Big data analytics for intelligent transportation systems Chapter | 19 209Remote sensing techniques become very popular because of the rapid
development of automatic positioning systems such as the Global Navigation
Satellite Systems, such as GPS, Galileo, etc., that collect data from around the
globe and smart mobile phones that deliver the processed information to any
individual. The main principle of remote sensing techniques is that vehicles can
be spotted at any time either by means of positioning devices or by Bluetooth on
mobile phones and/or vehicles. The tracking of vehicles, which are considered
as “floating vehicles” in the road network, results in vehicle path data (trajec-
tory data) that depict the vehicle’s positions over time. The trajectory data from
many vehicles are then aggregated and used to estimate traffic density, vehicle
speed, etc. (Seo, Bayen, Kusakabe & Asakura 2017). When a single vehicle is
equipped with second-generation onboard diagnostics such as radars, lidars, or
stereo cameras, it is possible to act as sensor for its nearby vehicles and collect
information for their speed or number, and it is considered an extended floating
Car (Barceló, Montero, Marqués, & Carmona, 2010). If some of the vehicles
are connected to vehicular ad hoc networks (VANET) they can collect data from
their network and report the summary data to the aggregation layer at a higher
level (Darwish & Bakar, 2015) using interconnected base stations and access
points.
19.2.2 Within smartcities
The growing need for a holistic transportation system within the smart city con-
text requires the collective analysis of data from every link of the urban trans-
port chain, including pedestrians, personal (cars, bicycles, and motors), and
mass means of transportation.
FIGURE 19.2 Vehicle counting using a video camera. (Based on the following source: https://
http://www.youtube.com/watch?v=B5l_3T-ThlA))