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Respiratory viruses, such as COVID-19, are spread over time and space based on human-to-human interactions. Human mobility plays a key role in the propagation of the virus. Different types of sensors in smart cities are able to continuously monitor traffic-related human mobility, showing the impact of COVID-19 on traffic volumes and patterns. In a similar way, traffic volumes measured by smart traffic sensors provide a proxy variable to capture human mobility, which is expected to have an impact on new COVID-19 infections. Adding traffic data from smart city sensors to machine learning models designed to estimate upcoming COVID-19 incidence values should provide optimized results compared to models based on COVID-19 data alone. This paper proposes a novel model to extract spatio-temporal patterns in the spread of the COVID-19 virus for short-term predictions by organizing COVID-19 incidence and traffic data as interrelated temporal sequences of spatial images. The model is trained and validated with real data from the city of Madrid in Spain for 84 weeks, combining information from 4372 traffic measuring points and 143 COVID-19 PCR test centers. The results are compared with a baseline model designed for the extraction of spatio-temporal patterns from COVID-19-only sequences of images, showing that using traffic information enhances the results when forecasting a new wave of infections (MSE values are reduced by a 70% factor). The information that traffic data has on the spread of the COVID-19 virus is also analyzed, showing that traffic data alone is not sufficient for accurate COVID-19 forecasting. [ABSTRACT FROM AUTHOR]

The COVID-19 virus continues to generate waves of infections around the world. With major areas in developing countries still lagging behind in vaccination campaigns, the risk of new variants that can cause re-infections worldwide makes the monitoring and forecasting of the evolution of the virus a high priority. Having accurate models able to forecast the incidence of the spread of the virus provides help to policymakers and health professionals in managing the scarce resources in an optimal way. In this paper, a new machine learning model is proposed to forecast the spread of the virus one-week ahead in a geographic area which combines mobility and COVID-19 incidence data. The area is divided into zones or districts according to the location of the COVID-19 measuring points. A traffic-driven mobility estimate among adjacent districts is proposed to capture the spatial spread of the virus. Traffic-driven mobility in adjacent districts will be used together with COVID-19 incidence data to feed a new deep learning LSTM-based model which will extract patterns from mobility-modulated COVID-19 incidence spatiotemporal data in order to optimize one-week ahead estimations. The model is trained and validated with open data available for the city of Madrid (Spain) for 3 different validation scenarios. A baseline model based on previous literature able to extract temporal patterns in COVID-19 incidence time series is also trained with the same dataset. The results show that the proposed model, based on the combination of traffic and COVID-19 incidence data, is able to outperform the baseline model in all the validation scenarios. [ABSTRACT FROM AUTHOR]

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Traffic accidents forecasting represents a major priority for traffic governmental organisms around the world to ensure a decrease in life, property, and economic losses. The increasing amounts of traffic accident data have been used to train machine learning predictors, although this is a challenging task due to the relative rareness of accidents, inter-dependencies of traffic accidents both in time and space, and high dependency on human behavior. Recently, deep learning techniques have shown significant prediction improvements over traditional models, but some difficulties and open questions remain around their applicability, accuracy, and ability to provide practical information. This paper proposes a new spatio-temporal deep learning framework based on a latent model for simultaneously predicting the number of traffic accidents in each neighborhood in Madrid, Spain, over varying training and prediction time horizons. [ABSTRACT FROM AUTHOR]

In this paper the performance of different Machine Learning and Deep Learning approaches is evaluated in problems related to green mobility in big cities. Specifically, the forecasting of bike sharing demand in Madrid and Barcelona (Spain) is approached, for different prediction time-horizons, and also a problem of cable car demand forecasting in Madrid city. An important number of predictive variables are considered, which are grouped into four different sets (categorical/calendrical, persistence-based, meteorological and, as a novelty of the paper, information about analogue past instances), whose relevance is studied for all cases. A feature selection mechanism is also incorporated in order to improve the prediction accuracy of the proposed algorithms. A total of 12 different multivariate regression techniques are implemented, covering from Machine Learning methods to time-series Deep Learning approaches. Excellent results in all the prediction problems approached are reported. Finally, the consequences of obtaining accurate prediction in these three problem of green mobility in big cities are discussed. In addition, it is studied how the results could be exported to other similar cases in more general urban mobility studies. Novelties of the work include: (1) Addressing the forecast problem of passenger flow on a cable car using ML and DL multivariate techniques; (2) using the demand of analogous past instances as an additional feature to solve the demand prediction problems; and (3) the extraction of global conclusions about feature relevance when addressing a demand forecasting problem in green mobility. [ABSTRACT FROM AUTHOR]

Copyright of Virtual Archaeology Review is the property of Virtual Archaeology Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• An innovative hybrid solar-biomass system in a multi-family building was studied. • Deep reinforcement learning was applied to improve the control of the system. • Energy consumption was reduced by 35% in winter compared to a standard control. • No significant energy savings were obtained in summer despite innovative elements. Concerns related to climate change put renewable energy at the centre of most of the policies aimed at achieving a deep decarbonisation of the building sector. The combined use of two or more renewable energy sources in the same energy system can lead to an increase in the total share of renewable energy and in the flexibility of the system. In this direction, the SolBio-Rev project aims to develop an innovative system that uses solar thermal collectors and a biomass boiler to meet energy demand in buildings in different climatic regions. An advanced control that used deep reinforcement learning techniques was considered in this paper to find an optimal control strategy for a specific SolBio-Rev system installed in a standard multi-family residential building located in Madrid. The advanced control was developed to minimize the total cost of operation of the system. The results indicated that the advanced control strategy achieved a cost reduction of 35% in winter, compared to a standard rule-based control strategy. However, the improved control was not able to produce a significant cost reduction in summer. [ABSTRACT FROM AUTHOR]

The increase of road traffic in large cities during the last years has produced that long and short-term traffic flow forecasting is a critical need for the authorities. The availability of good traffic flow prediction methods is a must to make informed decisions concerning (punctual) traffic congestions. Previous work has shown that the accuracy of these methods decreases if we consider urban traffic and long-term predictions. In this paper we present a hybrid model, combining a Convolutional Neural Network and a Bidirectional Long–Short-Term Memory network, and apply it to long-term traffic flow prediction in urban routes. This model combines the capability of CNN to extract hidden valuable features from the input model and the capability of BiLSTM to understand the temporal context. In order to assess the usefulness of our model, we considered four streets of the city of Madrid with different characteristics and compared the results of our proposed model with the ones obtained by eight widely used baseline models. The results show that our hybrid model outperforms the baseline models with respect to three metrics commonly used in regression: mean absolute error, root mean squared error and accuracy. [ABSTRACT FROM AUTHOR]

Traffic flow/volume data are commonly used to calibrate and validate traffic simulation models. However, these data are generally obtained from stationary sensors (e.g. loop detectors), which are expensive to install and maintain and cover a small number of locations in the transport network. On the other hand, Floating Car Data (FCD) are readily available at the network level, usually from a sample of vehicles. We present an indirect traffic flow estimation approach using transfer learning to address the traffic flow data scarcity and model generalization across cities. Using two cities (Paris and Madrid) as study areas, we demonstrate the indirect estimation using only exogenous features for flow prediction, mirroring limited predictive features without past link flows. Subsequently, we use the model pre‐trained on data from Paris city and test on data from Madrid city, and investigate the scenarios for successful transfer learning. Overall, the training set must adequately capture the flow‐speed relationship for successful indirect flow estimation. Transfer learning is beneficial when the data for the target task is minimal, in which case transferred models outperform newly trained models from scratch. Using real‐world and publicly available data, our approach and models can help scale a smaller traffic flow dataset to a larger sample across cities. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting. [ABSTRACT FROM AUTHOR]

Time series forecasting of passenger demand is crucial for optimal planning of limited resources. For smart cities, passenger transport in urban areas is an increasingly important problem, because the construction of infrastructure is not the solution and the use of public transport should be encouraged. One of the most sophisticated techniques for time series forecasting is Long Short Term Memory (LSTM) neural networks. These deep learning models are very powerful for time series forecasting but are not interpretable by humans (black-box models). Our goal was to develop a predictive and linguistically interpretable model, useful for decision making using large volumes of data from different sources. Our case study was one of the most demanded bus lines of Madrid. We obtained an interpretable model from the LSTM neural network using a surrogate model and the 2-tuple fuzzy linguistic model, which improves the linguistic interpretability of the generated Explainable Artificial Intelligent (XAI) model without losing precision. [ABSTRACT FROM AUTHOR]

COVID-19 has caused millions of infections and deaths over the last 2 years. Machine learning models have been proposed as an alternative to conventional epidemiologic models in an effort to optimize short- and medium-term forecasts that will help health authorities to optimize the use of policies and resources to tackle the spread of the SARS-CoV-2 virus. Although previous machine learning models based on time pattern analysis for COVID-19 sensed data have shown promising results, the spread of the virus has both spatial and temporal components. This manuscript proposes a new deep learning model that combines a time pattern extraction based on the use of a Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN) over a preceding spatial analysis based on a Convolutional Neural Network (CNN) applied to a sequence of COVID-19 incidence images. The model has been validated with data from the 286 health primary care centers in the Comunidad de Madrid (Madrid region, Spain). The results show improved scores in terms of both root mean square error (RMSE) and explained variance (EV) when compared with previous models that have mainly focused on the temporal patterns and dependencies. [ABSTRACT FROM AUTHOR]

In addition to classification and regression, outlier detection has emerged as a relevant activity in deep learning. In comparison with previous approaches where the original features of the examples were used for separating the examples with high dissimilarity from the rest of the examples, deep learning can automatically extract useful features from raw data, thus removing the need for most of the feature engineering efforts usually required with classical machine learning approaches. This requires training the deep learning algorithm with labels identifying the examples or with numerical values. Although outlier detection in deep learning has been usually undertaken by training the algorithm with categorical labels—classifier—, it can also be performed by using the algorithm as regressor. Nowadays numerous urban areas have deployed a network of sensors for monitoring multiple variables about air quality. The measurements of these sensors can be treated individually—as time series—or collectively. Collectively, a variable monitored by a network of sensors can be transformed into a map. Maps can be used as images in machine learning algorithms—including computer vision algorithms—for outlier detection. The identification of anomalous episodes in air quality monitoring networks allows later processing this time period with finer‐grained scientific packages involving fluid dynamic and chemical evolution software, or the identification of malfunction stations. In this work, a Convolutional Neural Network is trained—as a regressor—using as input Ozone‐urban images generated from the Air Quality Monitoring Network of Madrid (Spain). The learned features are processed by Density‐based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for identifying anomalous maps. Comparisons with other deep learning architectures are undertaken, for instance, autoencoders—undercomplete and denoizing—for learning salient features of the maps and later to use as input of DBSCAN. The proposed approach is able efficiently find maps with local anomalies compared to other approaches based on raw images or latent features extracted with autoencoders architectures with DBSCAN. [ABSTRACT FROM AUTHOR]

In this paper we approach the problem of predicting air quality in the region of Madrid using long short term memory recurrent artificial neural networks. Air quality, in this study, is represented by the concentrations of a series of air pollutants which are proved as risky for human health such as CO, NO 2 , O 3 , PM10, SO 2 and airborne pollen concentrations of two genus (Plantago and Poaceae). These concentrations are sampled in a set of locations in the city of Madrid. Instead of training an array of models, one per location and pollutant, several comprehensive deep network configurations are compared to identify those which are able to better extract relevant information out of the set of time series in order to predict one day-ahead air quality. The results, supported by statistical evidence, indicate that a single comprehensive model might be a better option than multiple individual models. Such comprehensive models represent a successful tool which can provide useful forecasts that can be thus applied, for example, in managerial environments by clinical institutions to optimize resources in expectation of an increment of the number of patients due to the exposure to low air quality levels. • A method to forecast CO, NO2, O3, PM10, SO2 and pollen concentrations is applied to a case study in different locations in Madrid. • The method presents and compares different topologies of long short term memory in recurrent neural networks. • A non-parametric hypothesis test is used to backup the decision to select the most accurate and robust configuration. • Results show increases in accuracy when compared to two traditional approaches which are used as a benchmark. • A comprehensive deep network configurations comparison to predict one day-ahead air quality. [ABSTRACT FROM AUTHOR]

In the framework of extreme pollution concentrations being more and more frequent in many cities nowadays, air quality forecasting is crucial to protect public health through the anticipation of unpopular measures like traffic restrictions. In this work, we develop the core of a 48 h ahead forecasting system which is being deployed for the city of Madrid. To this end, we investigate the predictive power of a set of neural network models, including several families of deep networks, applied to the task of predicting nitrogen dioxide concentrations in an urban environment. Careful feature engineering on a set of related magnitudes as meteorology and traffic has proven useful, and we have coupled these neural models with mesoscale numerical pollution forecasts, which improve precision by up to 10%. The experiments show that some neural networks and ensembles consistently outperform the reference models, particularly improving the Naive model's results from around (20%) up to (57%) for longer forecasting horizons. However, results also reveal that deeper networks are not particularly better than shallow ones in this setting. [ABSTRACT FROM AUTHOR]