A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective

One contemporary policy to deal with traffic congestion is the design and implementation of forecasting methods that allow users to plan ahead of time and decision makers to improve traffic management. Current data availability and growing computational capacities have increased the use of machine learning (ML) to address traffic prediction, which is mostly modeled as a supervised regression problem. Although some studies have presented taxonomies to sort the literature in this field, they are mostly oriented to classify the ML methods applied and a little effort has been directed to categorize the traffic forecasting problems approached by them. As far as we know, there is no comprehensive taxonomy that classifies these problems from the point of view of both traffic and ML. In this paper, we propose a taxonomy to categorize the aforementioned problems from both traffic and a supervised regression learning perspective. The taxonomy aims at unifying and consolidating categorization criteria related to traffic and it introduces new criteria to classify the problems in terms of how they are modeled from a supervised regression approach. The traffic forecasting literature, from 2000 to 2019, is categorized using this taxonomy to illustrate its descriptive power. From this categorization, different remarks are discussed regarding the current gaps and trends in the addressed traffic forecasting area

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC

White-box flight simulator built with system dynamics to support urban transportation decision-making and address induced travel demand

La demanda inducida de transporte (DIT) es un fenómeno en el que la construcción de nuevas vías aumenta el uso de automóviles privados. La DTI se ha medido y corroborado mediante modelos econométricos que dan cuenta de cuánta demanda de viajes puede inducirse después de la construcción de nuevas vías. Sin embargo, este enfoque econométrico no tienepretensiones de causalidad en su estructura interna (enfoque de caja negra). Más allá de las contribuciones de los modelos de caja negra, sigue siendo necesario explicar estructuralmente los DITpara comprender e identificar sus causas; así pues, este enfoque permite a los responsables políticos diseñar políticas integrales para abordar los DITen un contexto urbano en el que todavía se necesitan nuevas vías para garantizar la conectividad. En este artículo, presentamos un simulador de caja blanca basado en un modelo de Dinámica de Sistemas paraabordar los DIT yapoyar la toma de decisiones sobre el transporte urbano. A través del simulador desarrollado, es posible mejorar la comprensión causal de la DIT. Además, aunque las políticas consideradas para intervenir en este fenómeno tienen una connotación conceptual, el simulador es un medio para adquirir conocimientos sobre la complejidad estructural que subyace a la interacción entre las políticas y la DIT.Induced Travel Demand is a phenomenon (ITD) wherein building new road infrastructure increases private car use. ITD has been measured and corroborated by means of econometric models that give an account of how much travel demand can be induced after road construction, without claims of causality in their inner structure (black-box approach). However, beyond the contributions of black-box models, it is still necessary to explain structurally this phenomenon for understanding and identifying its causes, which then allow policy-makers to design comprehensive policies to deal with ITD in urban context wherein new roads are still needed to guarantee connectivity. In this paper, we present a white-box flight simulator based on a System Dynamics model to support urban transportation decision-making and address ITD. Through the simulator developed, it is possible to improve the causal understanding of ITD and, although the considered policies to intervene this phenomenon have a conceptual connotation, the simulator is a means to acquire knowledge of the structural complexity underlying the interaction between the policies and ITD

Analiza strukturne kompleksnosti povpraševanja po povzročenih potovanjih pri odločanju: pristop sistemske dinamike

Background and purpose: Induced travel demand (ITD) is a phenomenon where road construction increases vehicles’ kilometers traveled. It has been approached with econometric models that use elasticities as measure to estimate how much travel demand can be induced by new roads. However, there is a lack of “white-box” models with causal hypotheses that explain the structural complexity underlying this phenomenon. We propose a system dynamics model based on a feedback mechanism to explain structurally ITD. Methodology: A system dynamics methodology was selected to model and simulate ITD. First, a causal loop diagram is proposed to describe the ITD structure in terms of feedback loops. Then a stock-flows diagram is formulated to allow computer simulation. Finally, simulations are run to show the quantitative temporal evolution of the model built. Results: The simulation results show how new roads in the short term induce more kilometers traveled by vehicles already in use; meanwhile, in the medium-term, new traffic is generated. These new car drivers appear when better flow conditions coming from new roads increase attractiveness of car use. More cars added to vehicles already in use produce new traffic congestion, and high travel speeds provided by roads built are absorbed by ITD effects. Conclusion: We concluded that approaching ITD with a systemic perspective allows for identifying leverage points that contribute to design comprehensive policies aimed to cope with ITD. In this sense, the model supports decision-making processes in urban contexts wherein it is still necessary for road construction to guarantee connectivity, such as the case of developing countries.Ozadje in namen: Povpraševanje po povzročenih potovanjih (ang: induced travel demand, ITD) je pojav, kjer se izgradnjo cest povečuje prevoženih kilometrov na vozilo. ITD navadno analizirajo z ekonometričnimi modeli, ki up­orabljajo elastičnost za oceno koliko povpraševanja po povzročenih potovanjih lahko povzroči gradnja novih cest. V literaturi ne najdemo modelov »bele škratlje« z vzročno hipotezo, ki bi pojasnjevali strukturno kompleksnost tega pojava. V članku predlagamo model sistemske dinamike, ki temelji na mehanizmu povratne informacije, da pojasni strukturo ITD. Metodologija: Za modeliranje in simulacijo ITD smo uporabili metodologijo sistemske dinamike. Najprej smo izdelali diagram strukture ITD v smislu povratnih zank. Nato smo oblikovali diagram zalog in tokov, da smo lahko uporabili računalniško simulacijo. Na koncu smo izvedli simulacijo kvantitativno časovnega razvoja modela. Rezultati: Rezultati simulacije kažejo, kako nove ceste v kratkem času povzročajo več prevoženih kilometrov pri vo­zilih, ki so že v uporabi; v srednjeročnem obdobju pa povzročijo nastanek novega prometa. Pojavljajo se novi vozniki avtomobilov se pojavijo, ker boljši pogoji pretoka zaradi novih cest povečajo privlačnost uporabe avtomobila. Več novih avtomobilov skupaj z vozili, ki so že v uporabi, povzročijo prometne zastoje. Povečana hitrost potovanja, ki jo omogočajo zgrajene ceste, je omejena zaradi ITD učinkov. Zaključek: Pristop k analizi ITD s sistemskega vidika sistemskega omogoča ugotavljate finančno ravnovesje in prispeva k oblikovanju celovite politike obvladovanja ITD. V tem smislu je model podpira procese odločanja v urbanih okoljih, kjer se odloča o gradnji cest z namenom, da se zagotovi povezljivost znotraj države, na primer v državah v razvoju