On-Line Estimation of Traffic Densities from Time-Series of Flow and Speed Data

Gazis, Denos C.; Knapp, C.

doi:10.1287/trsc.5.3.283

Cited by 122 publications

(47 citation statements)

References 3 publications

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“…Since fusing different types of data influences the minimum quality level needed, for this problem it is also relevant to study the effect of combinations of these different types of data. Concerning the use and comparison of induction loop data or FCD data, research had been done already for example by Gazis and Knapp (1971). In this article, a new method is put forward for fusing heterogeneous and semantically different data from different traffic sensors.…”

Section: State-of-the-art Of Research On Traffic Data Qualitymentioning

confidence: 99%

The Effect of Inaccurate Traffic Data for Ramp Metering: Comparing Loop Detectors and Cameras Using Information Utility

Klunder

Taale²,

Kester

et al. 2014

IFAC Proceedings Volumes

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This paper introduces a topic which is of growing interest in the traffic control and management community, namely the relation between traffic data quality and the efficiency of traffic management. After some explanatory background and state of the art, it will become clear that no standard approach is available for traffic engineers to determine which accuracy of traffic data is needed for a certain traffic management application. In this paper we will show how the effect of inaccurate measurements on network level can be quantified by determining information utility functions. This approach is illustrated by presenting the results of a simulation study into the effect of inaccurate loop detector measurements on a ramp metering system. The relation between different accuracies of flow and speed measurements as input for a ramp metering system and the corresponding effect on the network performance is studied with a micro simulation model. The results of the simulation are used in a cost-benefit analysis for a case comparing loop detectors with camera measurements, where loop detectors are considered more accurate but also more expensive. We will show that higher measurement accuracy will lead to a better performance of the ramp metering system, resulting in a higher average network speed, and also quantify this relationship.

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Section: State-of-the-art Of Research On Traffic Data Qualitymentioning

confidence: 99%

The Effect of Inaccurate Traffic Data for Ramp Metering: Comparing Loop Detectors and Cameras Using Information Utility

Klunder

Taale²,

Kester

et al. 2014

IFAC Proceedings Volumes

View full text Add to dashboard Cite

show abstract

“…Notice that the most widely used tools for traffic state estimation are Kalman filters and their extensions. Thus, G a z i s and K n a p p [10], K n a p p [12] have proposed a Kalman filtering techniques for data processing. Such a method is based on time series of the mean speed and flow data from each detector and then it generates crude estimates of the vehicle counts.…”

Section: Introductionmentioning

confidence: 99%

Macroscopic Traffic Flow Control Via State Estimation

Abouaïssa

Majid

2015

Cybernetics and Information Technologies

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“…Secondly, the use of a Kalman filter is mostly adapted for linear models, while the measurements are a nonlinear function of the state variables. [18] and [19] have considered the problem of processing data at a fixed spatial location to produce estimates of spatial average quantities. As stated in [21], with good initial conditions, Nahi's method shows the ability to estimate the density closely in homogeneous situations [20].…”

Section: Introductionmentioning

confidence: 99%

“…Such an estimation method, which is of algebraic character, was developed by M. Fliess et al (see [9]), and it differs from other approaches of this type because it involves the use of differential geometry. As mentioned in [4], the method is applied to obtain an estimate of the derivative from any signal, thus avoiding reliance on the system model at least in the estimation of states 2 .…”

Section: Introductionmentioning

confidence: 99%