Chapter 1 Introduction: intelligent vehicular communications

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1.2 
EVOLUTION Of TRANSpORTATION MODELS
evaluation, were widely developed. We refer to models that have these features and that emerged during 
the 1980s–2000s as second-generation models. Most early ITS models lie within this generation, even 
though the scope of ITS has been greatly extended by more advanced technologies and models. The 
third and current wave has been primarily driven by rapidly growing wireless communication technolo-
gies in the new century. Reliable connectivity between all elements (human, vehicle, and infrastructure) 
in transportation systems can now be achieved. Such connectivity facilitates not only the real-time data 
collection of transportation systems but also the active coordination of vehicles in real-time. Models in 
this period have the characteristics of real-time capability, active control, and integration among differ-
ent data sources and different applications.
However, these models still assume that the natural characteristics of flow in the transportation 
system, such as human driving, local perception, and so on, will remain largely unchanged. With the 
future development of communication technologies along with smart vehicle technologies in the au-
tomobile industry, fully automated and controlled transportation systems may become possible. This 
advancement may start the next wave in transportation model development, since traffic flow can be 
changed fundamentally to automated, proactive, well-informed, and fully controlled flow, which may 
be triggered by several new technologies that are under development, such as cloud computing, Internet 
of Things, and distributed computing. Fourth-generation models in this wave may be highly integrated, 
highly reliable, distributed, and system optimized, based on the above new characteristics of traffic 
flow. There are several key differences between the third- and fourth-generation models. First of all, the 
third-generation models deal with the increased automation in driving and traveling with the develop-
ment of the connected vehicle technologies, while the fourth-generation models study the potential of 
fully automated traveling in the future. The difficulty in the third-generation models is to describe the 
impact of the increased connectivity and control within mixed noninformed, informed, and connected 
driving and traveling, while the difficulty in the fourth-generation model is to explore system-wide 
and customized solutions to stochastic travel demand by data mining over the massive amount of data. 
The latter one may sound trivial but is, in fact, a very complicated system optimization problem in the 
evolution of transportation models.