Abstract

Effective ITS and traffic management purposes requires a complete and accurate information about current and predicted traffic states in the transport network. The current state-of-the-art in literature regarding traffic state estimation and prediction yields efforts which mostly focus on highways, which are not bluntly transferrable to an urban environment and do not maximize the utilization of all available traffic data."br" This paper describes the development and assessment of a data-driven traffic state estimation and prediction framework for application in an urban environment. It uses the intuitive relationship between past, current and future traffic states on neighboring links to train and improve estimation/prediction accuracy and fill the gaps on those links where no floating car data are available. Additionally, this framework is tested on the well-known Sioux Falls Scenario. When penetration rate of floating cars is 5%, on average 50% of the urban links are estimated within 5 km/h accuracy. For a prediction horizon of 5 minutes, it performs almost equal with a percentage of 49%.

Abstract

Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller ("i"TLC"/i") network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of "i"TLC"/i" network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks. Authors wish to thank the support given by the project IoSENSE: Flexible FE/BE Sensor Pilot Line for the Internet of Everything, funded by the Electronic Component Systems for European Leadership Joint (ECSEL) Undertaking under grant agreement No 692480.This work has been also supported by MINECO (Spain) through the project RTC-2015-3942-4 TCAP: Auto. Moreover, Raúl M. del Toro acknowledges the financial support received from MINECO through grant “Juan de la Cierva-incorporación”, code IJCI-2014-20169. We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI). Document type: Article