Hubert Rehborn

Bio: Hubert Rehborn is an academic researcher from Daimler AG. The author has contributed to research in topics: Three-phase traffic theory & Traffic congestion reconstruction with Kerner's three-phase theory. The author has an hindex of 19, co-authored 130 publications receiving 2694 citations.

Experimental properties of complexity in traffic flow

Abstract: Experimental investigations of a complexity in traffic flow are presented. It is shown that this complexity is linked to space-time transitions between three qualitative different kinds of traffic: "free" traffic flow, "synchronized" traffic flow, and traffic jams. Peculiarities of "synchronized" traffic flow and jams that are responsible for a complex behavior of traffic are found.

Experimental properties of phase transitions in traffic flow

Abstract: Investigations of a great number of measurements of traffic flow on German highways show that there are some common macroscopic properties of phase transitions between free flow, synchronized flow, and traffic jams. In particular, it is shown that a short-time localized perturbation is able to cause a local phase transition from free flow to synchronized flow and that synchronized flow of slow moving vehicles can further be self-maintained on a highway for several hours.

Experimental features and characteristics of traffic jams

Abstract: Based on experimental investigations of traffic on highways it is shown that traffic jams can move stable through a highway keeping their structure and characteristic parameters for a long time (at least for about 50 min, when the jams moved through the longest, 13.1 km, section of the investigated highways). The experimental features of an almost stationary moving jam have been found. An occurrence of complex space-time structures of traffic inside a wide traffic jam has been observed. \textcopyright{} 1996 The American Physical Society.

Traffic state detection with floating car data in road networks

Abstract: A method for a reporting behavior at optimal costs of single vehicles (FCD: floating car data) in road networks with the aim of a high quality of traffic state recognition is presented. It is shown that based on minimum two FCD messages the substantial information of a typical traffic incident in a traffic center can be recognized. The two relevant periods of such an obstruction of traffic in road networks are the periods, in which either a travel time increase takes place due to congestion emergence or a travel time decrease because of congestion dissolution. A statistic analysis already shows the high quality of the reconstruction of the actual travel times in the net with 1.5% equipped FCD vehicles and a reduction of the FCD message sending of the vehicles by suppression of redundant incident information. Incidents with at least 20 min duration can be recognized with a probability of 65% with an penetration rate of 1.5% FCD vehicles within the whole amount of vehicles, whereby the FCD vehicles send only in each incident case two messages per event.

Recognition and tracking of spatial–temporal congested traffic patterns on freeways

Abstract: The two models FOTO (Forecasting of Traffic Objects) and ASDA (Automatische Staudynamikanalyse: Automatic Tracking of Moving Traffic Jams) for the automatic recognition and tracking of congested spatial–temporal traffic flow patterns on freeways are presented The models are based on a spatial–temporal traffic phase classification made in the three-phase traffic theory by Kerner In this traffic theory, in congested traffic two different phases are distinguished: “wide moving jam” and “synchronized flow” The model FOTO is devoted to the identification of traffic phases and to the tracking of synchronized flow The model ASDA is devoted to the tracking of the propagation of moving jams The general approach and the different extensions of the models FOTO and ASDA are explained in detail It is stressed that the models FOTO and ASDA perform without any validation of model parameters in different environmental and traffic conditions Results of the online application of the models FOTO and ASDA at the TCC (Traffic Control Center) of Hessen near Frankfurt (Germany) are presented and evaluated

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Traffic and related self-driven many-particle systems

Abstract: Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ``phantom traffic jams'' even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ``freeze by heating''? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.

Linear and nonlinear waves, by G. B. Whitham. Pp.636. £50. 1999. ISBN 0 471 35942 4 (Wiley).

Abstract: to be done in this area. Face recognition is a problem that scarcely clamoured for attention before the computer age but, having surfaced, has involved a wide range of techniques and has attracted the attention of some fine minds (David Mumford was a Fields Medallist in 1974). This singular application of mathematical modelling to a messy applied problem of obvious utility and importance but with no unique solution is a pretty one to share with students: perhaps, returning to the source of our opening quotation, we may invert Duncan's earlier observation, 'There is an art to find the mind's construction in the face!'.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

Abstract: This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.