Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting vehicular travel times by modeling heterogeneous influences
  between arterial roads   Predicting travel times of vehicles in urban settings is a useful and
tangible quantity of interest in the context of intelligent transportation
systems. We address the problem of travel time prediction in arterial roads
using data sampled from probe vehicles. There is only a limited literature on
methods using data input from probe vehicles. The spatio-temporal dependencies
captured by existing data driven approaches are either too detailed or very
simplistic. We strike a balance of the existing data driven approaches to
account for varying degrees of influence a given road may experience from its
neighbors, while controlling the number of parameters to be learnt.
Specifically, we use a NoisyOR conditional probability distribution (CPD) in
conjunction with a dynamic bayesian network (DBN) to model state transitions of
various roads. We propose an efficient algorithm to learn model parameters. We
propose an algorithm for predicting travel times on trips of arbitrary
durations. Using synthetic and real world data traces we demonstrate the
superior performance of the proposed method under different traffic conditions.

19
The categories of this paper are: eess
2
The categories of this paper are: math
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int