176 PART | V The future of ITS applications
Tran & Haasis, 2015) and the optimization of vessels’ routes. In the first
case, the focus is given to the management of congestions in ports (Arguedas
et al., 2018) and the overall handling of the maritime network dynamics under
varying conditions (Fang et al., 2018). In the second case the combination of
real-time information about vessel positions from the AIS system with weather
predictions, wave height, and current estimations, led to the design of algorithms
that search for the optimum transportation paths for vessels (mainly for cargo
vessels), paths that minimize travel time or cost. Weather routing (Christiansen
et al., 2013) and voyage optimization (Lu et al., 2015) are two optimization
problems with similar solutions but slightly different objectives (safety-based
vs. cost-based optimization, respectively). Finally, the “almost real-time” visu-
alization of vessels that sail around the globe is an application that gained the
attention of researchers and the industry, that is based on a global network of
AIS signal receivers that combine land and satellite stations.
Robustness is one of the main concerns for air transportation networks too.
Research in the field studies the structure and dynamics of networks and devel-
ops algorithmic solutions that reduce delays, avoid flight cancellations, and
optimize the transportation of passengers and goods. The identification of criti-
cal nodes (i.e., airports) (Sun et al., 2017), the maximization of edge capacity
(i.e., flow between airports) (Yang et al., 2018), and the response to the tem-
poral dynamics of traffic (Sun et al., 2015) are the main issues of optimization
algorithms.
All the aforementioned transportation networks have several differences
but also share many features in common. However, they share a common core
architecture which comprises a set of sensing devices embedded in the moving
objects and spread close to the network main routes, a processing backbone that
comprises applications, models, and processing infrastructures and a commu-
nication network for exchanging useful information between the network and
the moving objects.
The three layers of intelligent transportation systems are depicted in Fig. 16.1.
16.2 Network management systems
The applications of ITS when it comes to traffic management systems are many
and can be broadly divided into two major groups depending on the end-user
they are targeting—(1) applications that support drivers in making informed
decisions and (2) network operating systems that support operators to monitor
and optimize the transportation network performance.
16.2.1 Driving-assistance systems
Advanced driver-assistance systems (ADAS), are designed to support humans
during driving, with their primary aim being car safety and consequently, the
overall safety of the network. Additional objectives can be the optimization of