ABSTRACT Rising energy prices and decarbonisation targets have increased interest in energy optimisation for urban rail systems, but earlier reviews focus on a single subsystem or method family and do not compare artificial‐intelligence‐based (AI) and non‐AI approaches in a consistent way. This paper presents a systematic literature review of 226 studies on energy optimisation in urban rail systems published between 2011 and 2025. Using the PRISMA protocol, we group the literature into five optimisation families: speed profile / energy‐efficient driving (SPF), timetable (TMB), regenerative energy braking (REB), energy management with storage (EMG) and traction power network (POW). Each family is distinguished between AI‐based and non‐AI approaches. Overall, 166 studies use non‐AI methods and 60 use AI‐based techniques. For single‐module problems, the median normalised energy‐saving indices are higher for non‐AI methods in SPF, TMB and EMG (18.7%, 18.51% and 15.22% versus 11.2%, 6.6% and 2.5% for AI). In contrast, for integrated categories that combine regenerative braking and storage, AI‐based methods achieve higher median savings, with REB + EMG showing 30.1% for AI versus 16.7% for non‐AI. However, integrated‐family medians are based on small samples and reported savings depend on heterogeneous baseline definitions. Therefore, the quantitative comparisons should be interpreted as preliminary, indicative evidence rather than definitive rankings.
Wicaksana et al. (Thu,) studied this question.