A Data-driven Methodology Towards Mobility- and Traffic-related Big Spatiotemporal Data Frameworks

Human population is increasing at unprecedented rates, particularly in urban areas. This increase, along with the rise of a more economically empowered middle class, brings new and complex challenges to the mobility of people within urban areas. To tackle such challenges, transportation and mobility authorities and operators are trying to adopt innovative Big Data-driven Mobility- and Traffic-related solutions. Such solutions will help decision-making processes that aim to ease the load on an already overloaded transport infrastructure. The information collected from day-to-day mobility and traffic can help to mitigate some of such mobility challenges in urban areas. Road infrastructure and traffic management operators (RITMOs) face several limitations to effectively extract value from the exponentially growing volumes of mobility- and traffic-related Big Spatiotemporal Data (MobiTrafficBD) that are being acquired and gathered. Research about the topics of Big Data, Spatiotemporal Data and specially MobiTrafficBD is scattered, and existing literature does not offer a concrete, common methodological approach to setup, configure, deploy and use a complete Big Data-based framework to manage the lifecycle of mobility-related spatiotemporal data, mainly focused on geo-referenced time series (GRTS) and spatiotemporal events (ST Events), extract value from it and support decision-making processes of RITMOs. This doctoral thesis proposes a data-driven, prescriptive methodological approach towards the design, development and deployment of MobiTrafficBD Frameworks focused on GRTS and ST Events. Besides a thorough literature review on Spatiotemporal Data, Big Data and the merging of these two fields through MobiTraffiBD, the methodological approach comprises a set of general characteristics, technical requirements, logical components, data flows and technological infrastructure models, as well as guidelines and best practices that aim to guide researchers, practitioners and stakeholders, such as RITMOs, throughout the design, development and deployment phases of any MobiTrafficBD Framework. This work is intended to be a supporting methodological guide, based on widely used Reference Architectures and guidelines for Big Data, but enriched with inherent characteristics and concerns brought about by Big Spatiotemporal Data, such as in the case of GRTS and ST Events. The proposed methodology was evaluated and demonstrated in various real-world use cases that deployed MobiTrafficBD-based Data Management, Processing, Analytics and Visualisation methods, tools and technologies, under the umbrella of several research projects funded by the European Commission and the Portuguese Government.A população humana cresce a um ritmo sem precedentes, particularmente nas áreas urbanas. Este aumento, aliado ao robustecimento de uma classe média com maior poder económico, introduzem novos e complexos desafios na mobilidade de pessoas em áreas urbanas. Para abordar estes desafios, autoridades e operadores de transportes e mobilidade estão a adotar soluções inovadoras no domínio dos sistemas de Dados em Larga Escala nos domínios da Mobilidade e Tráfego. Estas soluções irão apoiar os processos de decisão com o intuito de libertar uma infraestrutura de estradas e transportes já sobrecarregada. A informação colecionada da mobilidade diária e da utilização da infraestrutura de estradas pode ajudar na mitigação de alguns dos desafios da mobilidade urbana. Os operadores de gestão de trânsito e de infraestruturas de estradas (em inglês, road infrastructure and traffic management operators — RITMOs) estão limitados no que toca a extrair valor de um sempre crescente volume de Dados Espaciotemporais em Larga Escala no domínio da Mobilidade e Tráfego (em inglês, Mobility- and Traffic-related Big Spatiotemporal Data —MobiTrafficBD) que estão a ser colecionados e recolhidos. Os trabalhos de investigação sobre os tópicos de Big Data, Dados Espaciotemporais e, especialmente, de MobiTrafficBD, estão dispersos, e a literatura existente não oferece uma metodologia comum e concreta para preparar, configurar, implementar e usar uma plataforma (framework) baseada em tecnologias Big Data para gerir o ciclo de vida de dados espaciotemporais em larga escala, com ênfase nas série temporais georreferenciadas (em inglês, geo-referenced time series — GRTS) e eventos espacio- temporais (em inglês, spatiotemporal events — ST Events), extrair valor destes dados e apoiar os RITMOs nos seus processos de decisão. Esta dissertação doutoral propõe uma metodologia prescritiva orientada a dados, para o design, desenvolvimento e implementação de plataformas de MobiTrafficBD, focadas em GRTS e ST Events. Além de uma revisão de literatura completa nas áreas de Dados Espaciotemporais, Big Data e na junção destas áreas através do conceito de MobiTrafficBD, a metodologia proposta contem um conjunto de características gerais, requisitos técnicos, componentes lógicos, fluxos de dados e modelos de infraestrutura tecnológica, bem como diretrizes e boas práticas para investigadores, profissionais e outras partes interessadas, como RITMOs, com o objetivo de guiá-los pelas fases de design, desenvolvimento e implementação de qualquer pla- taforma MobiTrafficBD. Este trabalho deve ser visto como um guia metodológico de suporte, baseado em Arqui- teturas de Referência e diretrizes amplamente utilizadas, mas enriquecido com as característi- cas e assuntos implícitos relacionados com Dados Espaciotemporais em Larga Escala, como no caso de GRTS e ST Events. A metodologia proposta foi avaliada e demonstrada em vários cenários reais no âmbito de projetos de investigação financiados pela Comissão Europeia e pelo Governo português, nos quais foram implementados métodos, ferramentas e tecnologias nas áreas de Gestão de Dados, Processamento de Dados e Ciência e Visualização de Dados em plataformas MobiTrafficB

Patterns of mobility in a smart city

Transportation data in smart cities is becoming increasingly available. This data allows building meaningful, intelligent solutions for city residents and city management authorities, the so-called Intelligent Transportation Systems. Our research focused on Lisbon mobility data, provided by Lisbon municipality. The main research objective was to address mobility problems, interdependence, and cascading effects solutions for the city of Lisbon. We developed a data-driven approach based on historical data with a strong focus on visualization methods and dashboard creation. Also, we applied a method based on time series to do prediction based on the traffic congestion data provided. A CRISP-DM approach was applied, integrating different data sources, using Python. Hence, understand traffic patterns, and help the city authorities in the decision-making process, namely more preparedness, adaptability, responsiveness to events.Os dados de transporte, no âmbito das cidades inteligentes, estão cada vez mais disponíveis. Estes dados permitem a construção de soluções inteligentes com impacto significativo na vida dos residentes e nos mecanismos das autoridades de gestão da cidade, os chamados Sistemas de Transporte Inteligentes. A nossa investigação incidiu sobre os dados de mobilidade urbana da cidade de Lisboa, disponibilizados pelo município. O principal objetivo da pesquisa foi abordar os problemas de mobilidade, interdependência e soluções de efeitos em cascata para a cidade de Lisboa. Para alcançar este objetivo foi desenvolvida uma metodologia baseada nos dados históricos do transito no centro urbano da cidade e principais acessos, com uma forte componente de visualização. Foi também aplicado um método baseado em series temporais para fazer a previsão das ocorrências de transito na cidade de Lisboa. Foi aplicada uma abordagem CRISP-DM, integrando diferentes fontes de dados, utilizando Python. Esta tese tem como objetivo identificar padrões de mobilidade urbana com análise e visualização de dados, de forma a auxiliar as autoridades municipais no processo de tomada de decisão, nomeadamente estar mais preparada, adaptada e responsiva

Data‐driven solutions to understand disruptive problems in transportation—the lisbon case study

Albuquerque, V., Oliveira, A., Barbosa, J. L., Rodrigues, R. S., Andrade, F., Dias, M. S., & Ferreira, J. C. (2021). Smart cities: Data‐driven solutions to understand disruptive problems in transportation—the lisbon case study. Energies, 14(11), 1-25. [3044]. https://doi.org/10.3390/en14113044Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP‐DM approach using Python. We focused on mobility problems and interdependence and cascading‐effect solutions for the city of Lisbon. We developed data‐driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events.publishersversionpublishe

Privacy-Friendly Mobility Analytics using Aggregate Location Data

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201

Interactive, multi-purpose traffic prediction platform using connected vehicles dataset

Traffic congestion is a perennial issue because of the increasing traffic demand yet limited budget for maintaining current transportation infrastructure; let alone expanding them. Many congestion management techniques require timely and accurate traffic estimation and prediction. Examples of such techniques include incident management, real-time routing, and providing accurate trip information based on historical data. In this dissertation, a speech-powered traffic prediction platform is proposed, which deploys a new deep learning algorithm for traffic prediction using Connected Vehicles (CV) data. To speed-up traffic forecasting, a Graph Convolution -- Gated Recurrent Unit (GC-GRU) architecture is proposed and analysis of its performance on tabular data is compared to state-of-the-art models. GC-GRU's Mean Absolute Percentage Error (MAPE) was very close to Transformer (3.16 vs 3.12) while achieving the fastest inference time and a six-fold faster training time than Transformer, although Long-Short-Term Memory (LSTM) was the fastest in training. Such improved performance in traffic prediction with a shorter inference time and competitive training time allows the proposed architecture to better cater to real-time applications. This is the first study to demonstrate the advantage of using multiscale approach by combining CV data with conventional sources such as Waze and probe data. CV data was better at detecting short duration, Jam and stand-still incidents and detected them earlier as compared to probe. CV data excelled at detecting minor incidents with a 90 percent detection rate versus 20 percent for probes and detecting them 3 minutes faster. To process the big CV data faster, a new algorithm is proposed to extract the spatial and temporal features from the CSV files into a Multiscale Data Analysis (MDA). The algorithm also leverages Graphics Processing Unit (GPU) using the Nvidia Rapids framework and Dask parallel cluster in Python. The results show a seventy-fold speedup in the data Extract, Transform, Load (ETL) of the CV data for the State of Missouri of an entire day for all the unique CV journeys (reducing the processing time from about 48 hours to 25 minutes). The processed data is then fed into a customized UNet model that learns highlevel traffic features from network-level images to predict large-scale, multi-route, speed and volume of CVs. The accuracy and robustness of the proposed model are evaluated by taking different road types, times of day and image snippets of the developed model and comparable benchmarks. To visually analyze the historical traffic data and the results of the prediction model, an interactive web application powered by speech queries is built to offer accurate and fast insights of traffic performance, and thus, allow for better positioning of traffic control strategies. The product of this dissertation can be seamlessly deployed by transportation authorities to understand and manage congestions in a timely manner.Includes bibliographical references

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change