Odette Alejandra Pliego-Martínez

Accurately modelling and predicting traffic congestion is essential for effective traffic management in dynamic urban environments. This study introduces a hybrid model that combines a deep neural network architecture, designed to capture long-term temporal dependencies, with physics-based approaches to traffic modelling. The deep learning component identifies complex temporal patterns and non-linear behaviours in traffic evolution, while the physics-based component incorporates dynamic constraints that enhance the model’s robustness and ability to generalise. This integration leverages the predictive power of machine learning while maintaining consistency with the fundamental principles governing vehicle flow. Experiments conducted using real-world traffic data demonstrate strong predictive performance, establishing a solid foundation for the development of intelligent transportation management systems and providing advanced tools to support adaptive mobility optimisation in congested urban settings.

Emotion recognition in speech has gained increasing relevance in recent years, enabling more personalized interactions between users and automated systems. This paper presents the development of a dataset of features obtained from RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) to classify emotions in speech. The paper highlights audio processing techniques such as silence removal and framing to extract features from the recordings. The features are extracted from the audio signals using spectral techniques, time-domain analysis, and the discrete wavelet transform. The resulting dataset is used to train a neural network and the support vector machine learning algorithm. Cross-validation is employed for model training. The developed models were optimized using a software package that performs hyperparameter tuning to improve results. Finally, the emotional classification outcomes were compared. The results showed an emotion classification accuracy of 0.654 for the perceptron neural network and 0.724 for the support vector machine algorithm, demonstrating satisfactory performance in emotion classification.

The identification of variables triggering traffic congestion points in cities has been revealed as an extraordinarily complex task, involving characterization, prediction, and forecasting activities of traffic congestion points. The exhaustive literature review indicates that key factors influencing urban traffic congestion include traffic incidents, road infrastructure conditions, the day of the week, time of day, the month, workweeks, and vacation periods. However, there is a lack of quantitative representations of traffic that allows us to accurately assess the degree of influence of relevant traffic variables such as incidents and service locations in the generation of traffic congestion points. In this context, the main objective of this research work focuses on identifying the most significant variables causing traffic congestion points through the use of mathematical techniques. This aims to contribute to the development of models for mitigating traffic congestion. The case study of traffic, incidents, and services in Mexico City is used in this work.

Esta investigación examina cómo la proximidad de puntos de interés, como tiendas, restaurantes, hospitales, escuelas, parques y oficinas, influye en la congestión del tráfico en áreas urbanas. Utilizando un enfoque de densidad de Kernel, el análisis busca entender cómo la concentración de estos puntos afecta los patrones de tráfico y la densidad en diferentes zonas. El método de densidad de Kernel permite estimar la intensidad del tráfico en áreas específicas, creando centroides de áreas dinámicas que muestra las regiones con mayor y menor congestión. El estudio integra datos geoespaciales sobre la ubicación de puntos de interés y los patrones de tráfico, identificando correlaciones entre la cercanía de estos puntos y los niveles de congestión. Los resultados indican que una alta densidad de puntos de interés en una zona tiende a incrementar el tráfico y la congestión, especialmente en áreas de alto atractivo comercial o recreativo. El estudio proporciona información valiosa para urbanistas y responsables de transporte al ofrecer alternativas sobre cómo la ubicación de puntos de interés puede ser optimizada para mejorar la fluidez del tráfico y reducir la congestión en áreas urbanas.

Traffic congestion in urban areas is a complex phenomenon influenced by various factors. The location of essential city services such as hospitals, schools, shopping centers, and workplaces plays a crucial role in generating vehicular congestion. Additionally, events like traffic accidents, ongoing constructions, adverse weather conditions, and other incidents can have a significant impact on traffic congestion. The representation of traffic congestion through a mathematical model that takes into account the distances be-tween the location of services and traffic incidents makes it possible to characterize the dynamic state of congestion in specific city locations. The contribution of this research is the development of a model that characterizes the level of congestion based on the proximity of city services and traffic events. The proposed mathematical model provides a valuable tool for effectively addressing and mitigating congestion and also enables informed decision making and proactive actions to improve urban mobility.

Vehicular transportation is one of the most widely used modes in modern cities for reaching key destinations such as workplaces, healthcare facilities, recreational areas, and educational institutions, among others. However, the performance of vehicular traffic on these roads can vary significantly due to the influence of different environmental variables. In the literature, factors such as traffic incidents, weather conditions, road infrastructure, and driving habits, among others, have been identified as impacting vehicular traffic performance. In this context, predictive models have been developed to anticipate congestion at specific points in cities based on statistics, machine learning, simulation, and complex networks. This study proposes a novel index aimed at assessing the level of performance of vehicular traffic on streets based on the relationship among relevant urban environmental variables. This index is generated through Genetic Programming, considering a set of variables related to traffic, incidents, and services. The case study will focus on the streets of the Tlalpan Municipality in Mexico City.

The precise allocation of weights to criteria plays a fundamental role in multicriteria decision-making, exerting a significant influence on the obtained results. Ensuring an appropriate weighting of criteria is crucial for conducting a fair and accurate evaluation of various alternatives. In this context, we present an innovative solution that addresses the allocation of weights to attributes in datasets, aiming to overcome limitations and challenges associated with expert consultation in multicriteria problems. The proposed method is grounded in an objective approach and adopts a hybrid perspective by integrating the mathematical principles of Principal Component Analysis with the application of the Entropy Method. This method was implemented along with the exponential weighted sum model in a case study related to the classification of neighborhoods in Mexico City based on the level of marginalization. Results were compared with the marginalization index reported in official sources, using evaluation metrics MAE and MAPE with values of 0.24 and 11.3%, respectively. This research demonstrates the efficiency of the proposed method, which integrates techniques used for attribute weighting, providing a robust and reliable tool for decision-making.