Multi Experiment Teaching Design of “Big Data and Intelligent Transportation” for Transportation General Course under the Background of Double First-class New Engineering

In response to a series of national strategic calls, such as innovation-driven development and “Strong Transportation Country”, and aiming to achieve the goal of reform in new engineering and double first-class teaching, there is a great need in the transportation field to cultivate a group of high-quality composite new technical talents with high practical ability, innovation, and international competitiveness. Therefore, the traditional course of the transportation specialty needs to be adjusted urgently. Taking the transportation general course “Big Data and Intelligent Transportation” as an example, aiming at improving the practical ability, innovation ability, computer ability and other abilities of students majoring in transportation, this paper puts forward new requirements in the course content to lay a foundation for training high-quality composite new engineering talents to meet the needs of the transportation industry. The traditional transportation engineering is combined with the big data technology to carry out the multiple experimental teaching. Each group of experiments adopts actual cases, and uses multi-source data, including space-time data of train travel time estimation, two-dimensional image data of track fastener state classification, and one-dimensional audio data of subway noise, so as to effectively improve students’ innovation and practice ability.