The Right Tools for the Job: The Case for Spatial Science Tool-Building

This paper was presented as the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC. The spatial sciences have recently seen growing calls for more accessible software and tools that better embody geographic science and theory. Urban spatial network science offers one clear opportunity: from multiple perspectives, tools to model and analyze nonplanar urban spatial networks have traditionally been inaccessible, atheoretical, or otherwise limiting. This paper reflects on this state of the field. Then it discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this. Next it reviews this tool's use in the recent multidisciplinary spatial network science literature to highlight upstream and downstream benefits of open-source software development. Tool-building is an essential but poorly incentivized component of academic geography and social science more broadly. To conduct better science, we need to build better tools. The paper concludes with paths forward, emphasizing open-source software and reusable computational data science beyond mere reproducibility and replicability

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Predicting Bus End-Trip Delays Using Different Machine Learning Algorithms to Model Planning Effectiveness

RÉSUMÉ : Le transport public existe presque partout dans le monde. Cela permet à toutes les personnes le désirant de se déplacer d’un endroit à un autre d’une ville de façon économique et écologique. De plus, de plus en plus de données sont disponibles de nos jours grâce aux systèmes embarqués à l’intérieur des véhicules. Ces données pourraient être utilisées dans une optique de prévision des retards, qui permettraient par la suite de les anticiper. Ainsi la fiabilité des horaires serait améliorée et plus de gens seraient susceptibles d’employer ce mode de transport. Des travaux ont été réalisés afin de prédire les retards en utilisant différentes données, cependant aucune d’elle ne l’a fait dans l’idée d’intégrer ces prévisions dans les procédures de création de planification de trajet. Au cours de ce mémoire, divers modèles de prédiction de retard pour les fins de trajet sont essayés. Il ne s’agit pas de prédire le retard exact, mais de classifier les retards des fins de trajet. Afin d’être utile aux planificateurs d’horaires, ces modèles n’utilisent que des données qui peuvent se trouver en amont de la planification. Les données exploitées pour les modèles sont des observations historiques de la ville de Montréal. Deux problèmes de classification sont abordés au cours de ce mémoire. Le premier est un modèle de classification binaire qui prédit si un bus va finir son trajet en retard ou à l’heure. Le second est un modèle qui prévoit dans quel créneau de retard le bus va finir son trajet. Pour chacun des problèmes, trois algorithmes de machine learning pour l’estimation des retards sont testés : réseau de neurones, forêt aléatoire et arbre stimulé par gradient. De plus, une régression logistique est également testée afin de comparer les résultats par rapport à une méthode plus standard. Les modèles sont optimisés selon différentes méthodes et sont comparés en terme de précision et de temps d’entraînement. Les modèles sont par la suite entraînés sur une période et testés sur d’autres afin d’étudier la possibilité d’intégrer ces modèles dans le processus de création de lignes. Par la suite, les prédictions sont utilisées afin de créer des distributions de probabilité pour les différents crénaux de retard pour les fins de trajet des bus. Les différents algorithmes sont testés afin de distinguer ceux qui reproduisent au mieux la réalité. Le projet conclut sur la possibilité d’utiliser les données de planning pour prédire le retard des fins de trajet des bus. Une classification sur plusieurs classes peut être améliorée en intégrant de l’apprentissage non supervisée afin de déterminer les classes de retard. Il est également possible d’entraîner un modèle sur des périodes passées afin de prédire sur de futures périodes, mais cette méthode doit être encore améliorée.----------ABSTRACT : Public transportation services are provided in almost all the cities of the world. They allow people to move through the cities in an economical and eco-friendly way. The buses are one of the possible solutions for public transportation. Moreover buses are interesting to study because more data are available from onboard systems and can be used to optimize service quality. Indeed, preventing delays could improve service reliability and thus make people more likely to use public transport instead of their cars, which are currently more comfortable and more reliable. The first step in this process would be to forecast the delays. A lot of factors are linked to delays: peak-hour traÿc, weather or accidents, etc. Some studies were conducted to predict end trips delay using real-time input which does not allow improvement to schedule reliability because these data are not available during planning. This research focuses on modeling end-trip arrival time for each bus trip based only on o˜ine input available to public transport planner. The models do not intend to predict the exact delays, but rather to classify them. The delays used to train and test the models are historical observations from the city of Montreal in autumn 2017. Two di˙erent classification problems were treated. The first one estimates the probability for a trip to end on-time or late. The second one estimates the slot of delay. For each problem, three di˙erent machine learning models were built and optimized: random forest, gradient boosted tree and artificial neural network. Also, logistic regression was tested in order to compare the results. Several optimization methods were tried. The models are compared in term of accuracy, recall, f1 score and training time. The data from another period (autumn 2016) were then added to the database, and the model tested on the aggregated database. The model accuracy remained constant after the addition of the new period. The models were then fit on a single period (autumn 2016) and tested on the other one (autumn 2017) in order to check the possibility to use the model to forecast future schedules. The prediction is then used to generate a probability distribution for the di˙erent trips to end late to assess service reliability. The probability distributions are then compared with reality by comparing the distance between them and the frequencies of delays for the di˙erent trips. Normal distribution was also tested and obtained better results than the machine learning models. The project concluded that it is possible to model end trip delays using o˜ine data. Multi-label classification can be improved by using unsupervised learning to determine classes

Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encour-aging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility‐as‐a‐service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope

Use of Petri Nets to Manage Civil Engineering Infrastructures

Over the last years there has been a shift, in the most developed countries, in investment and efforts within the construction sector. On the one hand, these countries have built infrastructures able to respond to current needs over the last decades, reducing the need for investments in new infrastructures now and in the near future. On the other hand, most of the infrastructures present clear signs of deterioration, making it fundamental to invest correctly in their recovery. The ageing of infrastructure together with the scarce budgets available for maintenance and rehabilitation are the main reasons for the development of decision support tools, as a mean to maximize the impact of investments. The objective of the present work is to develop a methodology for optimizing maintenance strategies, considering the available information on infrastructure degradation and the impact of maintenance in economic terms and loss of functionality, making possible the implementation of a management system transversal to different types of civil engineering infrastructures. The methodology used in the deterioration model is based on the concept of timed Petri nets. The maintenance model was built from the deterioration model, including the inspection, maintenance and renewal processes. The optimization of maintenance is performed through genetic algorithms. The deterioration and maintenance model was applied to components of two types of infrastructure: bridges (pre-stressed concrete decks and bearings) and buildings (ceramic claddings). The complete management system was used to analyse a section of a road network. All examples are based on Portuguese data

Design and validation of decision and control systems in automated driving

xxvi, 148 p.En la última década ha surgido una tendencia creciente hacia la automatización de los vehículos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logística de mercancías y otros sectores dependientes del transporte. En el desarrollo de la conducción automatizada en entornos estructurados, la seguridad y el confort, como parte de las nuevas funcionalidades de la conducción, aún no se describen de forma estandarizada. Dado que los métodos de prueba utilizan cada vez más las técnicas de simulación, los desarrollos existentes deben adaptarse a este proceso. Por ejemplo, dado que las tecnologías de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehículo y la estimación de parámetros. Además, las tecnologías en el vehículo deben ser lo suficientemente robustas para cumplir con los requisitos de seguridad, mejorando la redundancia y respaldar una operación a prueba de fallos. Considerando las premisas mencionadas, esta Tesis Doctoral tiene como objetivo el diseño y la implementación de un marco para lograr Sistemas de Conducción Automatizados (ADS) considerando aspectos cruciales, como la ejecución en tiempo real, la robustez, el rango operativo y el ajuste sencillo de parámetros. Para desarrollar las aportaciones relacionadas con este trabajo, se lleva a cabo un estudio del estado del arte actual en tecnologías de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehículos de simulación y ADS. Se introducen nuevas formulaciones predictivas basadas en modelos para mejorar la seguridad y el confort en el proceso de seguimiento de trayectorias. Por último, se evalúan escenarios de mal funcionamiento para mejorar la seguridad en entornos urbanos, proponiendo una estrategia alternativa de estimación de posicionamiento para minimizar las condiciones de riesgo

Design and validation of decision and control systems in automated driving

xxvi, 148 p.En la última década ha surgido una tendencia creciente hacia la automatización de los vehículos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logística de mercancías y otros sectores dependientes del transporte. En el desarrollo de la conducción automatizada en entornos estructurados, la seguridad y el confort, como parte de las nuevas funcionalidades de la conducción, aún no se describen de forma estandarizada. Dado que los métodos de prueba utilizan cada vez más las técnicas de simulación, los desarrollos existentes deben adaptarse a este proceso. Por ejemplo, dado que las tecnologías de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehículo y la estimación de parámetros. Además, las tecnologías en el vehículo deben ser lo suficientemente robustas para cumplir con los requisitos de seguridad, mejorando la redundancia y respaldar una operación a prueba de fallos. Considerando las premisas mencionadas, esta Tesis Doctoral tiene como objetivo el diseño y la implementación de un marco para lograr Sistemas de Conducción Automatizados (ADS) considerando aspectos cruciales, como la ejecución en tiempo real, la robustez, el rango operativo y el ajuste sencillo de parámetros. Para desarrollar las aportaciones relacionadas con este trabajo, se lleva a cabo un estudio del estado del arte actual en tecnologías de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehículos de simulación y ADS. Se introducen nuevas formulaciones predictivas basadas en modelos para mejorar la seguridad y el confort en el proceso de seguimiento de trayectorias. Por último, se evalúan escenarios de mal funcionamiento para mejorar la seguridad en entornos urbanos, proponiendo una estrategia alternativa de estimación de posicionamiento para minimizar las condiciones de riesgo