Volume 49, Issue 11 - 11

Using a recently developed microscopic traffic model, we simulate how speed limits, on-ramp controls, and vehicle-based driver-assistance systems influence freeway traffic. We present results for a section of the German Autobahn A8-East. Both, a speed limit and an on-ramp control could considerably reduce the severeness of the originally observed and simulated congestion. Introducing 20 % vehicles equipped with driver-assistance systems eliminated the congestion almost completely.

This paper presents a framework for on-line simulations and predictions of traffic states in large-scale networks, which is based on the combination of real-world traffic data with an agent-based traffic flow model. The system is applied to the urban road network of Duisburg and the freeway network of North Rhine-Westphalia. On the basis of historical traffic demand data heuristics are derived, which are combined with the current data to provide a short-term traffic forecast. Additionally, the demand for an anticipatory traffic forecast, which includes decision-making and route choice behaviour of the road users, is discussed.

Functionality and efficiency of traffic telematics systems depend crucially on the available data base. In our paper we argue that systems based on floating car data (FCD) display – in principle – self-consistency with respect to data generation and usage. Ensuing practical problems of FCD-based telematics systems are discussed. When designing a telematics system for the logistics of commercial traffic, business and operational aspects have to be taken into account. The corresponding systems design is discussed with reference to a tool developed at gedas telematics , the telematics provider in the Volkswagen Group .

In diesem Beitrag wird das Problem der Zustandsschätzung im Autobahnverkehr auf der Basis von gemischten Messdaten untersucht. Mit Hilfe eines schaltenden Kalman-Filters werden sowohl quasi-kontinuierliche Sensormessungen als auch unregelmässig eintreffende Informationen über Einzelfahrzeuggeschwindigkeiten (Floating Car Data) für die Schätzung des aktuellen Verkehrszustandes verarbeitet. Durch den schaltenden Charakter des Systems wird eine Stabilitäts- und Konvergenzanalyse notwendig. Diese zeigt, dass die Verwendung eines schaltenden Optimalfilters zu einem nicht divergenten Schätzfehler mit minimaler und beschränkter Varianz führt, während ein schaltendes stationäres Filter ein instabiles Schätzsystem zur Folge haben kann. Der Beitrag illustriert die Ergebnisse anhand von realen Autobahnmessdaten.

A review of methods for dynamic traffic forecasting which have recently been developed by DaimlerChrysler AG is presented. In particular, some new models for tracing and forecasting of congested spatial-temporal patterns on highways will be considered. These models ASDA (Automatische Staudynamikanalyse: Automatic Tracing of Moving Traffic Jams) and FOTO (Forecasting Of Traffic Objects) allow us to trace and to predict macroscopic spatial-temporal properties of the traffic patterns without any validation of model parameters at different infrastructure, weather and other environmental conditions. Results of the on-line application of these models are shortly discussed. A macroscopic model for dynamic forecast of traffic in urban areas is presented. This model allows us to predict macroscopic characteristics of traffic such as queue lengths at light signals and link travel times. The calculation time of the model can be several thousand times shorter than the one of microscopic traffic models.

Der Beitrag beschreibt am Beispiel einer automatischen, verkehrsabhängigen Auswahl von Ampelprogrammen zur Optimierung des Verkehrs in Städten den Einsatz lernender, adaptiver Verfahren. Hierbei werden speziell moderne Reinforcement-Lernverfahren vorgestellt.