*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

the vehicle, it's possible to see that "Automated Driving System" class present a non-homogeneous control, but these criteria it's not the only one.

Another criterion is represented by the "type of ownership". Different from SAE Levels 0 to SAE Levels 2 that present a huge percentage of private owned vehicle; it's possible to notice that:


Therefore different "user definitions" [11] must be considered in the two cases. Moreover, the new models needed to be studied and developed should be differentiated, at least with respect to parameters.

For all these reasons, a new approach to vehicle meta-classification should be formulated. Assuming that:


The new meta-classification proposed is the following including three classes:


This meta-classification allows the analysis of the following future scenarios:


**209**

**Figure 2.**

*Advanced Vehicles: Challenges for Transportation Systems Engineering*

**3. Analysis of transportation systems with mixed flow**

A change so great may be not technology-driven only, but also requires a carefully analysis of its several impact through well designed enhancements of tools of Traffic and Transportation Theory (TTT) already available to the transportation systems modelers and planners (see the comprehensive book by Cascetta [11]). The analysis of transportation systems with several types of vehicles require a generalization of existing models and algorithms for travel demand assignment to transportation networks, as described in Cantarella and Di Febbraro [12], Cantarella et al. [13, 14]; the proposed approach can be applied to real size net-

Users are partitioned into o-d pairs they are traveling from/to, user categories (with common socio-economic and behavioral features) and types of used vehicle, such as traditional, connected, automated, autonomous, …; fossil fuel vs. electrical powered; privately owned vs. shared; …. Demand flows are assumed constant and known. Transportation supply is modeled through a flow network, say a graph with a transportation cost and a flow associated to each arc. All costs are assumed measured by a common unit, usually travel time or money, through duly homogenization of different attributes, if the case. A route connecting an Origin Destination pairs is described by a path. (Presented results still hold if more general definitions

In uncongested networks the arc flows depend on the arc costs, through the arc-

flow function obtained as described below; its structure is shown in **Figure 2**. The arc costs (**c**) may be different among the vehicle types to reflect different performances, and we assume that the arc cost per vehicle type are given by an

*DOI: http://dx.doi.org/10.5772/intechopen.94105*

works. It is briefly reviewed in the following.

of routes are used, such as hyperpaths).

**3.1 Assignment to uncongested networks**

affine transformation of the arc generic costs.

*The arc-flow function for assignment to uncongested networks.*

*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

non-homogeneous control, but these criteria it's not the only one.

privately owned such as the so-called robotaxi).

possible to notice that:

be mostly privately owned,

ated, at least with respect to parameters.

formulated. Assuming that:

vehicle and level,

vehicle classes only,

class vehicles,

vehicles,

the vehicle, it's possible to see that "Automated Driving System" class present a

Another criterion is represented by the "type of ownership". Different from SAE Levels 0 to SAE Levels 2 that present a huge percentage of private owned vehicle; it's

• SAE Level 3 and SAE Level 4 can be considered a technological evolution of traditional vehicles aiming at reducing effort of human drivers and will likely

• SAE Level 5 vehicles should be considered an evolution of taxi, other vehicles available on demand and public transport system and will likely be mostly not

Therefore different "user definitions" [11] must be considered in the two cases. Moreover, the new models needed to be studied and developed should be differenti-

For all these reasons, a new approach to vehicle meta-classification should be

• the sensors related software and involved algorithms are the same for each

• the SAE Level Certification remains as fundamental definition for the type of

• the SAE Level can be used for an "ownership" definition by the user (Starting for SAE Level 0 to be private to arrive at SAE Level 5 as Shared Vehicle)

The new meta-classification proposed is the following including three classes:

• Human Driver: the same definition as the previous 2-class meta-classification,

• Advanced Driving System: this class includes the SAE Level 3 and SAE Level 4 vehicles; these vehicles can be modeled as the same type of vehicle for the "user definition", since a human driver is needed to control the vehicle and the

This meta-classification allows the analysis of the following future scenarios:

• short-term scenarios with both Human Driver and Advanced Driving System

• medium-term scenarios as above with low percentage of Automated Driver

• long-term scenarios with Advanced Driving Systems and Automated Driver

• Automated Driver: this class includes the SAE Level 5 vehicles only.

• the sensors have their functional range as described in **Table 2**,

• the sensors functional range as line of vision for the user,

considered sensors have the same application range,

classes only and no Human Driver class.

**208**
