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Traffic congestion increases CO2 emissions, fuel consumption and travel times. These increases are related to the evolution of the growth in urban areas. Urban areas are being transforming into smart cities, that is, they use massive Infrastructure and Communication Technologies (ICT) to improve all the services with which citizens are provided. One objective of smart cities is to reduce traffic congestion. One approach to reaching this complex objective is by using Intelligent Transport Systems (ITS) to reroute vehicles in cities or in their periurban areas. Although, rerouting is a complex task, ICT have evolved a lot and they are currently being applied to the development of more powerful ITS. New ITS cooperative systems are commonly denoted as combinations of X2X (with X = I and/or X = V). In this paper, a new smart system based on I2V communications for peri-urban areas is described and implemented with the overall goal of improving rerouting, which implies reducing congestion. A demonstrator has been developed to evaluate the architecture and the impact that the use of public and up-to-date traffic information can have on rerouting. A real model of the peri-urban area of Castellón de la Plana has been used to evaluate the architecture. Using this model, several tests have been performed. Results show that traffic status information is valuable, but its impact on rerouting depends on the number of drivers that use this information appropriately. [ABSTRACT FROM PUBLISHER]

Weather is an important factor that affects traffic flow and road safety. Adverse weather situations affect the driving conditions directly; hence, drivers must be informed about the weather conditions downstream to adapt their driving. In the framework of intelligent transport systems, several systems have been developed to know the weather situations and inform drivers. However, these systems do not forecast weather in advance, and they need the support of road operators to inform drivers. This paper presents a new autonomous system to forecast weather conditions in a short time and to give users the information obtained. The system uses a set of algorithms and rules to determine the weather and to forecast dangerous situations on the road network. It has been implemented using a multiagent approach and tested with real data. Results are very promising. The system is able to forecast adverse situations with a high degree of quality. This quality makes it possible to trust in the system and to avoid the supervision of operators. [ABSTRACT FROM PUBLISHER]