A multi-agent traffic simulation framework for evaluating the impact of traffic lights

This is an electronic version of the paper presented at the 3rd International Conference on Agents and Artificial Intelligence, held in Rome on 2011The growing of the number of vehicles cause serious strains on road infrastructures. Traffic jams inevitably occur, wasting time and money for both cities and their drivers. To mitigate this problem, traffic simulation tools based on multiagent techniques can be used to quickly prototype potentially problematic scenarios to better understand their inherent causes. This work centers around the effects of traffic light configuration on the flow of vehicles in a road network. To do so, a Multi-Agent Traffic Simulation Framework based on Particle Swarm Optimization techniques has been designed and implemented. Experimental results from this framework show an improvement in the average speed obtained by traffic controlled by adaptive over static traffic lights.This work has been supported by the Spanish Ministry of Science and Innovation. Grant TIN2010- 1987

A multi-agent simulation platform applied to the study of urban traffic lights

Proceedings of 6th International Conference on Software and Data Technologies, ICSOFT 2011The Multi-Agent system paradigm allows the development of complex software platforms to be used in a wide range of real-world scenarios. One of the most successful areas these technologies have been applied are in the simulation and optimization of complex systems. Traffic simulation/optimization problems are a specially suitable target for such a platform. This paper proposes a new Multi-Agent simulation platform, where agents are based on a Swarm model (lightweight agents with very low autonomy or proactivity). Using this framework, simulation designers are free to configure road networks of arbitrary complexity, by customizing road width, geometry and intersection with other roads. To simulate different traffic flow scenarios, vehicle trajectories can be defined by choosing start and end locations and providing traffic generation functions for each one trajectory defined. Finally, how many vehicles are generated at each time step can be determined by a time series function. The domain of traffic simulation has been selected to investigate the effect of traffic light configuration on the flow of vehicles in a road network. The experimental results from this platform show a strong correlation between traffic light behavior and the flow of traffic through the network that affects the congestion of the road.This work has been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2010-19872 and by Jobssy.com

A study on the impact of crowd-based voting schemes in the 'Eurovision' European contest

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in WIMS '11 Proceedings of the International Conference on Web Intelligence, Mining and Semantics, http://dx.doi.org/10.1145/1988688.1988718.The Eurovision contest has been the reference on european song contests for the past 50 years. Countries in the European Union can shows the rest of the participants their current music tendencies. This phenomena has been studied in domains like physic and social sciences to find correlations between contests and current political and socio-economy trends in EU. The inclusion of web and social technologies some years ago, have caused a disruption in the traditional voting system whereby the audience is encouraged to participate by casting votes for their favorite song. As a result, this system yields new, relevant information that may be extrapolated to social and political tendencies in Europe with a higher degree accuracy than by data collected using the previous jury-based system. This paper provides an initial data analysis in crowd behavior to assess the impact of the televote system, in the Eurovision voting dynamic, by focusing on two distinct five years periods that can successfully contrast each voting scheme. Analyzing these periods separately, we can observe results from the televoting contests and then compare to the jury to see if there is a change in voting patterns. Finally, we study the underlying community structure of the voting network using the Cluster Percolation Method and Edge Betweenness to discover stable core communities spanning a number of years in the contest. The clusters obtained using these algorithms are then used to compare how these stable communities have evolving during the considered periods.This work has been funded by the Spanish Ministry of Science and Technology under the projects ABANT (TIN2010 19872