mobility comes with many benefits, it has some challenges that are restricting its widespread adoption. More research is thus needed towards developing new shared mobility systems so that a better use of the available transportation assets can be obtained.This thesis aims at developing efficient models and optimization approaches for synchronizing people and freight flows in an urban environment. As such, the following research questions are addressed throughout the thesis:*Q1: What are the variants of shared mobility systems and how to optimize them?*Q2: How can people trips be synchronized and what gains can this synchronization yields?*Q3: How can people and freight flows be combined and what are the intended benefits?*Q4: What impacts uncertainty can have on planning and operating shared mobility systems?First, we review different variants of the shared mobility problem where either (i) travelers share their rides, or (ii) the transportation of passengers and freight is combined. We then classify these variants according to their models, solution approaches and application context and We provide a comprehensive overview of the recently published papers and case studies. Based on this review, we identify two shared mobility problems, which we study further in this thesis.Second, we study a ridesharing problem where individually-owned and on-demand autonomous vehicles (AVs) are used for transporting passengers and a set of meeting points is used for synchronizing their trips. We develop a two-phase method (a pre-processing algorithm and a matching optimization problem) for assessing the sharing potential of different AV ownership models, and we evaluate them on a case study for New York City.Then, we present a model that integrates freight deliveries to a scheduled line for people transportation where passengers demand, and thus the available capacity for transporting freight, is assumed to be stochastic. We model this problem as a two-stage stochastic problem and we provide a MIP formulation and a sample average approximation (SAA) method along with an Adaptive Large Neighborhood Search (ALNS) algorithm to solve it. We then analyze the proposed approach as well as the impacts of stochastic passengers demand on such integrated system on a computational study.Finally, we summarize the key findings, highlight the main challenges facing shared mobility systems, and suggest potential directions for future research.

Fundação de Apoio a Pesquisa e à Inovação Tecnológica do Estado de Sergipe - FAPITEC/SE / A falta de mobilidade urbana é uma grande preocupação da gestão pública em todo o mundo. Algumas políticas têm sido adotadas a fim de minimizar seus efeitos nas grandes cidades. Construção de rotas alternativas, melhorias e incentivo ao uso de transportes públicos, construção de ciclovias e estímulo ao uso de bicicletas são algumas dessas políticas. Uma prática que pode contribuir para a solução do problema é a carona. Carona consiste no ato de transportar gratuitamente num veículo pessoas que possuem trajetórias semelhantes. Embora existam algumas aplicações que se propõem a facilitar a prática de caronas, nenhuma dessas aplicações possuem funcionalidades de busca por usuários que possuem similaridades de trajetória e de perfil demográfico e social. Neste trabalho, propomos uma abordagem inovadora, considerando peculiaridades do contexto do uso de caronas, que visa a descoberta de agrupamentos de usuários que possuem trajetórias semelhantes, usuários que possuem perfis semelhantes e agrupamentos de usuários que são similares considerando suas trajetórias e seus perfis. Elementos intrínsecos ao problema são formalmente definidos e uma primeira análise de complexidade para tempo de processamento foi realizada. Uma rede social de propósito específico para o compartilhamento de caronas foi modelada e implementada com respeito à abordagem proposta. O método para experimentação e avaliação da abordagem consistiu (i) na confecção de base de dados alimentada periodicamente em tempo real por dados de trânsito obtidos a partir de aparelhos de smartphone com GPS de voluntários em trânsito com seus automóveis, (ii) aplicação da abordagem proposta para geração dos agrupamentos de usuários a partir da base estabelecida e (iii) aplicação da métrica Davies-Boulding Index, que indica o quão factível os agrupamentos são. Resultados mostraram a efetividade da abordagem para solução do problema se comparada a formas bem estabelecidas da literatura relacionada, como o K-means, por exemplo. Resultados da análise da base de dados também mostraram que algumas informações de trânsito podem ser inferidas a partir de ações de mineração. Por fim, a aceitabilidade de potenciais usuários da rede social foi medida a partir de questionário. / Problems related to urban mobility is a big concern to public administration. Some policies have been adopted in order to soften those problems in large cities. Building new routes, encouraging the use of public transportation, building new bike paths and encouraging the use of bicycle are some of them. A common practice which is closely related to cultural habits in some nations and which can contribute to soften the problem is ridesharing. Ridesharing is defined as a grouping of travellers into common trip by car or van. Though there exist some applications that aim to facilitate the practice of ridesharing, none of them have the functionality to search automatically for users with similar trajectories or demographic and social profile. In this work, we proposed an innovative approach, considering ridesharing context, that aims to discover clusters of users that have similar trajectories, clusters of users that have similar profile and clusters of users with similar trajectory and similar profile. Furthermore, we define a formalization of ridesharing terms and an initial time complexity analysis is done. A social network for ridesharing has been also modeled and developed according to proposed approach. Experimentation and evaluation method consist of: (i) Building a dataset from volunteers in transit with GPS-equipped smartphones, (ii) Using proposed approach to generate clusters of users and application of Davies-Boulding index metrics which reflects how similar the elements of the same cluster are, as well as the dissimilarity among distinct clusters. Results show the feasibility of the approach to problem solution if compared with some approach established in literature such as, K-means. Results of dataset analysis show that some traffic information should undergo data mining. Finally, social network mobile app acceptance was measured by questionnaire.

<div>The growth in online goods delivery is causing a dramatic surge in urban vehicle traffic from last-mile deliveries. On the other hand, ride-sharing has been on the rise with the success of ride-sharing platforms and increased research on using autonomous vehicle technologies for routing and matching. The future of urban mobility for passengers and goods relies on leveraging new methods that minimize operational costs and environmental footprints of transportation systems. </div><div><br></div><div>This paper considers combining passenger transportation with goods delivery to improve vehicle-based transportation. Even though the problem has been studied with model-based approaches where the dynamic model of the transportation system environment is defined, model-free approaches where the dynamics of the environment are learned by interaction have been demonstrated to be adaptable to new or erratic environment dynamics. </div><div><br></div><div>FlexPool is a distributed model-free deep reinforcement learning algorithm that jointly serves passengers \& goods workloads by learning optimal dispatch policies from its interaction with the environment. The model-free algorithm (as opposed to a model-based one) is an algorithm which does not use the transition probability distribution (and the reward function) associated with the Markov decision process (MDP).</div><div> The proposed algorithm pools passengers for a ride-sharing service and delivers goods using a multi-hop routing method. These flexibilities decrease the fleet's operational cost and environmental footprint while maintaining service levels for passengers and goods. The dispatching algorithm based on deep reinforcement learning is integrated with an efficient matching algorithm for passengers and goods. Through simulations on a realistic urban mobility platform, we demonstrate that FlexPool outperforms other model-free settings in serving the demands from passengers \& goods. FlexPool achieves 30\% higher fleet utilization and 35\% higher fuel efficiency in comparison to (i) model-free approaches where vehicles transport a combination of passengers \& goods without the use of multi-hop transit, and (ii) model-free approaches where vehicles exclusively transport either passengers or goods. </div>

<p>Autonomous systems are often deployed in dynamic environments and are challenged with unexpected changes (novelties) in the environments where they receive novel data that was not seen during training. Given the uncertainty, they should be able to operate without (or with limited) human intervention and they are expected to (1) Adapt to such changes while still being effective and efficient in performing their multiple tasks. The system should be able to provide continuous availability of its critical functionalities. (2) Make informed decisions independently from any central authority. (3) Be Cognitive: learns the new context, its possible actions, and be rich in knowledge discovery through mining and pattern recognition. (4) Be Reflexive: reacts to novel unknown data as well as to security threats without terminating on-going critical missions. These characteristics combine to create the workflow of autonomous decision-making process in multi-agent environments (i.e.,) any action taken by the system must go through these characteristic models to autonomously make an ideal decision based on the situation. </p> <p><br></p> <p>In this dissertation, we propose novel learning-based models to enhance the decision-making process in autonomous multi-agent systems where agents are able to detect novelties (i.e., unexpected changes in the environment), and adapt to it in a timely manner. For this purpose, we explore two complex and highly dynamic domains </p> <p>(1) Transportation Networks (e.g., Ridesharing application): where we develop AdaPool: a novel distributed diurnal-adaptive decision-making framework for multi-agent autonomous vehicles using model-free deep reinforcement learning and change point detection. (2) Multi-agent games (e.g., Monopoly): for which we propose a hybrid approach that combines deep reinforcement learning (for frequent but complex decisions) with a fixed-policy approach (for infrequent but straightforward decisions) to facilitate decision-making and it is also adaptive to novelties. (3) Further, we present a domain agnostic approach for decision making without prior knowledge in dynamic environments using Bootstrapped DQN. Finally, to enhance security of autonomous multi-agent systems, (4) we develop a machine learning based resilience testing of address randomization moving target defense. Additionally, to further  improve the decision-making process, we present (5) a novel framework for multi-agent deep covering option discovery that is designed to accelerate exploration (which is the first step of decision-making for autonomous agents), by identifying potential collaborative agents and encouraging visiting the under-represented states in their joint observation space. </p>