A Car-Following Model Using Online Sequential Extreme Learning Machine

Abstract

To prevent the sedimentation and loss of real-time data reflecting the car-following behavior and use it for modeling services, a car-following model using online sequential extreme learning machine is proposed. This model based on car-following behavior is affected by the driver’s memory effect, and a forgetting factor is introduced according to the timeliness of incremental data in different traffic conditions. It is trained, validated, and tested by NGSIM project data. Compared with other data-driven car-following models, the model has higher predictive accuracy and better real-time performance. With the help of experimental simulation, it verifies that the average time of the model’s online update is about 0.004s, which can fully meet the real-time requirements of the online traffic simulation system. The research results show that the car-following model proposed in this article is reasonable and feasible.