Optimization Of Recommended Speed Profile For Train Operation Based On Ant Colony Algorithm

Under the era background of promoting sustainable development and developing efficient transportation in the global railway, it is necessary and urgency for urban rail transportation to complete energy conservation and emissions reduction. As one of the largest energy consumption industries in the national economy, reducing energy consumption is a very important problem for sustainable development of urban rail transportation. In the form of energy consumption in urban rail transit, train traction energy consumption accounts for nearly half of the total energy consumption. Therefore, reducing train traction energy consumption is one of the effective ways to reduce the operating costs of urban rail transit system.Energy-efficient train operation is one of the important ways to reduce train traction energy consumption. In urban rail transit, train speed is generated and controlled by the automatic train operation system (ATO) to arrive at next station on time. Recommended speed profile as the input of ATO is tracked through a certain control strategy precisely and the traction/braking instructions are outputted afterwards in order to ensure the train arrive at the parking spot punctually and accurately. It determines the tracked trajectory and the energy consumption of trains. Therefore, the optimization of recommended speed profile and the tracking strategy are regarded as two important means to achieve energy-efficient train operation between the successive stations.With the fast calculation ability, an optimization method of the recommended speed profile that integrates the ATO tracking strategy is proposed in this paper. Base on the approximate calculation, a discrete combination optimization model is formulated and a new MAX-MIN ant system (MMAS) is taken as the core algorithm. With the fixed speed tracking strategy, this method achieves the recommended speed profile with optimized energy consumption and a perfect running punctuality along the actual tracked trajectory. The convergence time of the algorithm is shorter by integrating the drivers’ experience, which also reduces the energy consumption of train running between stations. This method has an important guiding and promoting effect on exploring the fast and impactful energy-saving optimization algorithm.This paper gives the design and implementation of the "CBTC energy-saving recommended speed profile generation software" finally, providing an open algorithm simulation test platform. The simulation platform encapsulates the energy-saving method proposed in this paper, providing the conditions for further study. In addition, the case results based on Beijing Yizhuang Subway verify the effectiveness of the proposed method by comparing the simulation comparison, which has a good performance on energy-efficient train operation.