[d] Multi-step Parallel Prediction For Train Delays Using Graph Neural Networks
| Hello everyone, I’m currently working on a project involving the use of Graph Neural Networks (GNNs) for predicting train delays. The goal is to perform multi-step parallel predictions for each train in the network. One of the main challenges is accounting for the spatial-temporal dimensionality of the problem and the interactions between the trains accross all the railway (See what the network looks like like in the picture). The dataset is made of snapshots of the state of the network (with the current trains) at a given time I’ve come up with two non mature approaches so far and would appreciate any advice, remarks, or alternatives: Approach 1: Heterogeneous Graph Model In this approach, I construct a heterogeneous graph with two types of nodes: stations and trains. This results in three types of relationships: train-station, train-train, and station-station. The prediction task is essentially edge prediction, specifically for train-station edges. For a given train, the model aims to predict its links (delays) with its n_following stations. Approach 2: Homogeneous Graph Model My second approach involves the use of a homogeneous graph where each node represents a Remarkable Point (RP) in the railway network such as a station, junction, etc. Each node has features such as the type of RP, geographic location, and a time-series feature representing the delay of trains at that RP over time. The edges in this graph represent the direct travel path between two RPs, with features such as average travel time and number of trains per day. The output of the GNN is the predicted delay at each RP for the next few time steps. In the existing litterature, either the model is for one train prediction, or the railway is a made of one line and this is not what i want. I would greatly appreciate any insights or suggestions you might have. Thanks in advance! [link] [comments] |
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