The management of road traffic incidents is a problem faced by governments in many countries. Normally, road operators have the infrastructure in place to monitor such incidents, albeit in a reactive manner. In Spain, there are traffic cameras on major roads to check for possible incidents, however, incident notification is slow and not automated. As an alternative, this paper proposes a system for automatic real-time traffic alerts. Thus, 1,500 camera images from the Dirección General de Tráfico (DGT) deployed on the main Spanish roads are analyzed in real time every 4 minutes. These images are not preprocessed, they have different qualities and are also affected by weather conditions such as fog, rain, sun reflections, etc. The system uses several Deep Learning classification models trained on a well-known dataset of traffic images including flowing traffic, dense traffic, accidents and fires. These models are used to classify the DGT images in real time, with satisfactory initial results, detecting both flowing traffic and dense traffic.

Traffic congestion is a significant problem in many cities, leading to delays, increased fuel consumption, and environmental pollution. In recent years, optimization algorithms have been used to improve traffic control systems and reduce congestion. This paper proposes an optimization algorithm based on genetic algorithms to optimize traffic signal controllers in Cádiz, Spain, using the Simulation of Urban MObility (SUMO) traffic simulator. The proposed optimization algorithm is a promising solution for the optimization of traffic control systems, which can lead to more efficient traffic flow, reduced congestion, and improved environmental conditions.

Time-series analysis plays a crucial role in extracting meaningful patterns from sequential data, serving to gain insights into temporal trends. Specifically, the burgeoning urbanization trend accentuates the urgency of traffic forecasting in modern cities due to its socio-economic and environmental impact. While Deep Learning techniques, notably Long Short-Term Memory (LSTM) networks, have shown promise in predicting traffic, they often overlook contextual factors like weather and holidays. To address this challenge, this paper proposes a hybrid model combining LSTM with categorical and continuous data to forecast traffic volume on the Interstate 94 American highway. Incorporating weather, temporal patterns, and holidays, this study explores the performance of a model that includes contextual factors against standalone LSTM. The results show superior predictive accuracy for the proposed hybrid model. SHapley Additive exPlanations (SHAP) analysis reveals the influence of diverse features, emphasizing the significance of contextual attributes in enhancing traffic prediction.

Sentiment analysis has gained prominence in the digital era, particularly within social media contexts. This study explores the integration of metadata, such as reposts, favorites, or followers, alongside textual information for sentiment analysis. A dataset of posts in Spanish is created, filtering by a set of keywords (a total of 17) of interest to us. A total of 3,962 posts were collected along with their metadata. Then, they have been manually classified as positive, negative, and neutral, and processed. The research studies and compares different models like LightGBM, BERT, and a hybrid model combining BERT outputs with metadata. Evaluation metrics, such as F1-score and precision, were employed, revealing that the hybrid model achieved the highest F1-score of 69.4%, showcasing the effectiveness of merging text comprehension capabilities with contextual metadata. While the incorporation of metadata enhanced overall sentiment classification, precision varied across sentiment classes, with notable improvements in neutral and positive sentiments, over 16% and 8% respectively.

[EN] Citizens¿ experiences are posted on social-media platforms (e.g. Twitter, Facebook, etc.)
to share their opinions, report significant occurrences, or show situations of interest. In
this way, citizens can act as social sensors, providing useful information for public institutions, companies or other citizens in decision making. In this context, DIAPASON (unifieD
hybrId ApProach to microtext Analysis in Social-media crOwdseNsing) is a system that
explores text-based user-generated content to detect problems in a given community. This
chapter aims to present the knowledge acquisition, specification and conceptualisation
stages in the development of the ontological model of DIAPASON. To illustrate, we describe the case of Beach Quality problems., This publication is part of the R&D&i project PID2020-112827GB-I00,
funded by MICIU/AEI/10.13039/501100011033.

The constructional element of irony has been noted by Veale (2012) and Veale & Hao
(2010), who claim that ironic interpretation can be triggered by conventionalized constructions
that bear a high ironic potential, as is the case of about as X as Y (About as fun
as watching paint dry). In inferential pragmatics, Attardo (2000) identifies indices of irony
(e.g. agreement markers such as yeah , right , sure ), which, in our view, can be regarded
as contributing factors or constraints on the characterization of potentially ironic constructions.
The present paper addresses the constructional dimension of irony, with special
emphasis on the axiological element of potentially ironic constructions, and it explains
implicational constructions and attitudinal and denotational figures of speech in relation
to irony. Axiology relates to the positive or negative evaluation of the various elements of
an utterance and is based on its explicit content and its additional meaning implications.
For instance, in the example mentioned above, the axiological load of the construction
depends largely on the meaning implications of the monotonous and uneventful activity
of observing paint dry. The heavy attitudinal element of irony makes this figure of speech
a particularly fertile ground for the study of axiological neutrality and non-neutrality. The
present paper provides a preliminary approach to the strategies that serve the purpose of
triggering the ironic interpretation of utterances., This publication is part of the R&D&i project PID2020-112827GB-I00,
funded by MICIU/AEI/10.13039/501100011033.