# Mobility, Citizens, Innovation and Technology in Digital and Smart Cities
## 3 Cities Transportation Trends
### Decentralization via Multi Agent Systems  
Multi Agent Systems (MAS) is a network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each agent, with either diverging information or diverging interests. MAS are quickly becoming reality given to the recent possibility of acquiring data from the various IoT based devices available in the market. In this sense, autonomous IoT agents facilitate common use of local controls, real-time tuning of control parameters, peer-to-peer negotiation protocols, automatic arming and disarming of control actions in real time, reacting and acting based on a variety of big-data. All that information is measured, processed, optionally shared, mined, forecasted, and used for guiding decision-making [139; 140]. A conceptual model via MAS and IoT is usually applied through the following principles: inseparability, virtualization, decentralization, real-time capability, service orientation and modality [141; 142]. Progress in mobile and ubiquitous computing is promoting work sharing between a large number of agents in an orchestrated and self-organized manner [143; 144]. Regulators can act autonomously in this environment, interacting with any device in the system. For this purpose, generalist platforms should communicate in a standardized and trustful manner, providing certificates and proof-checking. All of the trust can surely be handled by using blockchain concepts (as emphasized in Section 3.5). A generic conceptual model for smart cities was illustrated in [95], highlighting how MAS and ICT can result in new generation businesses and interactions. [PERSON] [145] proposes to delegate ambient devices through various machine-to-machine (M2M) communications. [PERSON] et al. [146] developed a hierarchical arrangement of mobile agents able to personalize users' needs. As should be noticed, with proper modifications, any task can be dealt in distributed manner [145], providing benefits such as improved scalability and trust. An initial attempt to develop a car parking locator service, within a university campus, was done by [PERSON] et al. [87], considering an info station based on MAS. In a subsequent work [94], an MAS flexible negotiation mechanism was developed, taking into account car owner parking vendors' preferences and social city benefits. In recent studies, developing adaptive traffic signal control [147] was done by reinforcement learning agents. Each intersection can be controlled by a single independent agent which determines the light switching sequence. [PERSON] et al. [148]developed the Tokyo virtual living laboratory traffic simulator based on autonomous vehicle agents' interaction. Understanding road user interactions, using game theory concepts, was discussed for a scenario in Norway [149], composed of vehicles and pedestrians. Furthermore, an MAS based traffic simulation system [150] can assist autonomous vehicles in connection with VRP on dynamic scenarios fulfilled with uncertainties, in which high-performance techniques are also embedded for assisting the decision-making process. Machine learning algorithms are being extensively applied for traffic control [151] and using MAS is inevitable for reaching such complex agreements [152]. Reference [153] modeled a game theory application discussing car pooling profits and incentive expenses. The role autonomous devices with GPS capabilities can be used for achieving a more efficient and safe urban traffic [154, 155, 156, 157]. An adaptive traffic signal control done with reinforcement learning agents was proposed in [147], in which each intersection is controlled by a single independent agent which determines the light switching sequence. It is noteworthy that each of these autonomous devices can solve different logistics problems for assisting their decision-making, in which the core of the problem solver can be in the cloud, on device, in a central or in a distributed manner.