Traffic congestion reconstruction with Kerner's three-phase theory

About: Traffic congestion reconstruction with Kerner's three-phase theory is a research topic. Over the lifetime, 5009 publications have been published within this topic receiving 86817 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.

Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism

Abstract: Traffic congestion is one of the leading causes of lost productivity and decreased standard of living in urban settings. Recent advances in artificial intelligence suggest vehicle navigation by autonomous agents will be possible in the near future. In this paper, we propose a reservation-based system for alleviating traffic congestion, specifically at intersections, and under the assumption that the cars are controlled by agents. First, we describe a custom simulator that we have created to measure the different delays associated with conducting traffic through an intersection. Second, we specify a precise metric for evaluating the quality of traffic control at an intersection. Using this simulator and this metric, we show that our reservation-based system can perform two to three hundred times better than traffic lights. As a result, it can smoothly handle much heavier traffic conditions. We show that our system very closely approximates an overpass, which is the optimal solution for the problem with which we are dealing.

Adaptive cruise control design for active congestion avoidance

Abstract: We present an adaptive cruise control (ACC) strategy where the acceleration characteristics, that is, the driving style automatically adapts to different traffic situations. The three components of the concept are the ACC itself, implemented in the form of a car-following model, an algorithm for the automatic real-time detection of the traffic situation based on local information, and a strategy matrix to adapt the driving characteristics (that is, the parameters of the ACC controller) to the traffic conditions. Optionally, inter-vehicle and infrastructure-to-car communication can be used to improve the accuracy of determining the traffic states. Within a microscopic simulation framework, we have simulated the complete concept on a road section with an on-ramp bottleneck, using empirical loop-detector data for an afternoon rush-hour as input for the upstream boundary. We found that the ACC vehicles improve the traffic stability and the dynamic road capacity. While traffic congestion in the reference scenario was completely eliminated when simulating a proportion of 25% ACC vehicles, travel times were already significantly reduced for much lower penetration rates. The efficiency of the proposed driving strategy even for low market penetrations is a promising result for a successful application in future driver assistance systems.