This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone’s GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model’s ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model’s capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin–destination (OD) matrix to better understand how people move within the city.

This study proposes an automatic gear shift classification algorithm in M1 category vehicles using data acquired through the onboard diagnostic system (OBD II) and GPS. The proposed approach is based on the analysis of identification parameters (PIDs), such as manifold absolute pressure (MAP), revolutions per minute (RPM), vehicle speed (VSS), torque, power, stall times, and longitudinal dynamics, to determine the efficiency and behavior of the vehicle in each of its gears. In addition, the unsupervised K-means algorithm was implemented to analyze vehicle gear changes, identify driving patterns, and segment the data into meaningful groups. Machine learning techniques, including K-Nearest Neighbors (KNN), decision trees, logistic regression, and Support Vector Machines (SVMs), were employed to classify gear shifts accurately. After a thorough evaluation, the KNN (Fine KNN) model proved to be the most effective, achieving an accuracy of 99.7%, an error rate of 0.3%, a precision of 99.8%, a recall of 99.7%, and an F1-score of 99.8%, outperforming other models in terms of accuracy, robustness, and balance between metrics. A multiple linear regression model was developed to estimate instantaneous fuel consumption (in L/100 km) using the gear predicted by the KNN algorithm and other relevant variables. The model, built on over 66,000 valid observations, achieved an R² of 0.897 and a root mean square error (RMSE) of 2.06, indicating a strong fit. Results showed that higher gears (3, 4, and 5) are associated with lower fuel consumption. In contrast, a neutral gear presented the highest levels of consumption and variability, especially during prolonged idling periods in heavy traffic conditions. In future work, we propose integrating this algorithm into driver assistance systems (ADAS) and exploring its applicability in autonomous vehicles to enhance real-time decision making. Such integration could optimize gear shift timing based on dynamic factors like road conditions, traffic density, and driver behavior, ultimately contributing to improved fuel efficiency and overall vehicle performance.

Vehicles powered by internal combustion engines are one of the main sources of emissions of harmful gases to the environment, which generates a deterioration of air quality and the health of people, pollutant emissions emitted by mobile sources are directly related to how much fuel the vehicle fleet consumes. The objective of this research is to analyze the operating parameters of a vehicle equipped with an engine with a multipoint fuel injection system (MPI) in the local environment by varying the characteristics of air filtering, fuel filter, and type of gasoline to improve fuel consumption performance. For the experimental section, a vehicle is driven following the RDE real emission cycle methodology while a data logger acquires data from the vehicle’s OBD II. To reduce the number of experimental runs, a full-factorial design with three factors and two levels was used. Within the research, it is found that the most important parameter in the reduction of fuel consumption is the type of fuel. The use of a fuel with 92 octanes can achieve reductions of up to 24.38% if compared with extra gasoline of 87 octanes.