Specification for Archiving ITS-Generated Traffic Monitoring Data

10.1520/e2665 ◽  
2015 ◽  
Author(s):  
Keyword(s):  
Traffic Monitoring ◽  
Monitoring Data

scholarly journals Evaluating Probabilistic Traffic Load Effects on Large Bridges Using Long-Term Traffic Monitoring Data

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5056 ◽  
Author(s):  
Lu ◽  
Ma ◽  
Liu
Keyword(s):  
Control Measure ◽  
Heavy Traffic ◽  
Suspension Bridge ◽  
Traffic Monitoring ◽  
Traffic Load ◽  
Monitoring Data ◽  
Traffic Density ◽  
Maximum Deflection ◽  
Load Effects ◽  
Crossing Theory

With the steadily growing of global transportation market, the traffic load has increased dramatically over the past decades, which may develop into a risk source for existing bridges. The simultaneous presence of heavy trucks that are random in nature governs the serviceability limit for large bridges. This study investigated probabilistic traffic load effects on large bridges under actual heavy traffic load. Initially, critical stochastic traffic loading scenarios were simulated based on millions of traffic monitoring data in a highway bridge in China. A methodology of extrapolating maximum traffic load effects was presented based on the level-crossing theory. The effectiveness of the proposed method was demonstrated by probabilistic deflection investigation of a suspension bridge. Influence of traffic density variation and overloading control on the maximum deflection was investigated as recommendations for designers and managers. The numerical results show that the congested traffic mostly governs the critical traffic load effects on large bridges. Traffic growth results in higher maximum deformations and probabilities of failure of the bridge in its lifetime. Since the critical loading scenario contains multi-types of overloaded trucks, an effective overloading control measure has a remarkable influence on the lifetime maximum deflection. The stochastic traffic model and corresponding computational framework is expected to be developed to more types of bridges.

Intelligent Transportation System Data Archiving: Statistical Techniques for Determining Optimal Aggregation Widths for Inductive Loop Detector Speed Data

2000 ◽  
Vol 1719 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Byron J. Gajewski ◽  
Shawn M. Turner ◽  
William L. Eisele ◽  
Clifford H. Spiegelman
Keyword(s):  
Traffic Management ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation System ◽  
Monitoring Data ◽  
Statistical Techniques ◽  
Parameter Distribution ◽  
Data Archiving ◽  
Wide Range

Although most traffic management centers collect intelligent transportation system (ITS) traffic monitoring data from local controllers in 20-s to 30-s intervals, the time intervals for archiving data vary considerably from 1 to 5, 15, or even 60 min. Presented are two statistical techniques that can be used to determine optimal aggregation levels for archiving ITS traffic monitoring data: the cross-validated mean square error and the F-statistic algorithm. Both techniques seek to determine the minimal sufficient statistics necessary to capture the full information contained within a traffic parameter distribution. The statistical techniques were applied to 20-s speed data archived by the TransGuide center in San Antonio, Texas. The optimal aggregation levels obtained by using the two algorithms produced reasonable and intuitive results—both techniques calculated optimal aggregation levels of 60 min or more during periods of low traffic variability. Similarly, both techniques calculated optimal aggregation levels of 1 min or less during periods of high traffic variability (e.g., congestion). A distinction is made between conclusions about the statistical techniques and how the techniques can or should be applied to ITS data archiving. Although the statistical techniques described may not be disputed, there is a wide range of possible aggregation solutions based on these statistical techniques. Ultimately, the aggregation solutions may be driven by nonstatistical parameters such as cost (e.g., “How much do we/the market value the data?”), ease of implementation, system requirements, and other constraints.

scholarly journals Data Mining Methods for Traffic Monitoring Data Analysis: A case study

2011 ◽  
Vol 20 ◽  
pp. 455-464 ◽  
Author(s):  
Gregorio Gecchele ◽  
Riccardo Rossi ◽  
Massimiliano Gastaldi ◽  
Armando Caprini
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Traffic Monitoring ◽  
Monitoring Data ◽  
Mining Methods

scholarly journals Variability in continuous traffic monitoring data

1996 ◽  
Author(s):  
T. Wright ◽  
P.S. Hu ◽  
J. Young
Keyword(s):  
Traffic Monitoring ◽  
Monitoring Data
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