Digital Twin is an emerging technology that replicates real-world entities into a digital space. It has attracted increasing attention in the transportation field and many researchers are exploring its future applications in the development of Intelligent Transportation System (ITS) technologies. Connected vehicles (CVs) and pedestrians are among the major traffic participants in ITS. However, the usage of Digital Twin in research involving both CV and pedestrian remains largely unexplored. In this study, a Digital Twin framework for CV and pedestrian in-the-loop simulation is proposed. The proposed framework consists of the physical world, the digital world, and data transmission in between. The features for the entities (CV and pedestrian) that need digital twining are divided into external state and internal state, and the attributes in each state are described. We also demonstrate a sample architecture under the proposed Digital Twin framework, which is based on Carla-Sumo Co-simulation and Cave automatic virtual environment (CAVE). A case study that investigates Vehicle-Pedestrian (V2P) warning system is conducted to validate the effectiveness of the presented architecture. The proposed framework is expected to provide guidance to the future Digital Twin research, and the architecture we build can serve as the testbed for further research and development of ITS applications on CV and pedestrians.

Identifying vehicle maneuvers in the context of Connected Vehicles (CV) system brings huge potentials to enhance traffic safety. However, this process requires various advanced sensors, which are either available for luxury vehicles or expensive to install. Differently, smartphone is a more feasible choice with high penetration rate and various built-in sensors. Among the existing studies of applying the smartphone to detect vehicle maneuvers, most treated the detection as a classification problem without considering the real-world application. For example, the smartphone was fixed. Too many descriptive features were generated from the sensor data. To alleviate these problems, this paper developed a vehicle maneuvers detection system using a common smartphone with GPS, gyroscope, accelerometer, and magnetometer sensors. We first released the constraints on the smartphone's position through a coordination system reorientation method. Then, simply filtered sensor data were directly used. A stacked-LSTM model was built to detect the vehicle maneuvers considering the time-dependency of the sensor data. This paper compared the performance of the proposed system with previous studies and various machine learning methods, including LightGBM, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest. Extensive experimental results indicated that the proposed system accurately detected different vehicle maneuvers with an average F1-score of 0.98, precision of 0.97, and recall of 0.98, which outperformed the counterparts. Moreover, the model can be easily transferred to different drivers and locations. The system is robust and suitable for the real-time application as it requires simple processing of smartphone sensor data.