###### Abstract  
This study presents a working concept of a model architecture allowing to leverage the state of an entire transport network to make estimated arrival time (ETA) and next-step location predictions. To this end, a combination of an attention mechanism with a dynamically changing recurrent neural network (RNN)-based encoder library is used. To achieve this, an attention mechanism was employed that incorporates the states of other vehicles in the network by encoding their positions using gated recurrent units (GRUs) of the individual bus line to encode their current state. By muting specific parts of the imputed information, their impact on prediction accuracy can be estimated on a subset of the available data. The results of the experimental investigation show that the full model with access to all the network data performed better in some scenarios. However, a model limited to vehicles of the same line ahead of the target was the best performing model, suggesting that the incorporation of additional data can have a negative impact on the prediction accuracy if they do not add any useful information. This could be caused by poor data quality but also by a lack of interaction between the included lines and the target line. The technical aspects of this study are challenging and resulted in a very inefficient training procedure. We highlight several areas where improvements to our presented method are required to make it a viable alternative to current methods. The findings in this study should be considered as a possible and promising avenue for further research into this novel architecture. As  
trips [3]. In sum, these travels correspond to an estimated 27.4 billion kilometres travelled and saved approximately 96 million tonnes of CO\({}_{2}\)[4]. In a recent study, the social costs of owning a privately owned SUV were estimated to be close to EUR 1 million if the costs associated with pollution, infrastructure maintenance, and climate are taken into account. This study highlights that the ownership of private vehicles is associated with substantial costs to society and should therefore be reduced [5]. This highlights the importance of making bus networks as attractive as possible to attract travellers who are currently using private cars. If this is achieved, not only will it have a positive environmental impact but will also alleviate congestion issues in urban areas. Additionally, the pandemic has had an impact on the usage of public transport, and operators must restore public confidence in the safety of this mode of transport. Alongside these efforts, reliable ETA predictions will make a difference in the perceived passenger convenience of public transport [6]. We previously noted that the latency of data transmission from buses is caused by the delay in the wireless network infrastructure and the fact that the data in our operational area passes through a number of third-party systems [7]. Consequently, the RTPI system may suggest that the vehicle is further away from a bus stop than it is in reality. The literature contains a wide range of approaches to predict bus ETAs. These range from more conventional methods such as historic averages [8; 9], ARIMA [10] or Kalman Filters [11; 12; 13; 14; 15; 16]. In general, such methods have low predictive power, and the introduction of Neural Networks (NN) drastically improved the performance of ETA predictions [14; 15; 16]. In the more recent literature, NN-based approaches have taken centre stage with some impressive results [17], however, further improvements compared to NNs were achieved using hierarchical NNs [18]. A particular focus can be found on RNN structures due to the sequential nature of ETA prediction problems. These methods include Long Short Term Memory (LSTM) networks [19], bidirectional LSTMs [20] or even convolutional LSTMs [21]. However, much more complex methods have become more common and tend to use different types of models for different aspects of the prediction task [22; 23; 24].