Development of a Novel Integrated Evaluation Index for Freeway Traffic Data

Since there are many influencing factors about freeway traffic safety, in order to provide a basis for comprehensive evaluation of traffic safety, on the basis of comprehensively analyzing the existing evaluation system of traffic safety, the evaluation index system of freeway traffic safety is established according to freeway characteristic under the unusual situation, as well as considering the feasibility of data collection and the rationality of setting up the index, which gives the definition, quantitative formula and suggestion value of domain for the indexes.

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Macroscopic Modeling of Freeway Traffic Using an Artificial Neural Network

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1588-14 ◽

1997 ◽

Vol 1588 (1) ◽

pp. 110-119 ◽

Cited By ~ 30

Author(s):

Hongjun Zhang ◽

Stephen G. Ritchie ◽

Zhen-Ping Lo

Keyword(s):

Neural Network ◽

Traffic Flow ◽

Network Model ◽

Neural Network Model ◽

Flow Model ◽

Traffic Data ◽

Freeway Traffic ◽

Traffic Flow Model ◽

Traffic System ◽

Real Traffic

Traffic flow on freeways is a complex process that often is described by a set of highly nonlinear, dynamic equations in the form of a macroscopic traffic flow model. However, some of the existing macroscopic models have been found to exhibit instabilities in their behavior and often do not track real traffic data correctly. On the other hand, microscopic traffic flow models can yield more detailed and accurate representations of traffic flow but are computationally intensive and typically not suitable for real-time implementation. Nevertheless, such implementations are likely to be necessary for development and application of advanced traffic control concepts in intelligent vehicle-highway systems. The development of a multilayer feed-forward artificial neural network model to address the freeway traffic system identification problem is presented. The solution of this problem is viewed as an essential element of an effort to build an improved freeway traffic flow model for the purpose of developing real-time predictive control strategies for dynamic traffic systems. To study the initial feasibility of the proposed neural network approach for traffic system identification, a three-layer feed-forward neural network model has been developed to emulate an improved version of a well-known higher-order continuum traffic model. Simulation results show that the neural network model can capture the traffic dynamics of this model quite closely. Future research will attempt to attain similar levels of performance using real traffic data.

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Research of Influence Factors on Freeway Incident Detection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.724.343 ◽

2015 ◽

Vol 724 ◽

pp. 343-346

Author(s):

Zhi Yong Yang ◽

Gui Yun Yan

Keyword(s):

Traffic Flow ◽

Traffic Simulation ◽

Influence Factors ◽

Simulation Software ◽

Incident Detection ◽

Traffic Data ◽

Simulation Data ◽

Freeway Traffic ◽

Traffic Incident ◽

Occupation Rate

This paper analyzes the kinds of freeway traffic incident and influences on traffic flow of recurring traffic incident and non-recurring traffic incident. It is full of interest and very useful. By using the traffic simulation software TSIS(Traffic Software Integration Systems) to obtain correlation traffic data which is needed when researching, then study and analyzes how to choose traffic parameters which are mainly traffic low speed, occupancy and lane occupancy of traffic incident detection. After analyzing the simulation data, we can find that it is more reasonable that choosing the vehicle lane occupation rate as detection parameter of traffic incident.

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Algorithm for Detecting and Correcting Errors in Archived Traffic Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1855-23 ◽

2003 ◽

Vol 1855 (1) ◽

pp. 183-190 ◽

Cited By ~ 11

Author(s):

Zachary R. Wall ◽

Daniel J. Dailey

Keyword(s):

Time Series ◽

Correction Factor ◽

Traffic Management ◽

Original Data ◽

Management Systems ◽

Traffic Data ◽

Traditional Methods ◽

Data Set ◽

Freeway Traffic ◽

Line Analysis

An algorithm is presented for correcting errors in archived loop data from freeway traffic-management systems that are the result of poorly calibrated sensors. These errors pose a significant difficulty when archived data are used in off-line analysis because the calibration errors are difficult to detect by using traditional methods. In the presented work, consistency of vehicle counts is used to judge the validity of the data: if vehicles counts are balanced, the data are valid; if vehicle counts are not balanced, the data are not valid. The method also can determine a correction factor. This correction factor is used to create a time series that can be combined with the original data to adjust the volume to create a consistent data set. To illustrate the methodology, an example case is presented that details the process of identifying a pair of reference stations that are properly calibrated. After the reference stations are identified, a poorly calibrated station is identified, and the data from this station are corrected. The result of the correction process is discussed.

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