Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Short-term bus travel time prediction for transfer synchronization with
  intelligent uncertainty handling   This paper presents two novel approaches for uncertainty estimation adapted
and extended for the multi-link bus travel time problem. The uncertainty is
modeled directly as part of recurrent artificial neural networks, but using two
fundamentally different approaches: one based on Deep Quantile Regression (DQR)
and the other on Bayesian Recurrent Neural Networks (BRNN). Both models predict
multiple time steps into the future, but handle the time-dependent uncertainty
estimation differently. We present a sampling technique in order to aggregate
quantile estimates for link level travel time to yield the multi-link travel
time distribution needed for a vehicle to travel from its current position to a
specific downstream stop point or transfer site.
  To motivate the relevance of uncertainty-aware models in the domain, we focus
on the connection assurance application as a case study: An expert system to
determine whether a bus driver should hold and wait for a connecting service,
or break the connection and reduce its own delay. Our results show that the
DQR-model performs overall best for the 80%, 90% and 95% prediction intervals,
both for a 15 minute time horizon into the future (t + 1), but also for the 30
and 45 minutes time horizon (t + 2 and t + 3), with a constant, but very small
underestimation of the uncertainty interval (1-4 pp.). However, we also show,
that the BRNN model still can outperform the DQR for specific cases. Lastly, we
demonstrate how a simple decision support system can take advantage of our
uncertainty-aware travel time models to prioritize the difference in travel
time uncertainty for bus holding at strategic points, thus reducing the
introduced delay for the connection assurance application.

36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph