Single-vehicle data of highway traffic: A statistical analysis

Neubert, L.; Santen, Ludger; Schadschneider, Andreas; Schreckenberg, Michael

doi:10.1103/physreve.60.6480

Cited by 244 publications

(264 citation statements)

References 26 publications

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“…Both, theoretical [5,6,7] and empirical [8,9,10,11,12,13,14,15,16,17] studies indicate that the phenomenology of congested traffic is more complex than originally expected. There seems to be a rich spectrum of traffic states [5,6,12,13], hysteretic or continuous temporal or spatial transitions among them [7,11,12,13,18,19], and fluctuations or a erratically appearing dynamics play a significant role [13,16,20,21,22]. In order to make sense out of this, it is increasingly important to have suitable ways of data processing, in order to extract the information relevant for scientific investigations or specific applications.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Smoothing Method for Traffic State Identification from Incomplete Information

Treiber

Helbing

2003

Lecture Notes in Computational Science and Engineering

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Abstract. We present a new method to obtain spatio-temporal information from aggregated data of stationary traffic detectors, the "adaptive smoothing method". In essential, a nonlinear spatio-temporal lowpass filter is applied to the input detector data. This filter exploits the fact that, in congested traffic, perturbations travel upstream at a constant speed, while in free traffic, information propagates downstream. As a result, one obtains velocity, flow, or other traffic variables as smooth functions of space and time. Applications include traffic-state visualization, reconstruction of traffic situations from incomplete information, fast identification of traffic breakdowns (e.g., in incident detection), and experimental verification of traffic models.We apply the adaptive smoothing method to observed congestion patterns on several German freeways. It manages to make sense out of data where conventional visualization techniques fail. By ignoring up to 65% of the detectors and applying the method to the reduced data set, we show that the results are robust. The method works well if the distances between neighbouring detector cross sections do not exceed 3 km.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Smoothing Method for Traffic State Identification from Incomplete Information

Treiber

Helbing

2003

Lecture Notes in Computational Science and Engineering

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“…This location has the unique characteristic that, although the flow of traffic is high, traffic jams are very sporadically seen. In this sense it is a unique measurement point to gather experimental facts to understand the microscopic structures in synchronized traffic states [1], which was not reported outside of Germany yet.…”

Section: Measurement Analysis Architecturementioning

confidence: 99%

“…Recent studies have shown very complex structures in the traffic flow [1]. This state is called the synchronized state, which has to be distinguished from a free flowing and congested state.…”

Section: Introductionmentioning

confidence: 99%

The Measurement Architecture of the Virtual Traffic Laboratory

Visser

Zoetebier

Yakali

et al. 2004

Computational Science - ICCS 2004

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Abstract. In this paper we introduce the measurement architecuture of an application for the Virtual Traffic Laboratory. We have seamlessly integrated the analyses of aggregated information from simulation and measurements in a Matlab environment in which one can concentrate on finding the dependencies of the different parameters, select subsets in the measurements, and extrapolate the measurements via simulation. Available aggregated information is directly displayed and new aggregate information, produced in the background, is displayed as soon as it is available.

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“…For both lanes, longitudinal velocity (standard deviation (SD) of 8.3 km/h) was modeled based on available data from instrumented vehicles. The distance separation of the lead vehicles was based on a THW density distribution, with a mean THW of 1.15 s, based on [13]. Lateral-positioning behavior (swerving) of the traffic was created according to a model based on available lanekeeping data (SD of 0.30 m).…”

Section: Experimental Design and Procedurementioning

confidence: 99%

A Two-Dimensional Weighting Function for a Driver Assistance System

Winter

Mulder

Abbink

et al. 2008

IEEE Trans. Syst., Man, Cybern. B

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Abstract-Driver assistance systems that supply force feedback (FF) on the accelerator commonly use relative distance and velocity with respect to the closest lead vehicle in front of the own vehicle. This 1-D feedback might not accurately represent the situation and can cause unwanted step-shaped changes in the FFs during lateral maneuvers. To address these shortcomings, a 2-D system is proposed that calculates FF using a weighted average of the influences of lead vehicles. Offline simulations and an experiment in a driving simulator were performed to compare no feedback, 1-D systems, and the novel 2-D system during a car-following task with cut-in maneuvers. Results show that the 2-D feedback resulted in lower mean forces, lower response times to cut-in vehicles, and favorable subjective experiences as compared to the 1-D systems.

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Single-vehicle data of highway traffic: A statistical analysis

Cited by 244 publications

References 26 publications

An Adaptive Smoothing Method for Traffic State Identification from Incomplete Information

An Adaptive Smoothing Method for Traffic State Identification from Incomplete Information

The Measurement Architecture of the Virtual Traffic Laboratory

A Two-Dimensional Weighting Function for a Driver Assistance System

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