Several hybrid regression models that predict hot stabilized vehicle fuel consumption and emission rates for light-duty vehicles and light-duty trucks are presented in this paper. Key input variables to these models are instantaneous vehicle speed and acceleration measurements. The energy and emission models described in this paper utilize data collected at the Oak Ridge National Laboratory (ORNL) that included fuel consumption and emission rate measurements (CO, HC, and ${\mathrm{NO}}_x)$ for five light-duty vehicles and three light-duty trucks as a function of the vehicle’s instantaneous speed and acceleration levels. The fuel consumption and emission models are found to be highly accurate as compared to the ORNL data, with coefficients of determination ranging from 0.92 to 0.99. Given that the models utilize the vehicle’s instantaneous speed and acceleration levels as independent variables, these models are capable of evaluating the environmental impacts of operational-level projects including intelligent transportation systems. The models developed in this study have been incorporated within the INTEGRATION microscopic traffic simulation model to further demonstrate their application and relevance to the transportation profession. Furthermore, these models have been utilized in conjunction with global positioning system speed measurements to evaluate the energy and environmental impacts of operational-level projects in the field.

This paper adopts different supervised learning methods from the field of machine learning to develop multiclass classifiers that identify the transportation mode, including driving a car, riding a bicycle, riding a bus, walking, and running. Methods that were considered include K-nearest neighbor, support vector machines (SVMs), and tree-based models that comprise a single decision tree, bagging, and random forest (RF) methods. For training and validating purposes, data were obtained from smartphone sensors, including accelerometer, gyroscope, and rotation vector sensors. K-fold cross-validation as well as out-of-bag error was used for model selection and validation purposes. Several features were created from which a subset was identified through the minimum redundancy maximum relevance method. Data obtained from the smartphone sensors were found to provide important information to distinguish between transportation modes. The performance of different methods was evaluated and compared. The RF and SVM methods were found to produce the best performance. Furthermore, an effort was made to develop a new additional feature that entails creating a combination of other features by adopting a simulated annealing algorithm and a random forest method.