Incentive mechanism for collective coordination in an urban intelligent transportation system using G-networks

Although the abilities of human beings as participants in urban traffic, when they take decisions and interact with the transportation infrastructure and other vehicles, have been greatly amplified by powerful portable devices and efficient human-machine interfaces, the intelligence of vehicle drivers and pedestrians and their possible pro-social behaviour such as helpfulness and sense of duty, have been excluded in previous studies of Intelligent Transportation Systems (ITS). Thus the robustness of an ITS has not been evaluated as a function of the likelihood that participants follow instructions. Moreover, much effort has been dedicated to the use of Artificial Intelligence, while in fact many tasks can be easily accomplished by road users in the system who use ordinary human intelligence. Hence, in this paper, we propose a reward mechanism to integrate the intelligence of human road users into a large-scale transportation system to improve the effectiveness and robustness of the system by introducing a transportation-related task publishing system which is assisted by a queueing network model. The experimental results show that the use of a reward mechanism can significantly improve the performance of the transportation system in terms of average travel time of vehicles and the average response time to various tasks.