AbstractUrban growth and the resulting highway congestion is driving up demand for rail transit. Rail, a significant component of transportation infrastructure, is critical to economic efficiency and is one of the least energy-intensive modes. However, the scale of operations results in high energy consumption, atmospheric pollution, and operating costs. Fortunately, some of the braking energy can be harvested and either used to power a simultaneously accelerating train or stored to power subsequent accelerations. The objective of this research was to optimize the number of locations of the energy storage devices and speed profiles. First, kinematic equations were applied to simulate energy consumption. Then, a genetic algorithm (GA) was developed to optimize the speed profiles that minimize the energy consumption with and without a wayside energy storage unit (WESS) for a rail transit line. Finally, a model was developed to optimize the WESS locations that maximized the net benefit. A case study was conducted to examine the model in a real-world setting and to demonstrate its effectiveness. The results indicate that about 980 MWh of electrical energy, or an additional 5%, could be saved by optimizing the WESS locations over only applying speed profile optimization. In addition to significant energy savings, environmental emissions could be mitigated using these methods.Practical ApplicationsExcessive highway congestion and the resulting atmospheric pollution is resulting in increased demand for rail travel. Expanding service to meet these demands would result in higher overall energy consumption and increased costs. However, electric trains possess the ability to recover some of the energy dissipated as heat during the braking cycle. This recovered energy could be used to power subsequent acceleration cycles, and therefore reduce operating expenses. Due to the unevenness of rail alignment, the energy consumed and braking energy available for recovery varies on different alignment sections of equal lengths. Therefore, optimization is the key to minimizing fuel consumption and maximizing the benefit to the operator. Starting with algebraic energy equations based on Newton’s laws of motion, a model was developed to maximize the net benefit of the operator. The model contained an algorithm that dictated the position of the throttle and the locations of the energy storage devices for optimal operation. Consequently, a case study was conducted to examine the model and to demonstrate its effectiveness in a practical setting. The results indicate that, by optimizing the placements of the storage devices, approximately 5% more energy savings can be achieved than by only optimizing the throttle position.