As a typical application of intelligent transportation in urban rail transit

smart urban rail transit (SURT) enhances system efficiency through data-driven transformation with its technical foundation rooted in the synchronous perception of train position and electrical parameters. Traditional train information perception methods

highly dependent on direct measurements from on-board sensors

face challenges such as high hardware costs

difficulty in retrofitting existing trains

and poor synchronization in communicating independent position and electrical parameter data; thus

this paper proposes a data-physics model fusion-driven online perception method for urban rail train information to address these issues. The method constructs a cascaded random forest-neural network model under a collaborative data-driven and model-driven framework: first

a random forest classifier is leveraged to identify train operation organization in real time using the measurement features of traction substation(e.g.

hourly traffic rate)

and then a neural network model maps the electrical parameters and position of the lowest-voltage train from traction substation data (e.g.

voltage

power). Further integrating the spatiotemporal rigid constraints of train operation diagrams

the study designs a confidence interval projection method to correct positioning results by fusing the joint confidence interval of voltage-position with the operation diagram time-axis

enabling online derivation of global train operation states. Finally

the applicability of the proposed method in long-distance power supply sections is validated through a single traction substation fault scenario from a domestic metro line. Simulation results show that under peak-hour operation density (20 train pairs per hour)

the real-time global train state perception in the power supply section is effective and accurate

providing a low-cost perception solution for real-time energy management of traction power supply systems compared with traditional direct sensor measurement.