Floating car data

About: Floating car data is a research topic. Over the lifetime, 7541 publications have been published within this topic receiving 110971 citations.

MicroTE: fine grained traffic engineering for data centers

Abstract: The effects of data center traffic characteristics on data center traffic engineering is not well understood. In particular, it is unclear how existing traffic engineering techniques perform under various traffic patterns, namely how do the computed routes differ from the optimal routes. Our study reveals that existing traffic engineering techniques perform 15% to 20% worse than the optimal solution. We find that these techniques suffer mainly due to their inability to utilize global knowledge about flow characteristics and make coordinated decision for scheduling flows.To this end, we have developed MicroTE, a system that adapts to traffic variations by leveraging the short term and partial predictability of the traffic matrix. We implement MicroTE within the OpenFlow framework and with minor modification to the end hosts. In our evaluations, we show that our system performs close to the optimal solution and imposes minimal overhead on the network making it appropriate for current and future data centers.

SUMO (Simulation of Urban MObility) - an open-source traffic simulation

Abstract: As no exact model of traffic flow exists due to its high complexity and chaotic organisation, researchers mainly try to predict traffic using simulations. Within this field, many simulation packages exist and differ in their software architecture paradigm as well as in the models that describe traffic itself. We will introduce yet another system which, in contrast to most of the other simulation software packages, is available as on open-source programm and may therfore be extended in order to fit a researcher´s own needs and also be used as a reference testbed for new traffic models.

Real-time road traffic prediction with spatio-temporal correlations

Abstract: Real-time road traffic prediction is a fundamental capability needed to make use of advanced, smart transportation technologies. Both from the point of view of network operators as well as from the point of view of travelers wishing real-time route guidance, accurate short-term traffic prediction is a necessary first step. While techniques for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. We present a method that has proven to be able to meet this challenge. The method presented provides predictions of speed and volume over 5-min intervals for up to 1 h in advance.

An evaluation tool for advanced traffic information and management systems in urban networks

Abstract: Tools for evaluating traffic networks under information supply are a crucial necessity in view of the ATMS/ATIS systems being proposed and implemented around the world as part of Intelligent Vehicle-Highway Systems of the future. This paper presents an evaluation model that incorporates the driver response to information, the traffic flow behavior, and the resulting changes in the characteristics of network paths, into an integrated simulation framework. The model is based on simulating individual vehicle movements according to macroscopic flow principles, the driver path selection behavior under information being explicitly modelled. Detailed modelling of intersection delays as well as a variety of traffic control options for both freeways and arterials are performed. The path-processing component is designed for efficient application of the framework to large and realistic networks. The model can be effectively used for studying alternative information supply and traffic control strategies under various levels of market penetration of in-vehicle ATIS hardware. The paper also discusses its application to candidate networks.