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[Objective] The rapid development of the intelligent transportation industry brings new opportunities and challenges for talent cultivation. The traditional cultivation system and teaching platforms for transportation majors face the following problems: 1) The cultivation modes of talents are not suitable for the rapid development of the industry. 2) The teaching methods are not coordinated with the development of industrial technologies. 3) The construction of a practical teaching platform is not synchronized with industrial development. 4) There is no sustainable and effective cooperation mechanism in the integration of industry and education. To solve the above problems, a teaching reform method based on the digital twin is proposed in this paper. [Methods] First, this paper analyzed the demand for transportation innovation talents and the significance of technological innovation capabilities, interdisciplinary comprehensive capabilities, and industry foresight capabilities. Meanwhile, a double-closed-loop innovative talent cultivation system of the integration of industry and education was established. One closed loop was theoretical teaching and professional skills practice, and the other closed loop was industrial demand and school supply. Second, a transportation digital twin method was proposed based on the five-dimensional model of the digital twin. The five dimensions of physical space, cyberspace, service system, digital twin data, and connection were deeply integrated with traffic scenarios. Based on the transportation digital twin method, this paper combined hardware (e.g., intelligent roadside devices, driving simulators, and miniature intelligent vehicles) with software (e.g., PreScan, Matlab, and PyCharm) to build the transportation digital twin experimental platform. The platform comprised three parts: the real traffic road, the cyberspace, and the semiphysical traffic simulation scene. Using this platform, real field tests can be replaced by digital twin experiments in the laboratory. Third, a typical experimental example of autonomous driving was selected to demonstrate the operating steps, including data collection and preprocessing, data conversion and traffic scenario construction, and autonomous driving algorithm development. Finally, six indexes were selected to evaluate the transportation digital twin teaching reform method, including learning attitude, cognition of majors, practical participation, course satisfaction, academic performance, and science and technology competition. [Results] The final evaluation results showed the following: 1) The proposed method can meet the learning needs of students, allow students to have a deep understanding of the practical applications of transportation professional courses, and improve their learning attitudes and cognition of majors. 2) Cyber space can provide students with repeated practice opportunities, effectively stimulating their enthusiasm for innovation and creativity, thereby improving their practical participation and course satisfaction. 3) There is a positive effect in promoting students' class performance, experimental results, and exam results, among others. This plays an important role in science and technology competitions. [Conclusions] The proposed teaching reform method based on the digital twin solved the problems existing in traditional transportation talent training systems and teaching platforms, which is of great significance for the cultivation of innovative talents of transportation majors. [ABSTRACT FROM AUTHOR]

It is agreed that connected vehicle technologies have broad implications to traffic management systems. In order to alleviate urban congestion and improve road capacity, this paper proposes a multilane spatiotemporal trajectory optimization method (MSTTOM) to reach full potential of connected vehicles by considering vehicular safety, traffic capacity, fuel efficiency, and driver comfort. In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin's maximum principle and reinforcement learning (RL). A typical scenario of intersection with a one-way 4-lane section is measured, and the data within 24 hours are collected for tests. The results demonstrate that the proposed method can optimize the traffic flow by enhancing vehicle fuel efficiency by 32% and reducing pollutants emissions by 17% compared with the advanced glidepath prototype application (GPPA) scheme., Author(s): Pangwei Wang [1]; Yunfeng Wang [1]; Hui Deng [1]; Mingfang Zhang [1]; Juan Zhang (corresponding author) [2] 1. Introduction The traffic capacity of urban road network is restricted by [...]

The emerging advanced intelligent transportation systems, such as connected vehicles (CV) and cooperative vehicle-to-infrastructure system (CVIS), attract much research attentions nowadays. Taking the advantages of such systems, first, this article develops a joint control model which optimizing the speeds of the connected vehicles and coordinating signals along an arterial simultaneously. This control model forms connected vehicles into platoons so that the vehicles can pass through intersections together with no stops or the least stop time. At the same time, it optimizes signal timing plans along an arterial to achieve lower signal delay and higher throughput. Finally, the proposed model is tested on a mimic of a real-world road network in simulation. Compared with the MAXBAND model, the results indicate that the joint control model can reduce the stop time and stops of coordinate phase by up to 53.69% and 41.15%. The signalized intersection delay per vehicle is reduced by 13.19%. Meanwhile, the vehicle queue is eliminated by the platoon control and arterial signal coordination. The sensitivity analysis also shows that the signalized intersection delay reduction is proportional to the traffic flow volume. [ABSTRACT FROM AUTHOR]