From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government

How can Data Analytics Results be Exploited in the Early Phase of Product Development? 13 Design Principles for Data-Driven Product Planning

The megatrend digitalization turns mechatronic products into continuous collectors and generators of use phase data. By analyzing this data, manufacturers can uncover valuable insights about the products and the users. Especially in product planning, these insights could be used to plan promising future product generations. The systematic exploitation of data analytics results, however, represents a serious challenge, as research on the topic is still scarce. In this paper, we present 13 design principles for exploiting data analytics results in product planning. The results are based on a systematic literature review and a workshop with a research consortium. The evaluation of the design principles is demonstrated with a real case of a manufacturing company. The identified design principles represent a first contribution to a still scarcely explored research field

State-of-the-Art Review and Synthesis: A Requirement-based Roadmap for Standardized Predictive Maintenance Automation Using Digital Twin Technologies

Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance. Yet, it continues to face numerous limitations such as poor explainability, sample inefficiency of data-driven methods, complexity of physics-based methods, and limited generalizability and scalability of knowledge-based methods. This paper proposes leveraging Digital Twins (DTs) to address these challenges and enable automated PMx adoption at larger scales. While we argue that DTs have this transformative potential, they have not yet reached the level of maturity needed to bridge these gaps in a standardized way. Without a standard definition for such evolution, this transformation lacks a solid foundation upon which to base its development. This paper provides a requirement-based roadmap supporting standardized PMx automation using DT technologies. A systematic approach comprising two primary stages is presented. First, we methodically identify the Informational Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a foundation from which any unified framework must emerge. Our approach to defining and using IRs and FRs to form the backbone of any PMx DT is supported by the track record of IRs and FRs being successfully used as blueprints in other areas, such as for product development within the software industry. Second, we conduct a thorough literature review spanning fields to determine the ways in which these IRs and FRs are currently being used within DTs, enabling us to point to the specific areas where further research is warranted to support the progress and maturation of requirement-based PMx DTs.Comment: (1)This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202

Prescripción de las infracciones al reglamento nacional de tránsito y recaudación en el Servicio de Administración Tributaria, Tarapoto, 2022

La investigación tuvo como objetivo general determinar la relación que existe entre la prescripción de infracciones al Reglamento Nacional de Tránsito y la recaudación en el Servicio de Administración Tributaria, Tarapoto 2022, respecto a su estructura metodológica comprende investigación tipo básica, enfoque cuantitativo, diseño no experimental, de corte transversal y nivel descriptivo correlacional, la muestra estuvo constituida por 100 expedientes, y en la recolección de datos se usó fichas de verificación. Los resultados principales revelaron que el nivel de prescripción de infracciones es medio, en 72%, de igual manera, el estado actual de recaudación es regular en 68%. Quedó demostrado que la prescripción de oficio y vía defensa se relacionan significativamente con la recaudación, por cuanto se constató mediante prueba Chi2 un nivel de significancia igual 0.00. Por lo tanto, se concluyó que existe relación significativa entre la prescripción de infracciones al reglamento nacional de tránsito y la recaudación en el Servicio de Administración Tributaria, dado que el p-valor (0,000) es menor al nivel de significancia (α=0,050), además, el Chi cuadrado calculado ( 2= 50,980) es mayor que el Chi cuadrado de la tabla ( 2= 3,841) por lo tanto, se aceptó la hipótesis de la investigación