Bicycle use has become more important today, but more information and planning models are needed to implement bike lanes that encourage cycling. This study aimed to develop a methodology to predict the speed a cyclist can reach in an urban environment and to provide information for planning cycling infrastructure. The methodology consisted of obtaining GPS data on longitude, latitude, elevation, and time from a smartphone of two groups of cyclists to calculate the speeds and slopes through a model based on a recurrent short-term memory (LSTM) type neural network. The model was trained on 70% of the dataset, with the remaining 30% used for validation and varying training epochs (100, 200, 300, and 600). The effectiveness of recurrent neural networks in predicting the speed of a cyclist in an urban environment is shown with determination coefficients from 0.77 to 0.96. Average cyclist speeds ranged from 6.1 to 20.62 km/h. This provides a new methodology that offers valuable information for various applications in urban transportation and bicycle line planning. A limitation can be the variability in GPS device accuracy, which could affect speed measurements and the generalizability of the findings.

The use of cold asphalt mixtures has aroused interest in recent years because it is a technique that reduces the use of fuels and thus the carbon footprint in the construction and maintenance of pavements, however its use has been limited in patching applications and low-traffic pavements, however, these mixtures have low resistance at an early age and high amounts of voids. The objective of this research was the development of a cold mixture with replacement of the mineral filler with ternary cementitious fillers that together form hydration products and provide greater resistance in the presence of water. The ternary cementitious fillers are composed of granulated blast furnace slag (EGAH), fly ash (CV) and an alkaline activator, Portland cement (CP) or calcium hydroxide (lime), in order to compare which of these activators provides greater performance different mixtures were prepared with partial and total substitution, to evaluate in three curing conditions and observe the behavior of the mixtures in the presence of water. It was proven that replacement with both types of cementitious fillers significantly reduces the effects of moisture at an early age. For the curing condition of 3 days at 25 °C, the reference sample obtained a loss of stability of 30,78 %, the samples with CP of 7,61 and 7,56 %, and those of lime with 13,83 and 16,39 %, have a lower loss of stability in all cases, this same pattern is maintained over time. El uso de mezclas asfálticas en frío ha despertado el interés en los últimos años debido a que es una técnica que reduce el uso de combustibles y con ello, la huella de carbono en la construcción y mantenimiento de pavimentos, sin embargo, se ha limitado su uso en aplicaciones de bacheo y pavimentos de bajo tránsito, debido a que estas mezclas presentan baja resistencia a edad temprana y altas cantidades de vacíos. El objetivo de la presente investigación fue el desarrollo de una mezcla fría con sustitución del relleno mineral por rellenos cementantes ternarios que en conjunto forman productos de hidratación y brindan mayor resistencia en presencia de agua. Los rellenos cementantes ternarios están compuestos por escoria de alto horno granulada (EGAH), ceniza volante (CV) y un activador alcalino, ya sea cemento Portland (CP) o hidróxido de calcio (cal), a fin de comparar cuál de estos activadores brinda un mayor desempeño se prepararon distintas mezclas con sustitución parcial y total, para evaluar en tres condiciones de curado y observar el comportamiento de las mezclas en presencia de agua. Se comprobó que la sustitución con ambos tipos de rellenos cementantes reduce significativamente los efectos de la humedad a edad temprana. Para la condición de curado de 3 días a 25 °C, la muestra de referencia obtuvo una pérdida de estabilidad de 30,78 %, las muestras con CP de 7,61 y 7,56 %, y las de cal con 13,83 y 16,39 %, por lo que se observa que tienen una menor pérdida de estabilidad en todos los casos, este mismo patrón se mantiene en el tiempo.

Many studies focus on the location of bicycle stations rather than network design, often overlooking the cyclist’s experience, which depends on individual characteristics and the type of bicycle used. This study proposes a methodology for designing bicycle corridors using GPS data and autoregressive models with exogenous variables (ARX). Two experiments were conducted: (1) a fixed-route scenario varying cyclists and bicycles, and (2) a variable-route scenario using the cyclists’ own bicycles. Both experiments evaluated the model’s accuracy and “transferability” using historical datasets to predict subsequent speeds. Results show an R-value of 0.94 for fixed routes and 0.93 for variable routes, with lags of 255 and 139 historical data points, respectively. The best-performing model for fixed routes achieved an MAE of 2.01 km/h, an RMSE of 2.55 km/h, a MAPE of 0.13%, and an R-value of 0.92 with 238 lags; with only 2 lags, the R-value was 0.82. Public and six-speed bicycles demonstrated greater transfer compatibility than electric ones. Finally, the model (using two lags) was applied to the road network in Querétaro to create a preliminary corridor design. The study concludes that while the model is useful—and could better represent cyclist behavior with additional data—it should not be viewed as a universal solution.

Accelerated industrial and population growth in the Queretaro Metropolitan Area has intensely strained the local public transportation system, which undermines daily mobility through inefficient route design and extended wait times. To tackle these structural issues without relying on governmental intervention, this study presents a new collaborative solution grounded in Design Thinking and Human-Centered Design methodologies. By prioritizing actual commuter needs, the present work developed SIMOT (Transport Monitoring System), an open crowdsourced mobile application that allows for real-time public transportation tracking through user-generated georeferencing. The methodological framework consists in four phases: empathizing, defining, ideating, and testing. The data compilation relied upon ethnographic tools, including participant observation, and semi-structured interviews, as well as focus groups conducted with the Autonomous University of Querétaro alumni and the municipal school transport staff. These results revealed that SIMOT cuts off commute uncertainty and improves user perception regarding wait times. Furthermore, the platform proved highly successful due to its user friendly interface, free availability, and low mobile data consumption. Ultimately, SIMOT demonstrates that user-driven technological innovation and cooperative community action can effectively make up for public infrastructure shortcomings, letting citizens improve everyday urban mobility and optimize family schedules across cities.