Key points are not available for this paper at this time.
With the latest developments in technology, the Automatic Train Operation (ATO) has been widely used in urban rail transit systems over the past decade. The control process used by the ATO system generally consists of two levels. The high-level control calculates the target speed according to the moving authority of the trains and the low-level control implements precise tracking on the target speed by controlling the traction and braking force. Most of the literature has only focused on the high-level control to optimize the train trajectory, but did not practically combine the low-level control of the ATO system. When the optimized trajectory is applied as the target speed, it will cause frequent switches between acceleration and braking for precise tracking and waste a lot of energy. Hence, this previous research may not be applied to practical ATO systems. In this paper, a numerical algorithm is proposed to solve the energy-ecient train control problem with a given trip time by distributing the reverse time to dierent segments. Then a method is presented for optimization of target speeds based on the ATO control principles, which guides the train to output optimized control sequences. The proposed approach is capable of avoiding the unnecessary switching and then eciently reduces the traction energy consumption of the train switches. Furthermore, case studies have been undertaken based on infrastructure data from the Beijing Yizhuang rail transit line, and the simulation results illustrate that the proposed approach results in good performance with regards to energy saving.
Su et al. (Thu,) studied this question.