Ernesto De la Cruz-Nicolás

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.

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.