scholarly journals Air quality impact of intelligent transportation system actions used in a decision support system for adaptive traffic management

2015 ◽  
Vol 57 (3/4) ◽  
pp. 133 ◽  
Author(s):  
Stijn Vranckx ◽  
Wouter Lefebvre ◽  
Martine Van Poppel ◽  
Carolien Beckx ◽  
Jan Theunis ◽  
...  
Keyword(s):  
Decision Support ◽  
Air Quality ◽  
Decision Support System ◽  
Support System ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System

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.

Individual Exposure Modelling in an Integrated Decision Support System for Air Quality Management

Epidemiology ◽  
2009 ◽  
Vol 20 ◽  
pp. S21-S22
Author(s):  
Alexandra Kuhn ◽  
Miranda Loh ◽  
Lydia Gerharz ◽  
Sandra Torras Ortiz ◽  
Aileen Yang ◽  
...  
Keyword(s):  
Decision Support ◽  
Air Quality ◽  
Quality Management ◽  
Decision Support System ◽  
Support System ◽  
Air Quality Management ◽  
Individual Exposure

scholarly journals A Statistical Model for Shutdowns due to Air Quality Control for a Copper Production Decision Support System

Organizacija ◽  
2015 ◽  
Vol 48 (3) ◽  
pp. 198-202
Author(s):  
Khalid Aboura
Keyword(s):  
Quality Control ◽  
Decision Support ◽  
Air Quality ◽  
Decision Support System ◽  
Statistical Model ◽  
Support System ◽  
Copper Production ◽  
Production Facility ◽  
Air Quality Control ◽  
High Level

Abstract Background: In the mid-1990s, a decision support system for copper production was developed for one of the largest mining companies in Australia. The research was conducted by scientists from the largest Australian research center and involved the use of simulation to analyze options to increase production of a copper production facility. Objectives: We describe a statistical model for shutdowns due to air quality control and some of the data analysis conducted during the simulation project. We point to the fact that the simulation was a sophisticated exercise that consisted of many modules and the statistical model for shutdowns was essential for valid simulation runs. Method: The statistical model made use of a full year of data on daily downtimes and used a combination of techniques to generate replications of the data. Results: The study was conducted with a high level of cooperation between the scientists and the mining company. This contributed to the development of accurate estimates for input into a support system with an EXCEL based interface. Conclusion: The environmental conditions affected greatly the operations of the production facility. A good statistical model was essential for the successful simulation and the high budget expansion decision that ensued.

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