Development of Intelligent Transportation System Data Management

1998 ◽  
Vol 1625 (1) ◽  
pp. 124-130 ◽  
Author(s):  
Robert E. Brydia ◽  
Shawn M. Turner ◽  
William L. Eisele ◽  
Jyh C. Liu
Keyword(s):  
Data Management ◽  
Urban Areas ◽  
Intelligent Transportation System ◽  
San Antonio ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Data Management System ◽  
Transportation Management ◽  
User Costs ◽  
System Data

The intelligent transportation system (ITS) components deployed in U.S. urban areas produce vast amounts of data. These ITS data often are used for real-time operations and then are discarded. Few transportation management centers have any mechanism for sharing the data resources among other transportation groups or agencies within the same jurisdiction. Meanwhile, transportation analysts and researchers often struggle to obtain accurate, reliable data about existing transportation performance and patterns. The development of an ITS data management system (referred to as ITS DataLink) that is used to store, access, analyze, and present data from the TransGuide center in San Antonio, Texas, is presented. Data outputs are both tabular and graphical. No user costs are associated with the system except for an Internet connection.

Telematics and Mobile Internet

2021 ◽  
pp. 373-396
Author(s):  
Gurkan Tuna ◽  
Korhan Cengiz
Keyword(s):  
Mobile Networks ◽  
Urban Areas ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Mobile Internet ◽  
Future Research ◽  
Internet Applications ◽  
Data Rates ◽  
Latency Jitter

Telematics technologies and vehicular communications enable various intelligent transportation system applications with different data flow requirements that must be considered by the communications infrastructure provider in terms of transmission reliability, latency, jitter, and security. To meet those requirements, the dynamic nature of traffic and spatiotemporal features of roads must be considered. In parallel with the full coverage in urban areas and increase in the data rates, mobile networks have been started to be widely used by intelligent transportation system applications, especially for gathering data from various sensors. In this chapter, firstly, the current situation of telematics applications for intelligent transportation system is focused on and then mobile internet and mobile internet based applications are reviewed. Second, how much benefit vehicle telematics and mobile internet applications can obtain from the evolution of mobile networks is analysed. Finally, future research directions in this domain are pointed out.

Archived Intelligent Transportation System Data Quality: Preliminary Analyses of San Antonio TransGuide Data

2000 ◽  
Vol 1719 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Shawn Turner ◽  
Luke Albert ◽  
Byron Gajewski ◽  
William Eisele
Keyword(s):  
Missing Data ◽  
Data Quality ◽  
Intelligent Transportation System ◽  
San Antonio ◽  
Ground Truth ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Full Potential ◽  
Traffic Volumes ◽  
Erroneous Data

Described are three data quality attributes that are considered relevant to intelligent transportation system (ITS) data archiving: suspect or erroneous data, missing data, and data accuracy. Preliminary analyses of loop detector data from the TransGuide system in San Antonio were performed to identify the nature and extent of these data quality concerns in typical archived ITS data. The findings of the analyses indicated that missing data were inevitable, accounting for about one in five of all possible data records. Error detection rules were developed to screen for suspect or erroneous data, which accounted for only 1 percent of all possible data records. Baseline testing of TransGuide detector accuracy showed mixed results; one location collected traffic volumes within 5 percent of ground truth, whereas traffic volumes at another location ranged from 12 to 38 percent of ground truth. It was concluded that data quality procedures will be essential for realizing the full potential of archived ITS data.

Fine-grained vehicle emission management using intelligent transportation system data

2018 ◽  
Vol 241 ◽  
pp. 1027-1037 ◽  
Author(s):  
Shaojun Zhang ◽  
Tianlin Niu ◽  
Ye Wu ◽  
K. Max Zhang ◽  
Timothy J. Wallington ◽  
...  
Keyword(s):  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Vehicle Emission ◽  
Fine Grained ◽  
System Data

Empirical Approaches to Outlier Detection in Intelligent Transportation Systems Data

2003 ◽  
Vol 1840 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Eun Sug Park ◽  
Shawn Turner ◽  
Clifford H. Spiegelman
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
San Antonio ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Screening Methods ◽  
Traffic Data ◽  
New Methods ◽  
System Data ◽  
Empirical Approaches

Novel methods for implementation of detector-level multivariate screening methods are presented. The methods use present data and classify data as outliers on the basis of comparisons with empirical cutoff points derived from extensive archived data rather than from standard statistical tables. In addition, while many of the ideas of the classical Hotelling’s T2-statistic are used, modern statistical trend removal and blocking are incorporated. The methods are applied to intelligent transportation system data from San Antonio and Austin, Texas. These examples show how the suggested new methods perform with high-quality traffic data and apparently lower-quality traffic data. All algorithms were implemented by using the SAS programming language.

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