Predicting bicycle arrivals in a Bicycle Sharing System network: A data science driven approach grounded in Zero-Inflated Regression

The adoption of bicycle sharing systems (BSS) is growing in order to improve the way people move around cities, but also to stimulate the development of a more sustainable urban mobility. For the proper functioning of a BSS, it is important to have bicycles permanently available at the stations for users to start their trips, so the literature has undertaken efforts, from the perspective of the service operator, to improve the process of redistribution of bicycles and thus ensure their availability at the different stations. Since the guarantee of available bicycles cannot be assured, this work proposes to develop, from the cyclist's perspective, a proof of concept on the feasibility of informing the user about the possibility of starting a trip in a pre-defined time interval. The main contributions of this work are: (i) the ability to predict how many bicycles will arrive at a given station is a feasible improvement for BSS, (ii) the models developed through the Zero-Inflated Regression approach are a path that can be explored to improve prediction and (iii) unprecedented methodological contribution to the literature on BSS focusing on the end-user's decision power about whether or not it will soon be possible to start a trip.A adoção de sistemas de bicicletas partilhadas (BSS) vem crescendo com o objetivo de melhorar a forma como as pessoas se deslocam pelas cidades, mas também para estimular o desenvolvimento de uma mobilidade urbana mais sustentável. Para o bom funcionamento de um BSS é importante que haja bicicletas permanentemente disponíveis nas estações para os utilizadores iniciarem as suas viagens, pelo que a literatura tem empreendido esforços, sob a ótica do operador do serviço, para melhorar o processo de redistribuição das bicicletas e assim garantir a sua disponibilidade nas diferentes estações. Como a garantia de bicicletas disponíveis não pode ser assegurada, este trabalho propõe-se desenvolver, sob a ótica do ciclista, uma prova de conceito sobre a viabilidade de informar o utilizador acerca da possibilidade de iniciar uma viagem num intervalo de tempo pré-definido. As principais contribuições deste trabalho são: (i) a capacidade de previsão de quantas bicicletas chegarão a uma determinada estação é uma melhoria viável para os BSS, (ii) os modelos desenvolvidos através da aproximação Zero-Inflated Regression são um caminho que pode ser explorado para melhorar a previsão e (iii) contributo metodológico inédito à literatura sobre os BSS com foco no poder decisório do utilizador final sobre se será, ou não, possível iniciar uma viagem em breve

Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes

Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a biased, or censored, version of the true underlying demand pattern. Without explicitly accounting for this inherent distinction, predictive models of demand would necessarily represent a biased version of true demand, thus less effectively predicting the needs of service users. To counter this problem, we propose a general method for censorship-aware demand modeling, for which we devise a censored likelihood function. We apply this method to the task of shared mobility demand prediction by incorporating the censored likelihood within a Gaussian Process model, which can flexibly approximate arbitrary functional forms. Experiments on artificial and real-world datasets show how taking into account the limiting effect of supply on demand is essential in the process of obtaining an unbiased predictive model of user demand behavior.Comment: 21 pages, 10 figure

Process algebra for located Markovian agents and scalable analysis techniques for the modelling of Collective Adaptive Systems

Recent advances in information and communications technology have led to a surge in the popularity of artificial Collective Adaptive Systems (CAS). Such systems, comprised by many spatially distributed autonomous entities with decentralised control, can often achieve discernible characteristics at the global level; a phenomenon sometimes termed emergence. Examples include smart transport systems, smart electricity power grids, robot swarms, etc. The design and operational management of CAS are of vital importance because different configurations of CAS may exhibit very large variability in their performance and the quality of services they offer. However, due to their complexity caused by varying degrees of behaviour, large system scale and highly distributed nature, it is often very difficult to understand and predict the behaviour of CAS under different situations. Novel modelling and quantitative analysis methodologies are therefore required to address the challenges posed by the complexity of such systems. In this thesis, we develop a process algebraic modelling formalism that can be used to express complex dynamic behaviour of CAS and provide fast and scalable analysis techniques to investigate the dynamic behaviour and support the design and operational management of such systems. The major contributions of this thesis are: (i) development of a novel high-level formalism, PALOMA, the Process Algebra for Located Markovian Agents for the modelling of CAS. CAS specified in PALOMA can be automatically translated to their underlying mathematical models called Population Continuous-Time Markov Chains (PCTMCs). (ii) development of an automatic moment-closure approximation method which can provide rapid Ordinary Differential Equation-based analysis of PALOMA models. (iii) development of an automatic model reduction algorithm for the speed up of stochastic simulation of PALOMA/PCTMC models. (iv) presenting a case study, predicting bike availability in stations of Santander Cycles, the public bike-sharing system in London, to show that our techniques are well-suited for analysing real CAS

Simulation study on the fleet performance of shared autonomous bicycles

Rethinking cities is now more imperative than ever, as society faces global challenges such as population growth and climate change. The design of cities can not be abstracted from the design of its mobility system, and, therefore, efficient solutions must be found to transport people and goods throughout the city in an ecological way. An autonomous bicycle-sharing system would combine the most relevant benefits of vehicle sharing, electrification, autonomy, and micro-mobility, increasing the efficiency and convenience of bicycle-sharing systems and incentivizing more people to bike and enjoy their cities in an environmentally friendly way. Due to the uniqueness and radical novelty of introducing autonomous driving technology into bicycle-sharing systems and the inherent complexity of these systems, there is a need to quantify the potential impact of autonomy on fleet performance and user experience. This paper presents an ad-hoc agent-based simulator that provides an in-depth understanding of the fleet behavior of autonomous bicycle-sharing systems in realistic scenarios, including a rebalancing system based on demand prediction. In addition, this work describes the impact of different parameters on system efficiency and service quality and quantifies the extent to which an autonomous system would outperform current bicycle-sharing schemes. The obtained results show that with a fleet size three and a half times smaller than a station-based system and eight times smaller than a dockless system, an autonomous system can provide overall improved performance and user experience even with no rebalancing. These findings indicate that the remarkable efficiency of an autonomous bicycle-sharing system could compensate for the additional cost of autonomous bicycles

Car Allocation between Household Heads in Car Deficient Households: A Decision Model

This paper considers car allocation choice behaviour in car-deficient households explicitly in the context of an activity-scheduling process, focusing on work activities. A decision tree induction method is applied to derive a decision tree for the car allocation decision in automobile deficient households using a large travel-and-activity diary data set recently collected in the Netherlands. The results show a satisfactory improvement in goodness of fit of the decision tree model compared to a null model. Overall, the probability of males getting the car for work is considerably higher than that of female in many condition settings. However, activity schedule, spatial and socio-economic variables appear to have an influence as well. An analysis of impacts of condition variables on car allocation decisions reveals that socio-economic variables have only a limited impact, whereas attributes of the transportation and land-use system have a relatively big impact. The propensity of men driving a car to the work place is higher than that of women. However, the relative accessibility of the work location by bike compared to car appears to have a relatively large influence on who gets the car for work. Household income and presence of children also appear to have significant effects