Street vending is a complex systemic phenomenon in most cities worldwide, with different intensities and features. In the Mexican case, it is an activity with remnants of a precolonial logic in its spatial distribution. Thus, a low correlation exists between the street vending government regulations and the actual day by day organization of the activity. Certain authors have suggested and compiled an econometric model that considers some variables to comprehend the phenomenon better. All the variables came with the detailed information except for the territorial aspect. Thus, an accessibility tool was created to provide a robust location profile, using official variables related to socioeconomic topics recommended by the World Bank. The resulting database was then analyzed by Machine Learning prediction models. The results provided a map with spatial categorization of the street vending activity, with a solid correlation ( $0.509\pm 0.039$) to the jobs variable.

Urban renewal projects worldwide focus mainly on resolving motorized, personal, and low occupancy problems instead of sustainable mobility. As part of the process, traditional field audits have a high cost in time and resources. This paper reviews a spatial model of accessibility and habitability of the streets, oriented to the location of the volume of people moving sustainably out of an extensive street network. The exercise site is in the Monterrey Metropolitan Area, the second largest in Mexico. Here, the population that moves sustainably as the collective (public and enterprise transportation) and the active (cycling, walking, and others) represents a considerable portion (49%) of travelers, thus, confirming the need for intervention. The spatial model is elaborated in a Geographical Information System (GIS), and the main results are compared with the actual public transport demand using a neural networks process. The results of the tool as a predictor have a 91% efficiency, making it possible to determine the location of urban renewal projects related to the volume of people moving sustainably.

The construction of urban and transport indicators aims for a better diagnosis that enables technical and precise decision-making for the public administration or private investment. Therefore, it is common to make comparisons and observe which has better diagnosis results in a diversity of indexes and models. The present study made a comparative analysis of spatial models using artificial intelligence to estimate transport demand. To achieve this goal, the audit field was recollected in specific urban corridors to measure the indicators. A study case in Querétaro, an emergent city in the Mexican region known as El Bajío, is conducted. Two similar urban avenues in width and length and close to each other were selected to apply a group of spatial models, evaluating the avenues by segments and predicting the public transport demand. The resulting database was analyzed using Artificial Neural Networks. It displays specific indicators that have around 80% of correlations. The results facilitate the localization of the avenue segments with the most volume of activity, supporting interventions in urban renewal and sustainable transportation projects.

Emerging mid-sized cities in the Global South face growing challenges in aligning transport infrastructure with equity and safety objectives under conditions of rapid urban expansion. While accessibility metrics are widely used in transport planning, their ability to capture functional inequalities and safety-related dynamics remains limited, particularly in corridor-level assessments. This study examines structural and functional accessibility patterns along two planned BRT corridors in Querétaro, Mexico, an emerging mid-sized Latin American city. The analysis integrates spatial accessibility indicators, selected urban process proxies related to inequality and road safety, and AI-based modeling to explore non-linear spatial associations across fine-grained corridor segments. Structural accessibility is evaluated using network-based indicators, while functional accessibility reflects observed service dynamics and operational conditions. Spatial correlations and artificial neural networks are employed as exploratory tools to identify co-occurring patterns rather than causal relationships. Results reveal pronounced spatial mismatches between structural and functional accessibility, socio-spatial marginalization, and crash concentration, highlighting corridor segments where future BRT implementation may either reinforce or mitigate existing inequalities. By framing BRT corridors as test cases, the study contributes a transferable diagnostic framework for assessing accessibility–equity–safety tensions in emerging mid-sized cities.