Road data enrichment framework based on heterogeneous data fusion for ITS.
No Thumbnail Available
Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
In this work, we propose the Road Data Enrichment
(RoDE), a framework that fuses data from heterogeneous data
sources to enhance Intelligent Transportation System (ITS)
services, such as vehicle routing and traffic event detection.
We describe RoDE through two services: (i) Route service,
and (ii) Event service. For the first service, we present the
Twitter MAPS (T-MAPS), a low-cost spatiotemporal model to
improve the description of traffic conditions through Location-
Based Social Media (LBSM) data. As a case study, we explain how
T-MAPS is able to enhance routing and trajectory descriptions by
using tweets. Our experiments compare T-MAPS’ routes against
Google Maps’ routes, showing up to 62% of route similarity, even
though T-MAPS uses fewer and coarse-grained data. We then
propose three applications, Route Sentiment (RS), Route Infor-
mation (RI), and Area Tags (AT), to enrich T-MAPS’ suggested
routes. For the second service, we present the Twitter Incident
(T-Incident), a low-cost learning-based road incident detection
and enrichment approach built using heterogeneous data fusion.
Our approach uses a learning-based model to identify patterns
on social media data which is then used to describe a class of
events, aiming to detect different types of events. Our model to
detect events achieved scores above 90%, thus allowing incident
detection and description as a RoDE application. As a result,
the enriched event description allows ITS to better understand
the LBSM user’s viewpoint about traffic events (e.g., jams) and
points of interest (e.g., restaurants, theaters, stadiums).
Description
Keywords
Incident detection
Citation
RETTORE, P. H. L. et al. Road data enrichment framework based on heterogeneous data fusion for ITS. IEEE Transactions on Intelligent Transportation Systems, v. 1, n. 4, p. 1751-1766, 2020. Disponível em: <https://ieeexplore.ieee.org/document/9040415>. Acesso em: 29 abr. 2022.