Reference:
A. Jamshidnejad and
B. De Schutter,
"A combined probabilistic-fuzzy approach for dynamic modeling of
traffic in smart cities: Handling imprecise and uncertain traffic
data," Computers and Electrical Engineering, vol. 119-A, p.
109552, 2024.
Abstract:
Humans and autonomous vehicles will jointly use the roads in smart
cities. Therefore, it is a requirement for autonomous vehicles to
properly handle the information and uncertainties that are introduced
by humans (e.g., drivers, pedestrians, traffic managers) into the
traffic, to accordingly make proper decisions. Such information is
commonly available as linguistic, fuzzy (non-quantified) terms. Thus,
we need mathematical modeling approaches that, at the same time,
handle mixed (i.e., quantified and non-quantified) data. For this, we
introduce novel type-2 sets and membership functions to translate such
mixed traffic data into mathematical concepts that handle different
levels and types of uncertainties and that can undergo mathematical
operations. Next, we propose rule-based data processing and modeling
approaches to exploit the advantages of these sets. This is inspired
by the rule-based reasoning of humans, which has proven to be very
effective and efficient in various applications, especially in
traffic. The resulting models, hence, handle more than one level and
type of uncertainty, which results in precise estimations of traffic
dynamics that are comparable in accuracy with similar analyses if only
one level of uncertainty (either probabilistic or fuzzy) would exist
in the dataset. This will significantly improve the analysis,
prediction, management, and safety of traffic in future smart cities.
Bibtex entry:
@article{JamDeS:24-018,