An agent-based approach to assess drivers’ interaction with pre-trip information systems.

This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers’ responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing us to represent many of the mental aspects involved in the deliberation process

Video Processing Techniques for Traffic Information Acquisition Using Uncontrolled Video Streams

This paper reports on the first steps taken in search of a solution that uses public video streams available on the Internet to address the increasing need for monitoring transportation networks with the intent of returning added value to the community, either by allowing a better understanding of the network and its needs or by feeding applications with real-time information for various purposes, such as simulation, decision-making support and updated route guidance. After the introduction of the field, we present our findings from a survey that briefly describes several works with related studies and explain the algorithms that can be adopted to get relevant information from video streams. This is followed by an analysis of the issues that may arise and the best ways to address them. Next it reports on the results achieved so far, draws some conclusions on what has been done and suggests the next steps of our research

An approach to simulate autonomous vehicles in urban traffic scenarios

The most common cause of traffic accidents is arguably the driver error due to lack of attention. And it is very unlikely this is going to change soon thanks to increasingly cell-phone usage, in-car entertainment systems, and naturally the more frequent traffic jams in highly populated areas. Autonomous vehicles, such as driverless cars, are a promising approach to decrease traffic accidents, as well as congestions. To test this approach, simulations are a safer, more efficient, and cheaper way than live testing. This paper presents an approach to implement a simulator to test such vehicles. It includes a study of the state of the art in driverless car simulation and discusses on the specific objectives that this particular simulator aims to achieve in order to aid testing the interactions of multiple driverless cars in urban networks

Video Processing Techniques for Traffic Information Acquisition Using Uncontrolled Video Streams

This paper reports on the first steps taken in search of a solution that uses public video streams available on the Internet to address the increasing need for monitoring transportation networks with the intent of returning added value to the community, either by allowing a better understanding of the network and its needs or by feeding applications with real-time information for various purposes, such as simulation, decision-making support and updated route guidance. After the introduction of the field, we present our findings from a survey that briefly describes several works with related studies and explain the algorithms that can be adopted to get relevant information from video streams. This is followed by an analysis of the issues that may arise and the best ways to address them. Next it reports on the results achieved so far, draws some conclusions on what has been done and suggests the next steps of our research

Preliminary experiments with EVA - Serious Games virtual fire drill simulator

Fire keeps claiming a large number of victims in building fires. Although there are ways to minimize such events, fire drills are used to train the building occupants for emergency situations, organizing and implement these exercises is a complex task, and sometimes not sucessfull. Moreover, fire drills require the mobilization of some finantial resources and time, and affect the normal functioning of the site where they occur. To overcome the aforementioned issues, computer games have a set of features that might overcome this problem. They offer engagement to their players, keeping them focused, and providing training to real life situations. The game evaluate users, providing them some feedback, making possible for the players to improve their performance. The proposed methodology aims to study the viability of using a game that recreates a fire drill in a 3D environment using Serious Games. The information acquired through the players performance is then used to implement an artificial population. A sample of 20 subjects was selected to test the application. The results are promising, showing that the exercise had a positive impact on users. Moreover, the data acquired was used to demonstrate the possibility of creating an artificial population based on human