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Distributed CAV Mobility Data Intelligence and Informatics Towards Self-Adaptive Traffic Operation and Sustainability Assessment /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Distributed CAV Mobility Data Intelligence and Informatics Towards Self-Adaptive Traffic Operation and Sustainability Assessment // Wei Lin.
Author:	Lin, Wei,
Description:	1 electronic resource (208 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: A.
Contained By:	Dissertations Abstracts International86-03A.
Subject:	Architectural engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31628952
ISBN:	9798384035442

Distributed CAV Mobility Data Intelligence and Informatics Towards Self-Adaptive Traffic Operation and Sustainability Assessment /
Lin, Wei,

Distributed CAV Mobility Data Intelligence and Informatics Towards Self-Adaptive Traffic Operation and Sustainability Assessment /Wei Lin. - 1 electronic resource (208 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: A.

Emerging transportation technologies, such as connected and autonomous vehicle (CAV) technologies with the support of cooperative vehicle-to-everything (C-V2X) wireless communications, are bringing up diversity of real-time data sources which will ultimately ubiquitously available over roadway networks. Examples include CAV mobility trajectory datasets, basic safety message (BSM) for vehicle safety state, and map data describing the physical geometry of road configurations such as intersections. Apparently, those emerging data sources can be used to depict the main characteristics of traffic flow operations and provide a solid basis to derive profiles of traffic patterns. The quality and scalability of the CAV mobility data really matters to the effect of CAVs cooperative driving behaviors and systematic traffic control schemes, aligned with the top priority of safety assurance. A convergent challenge hence arises from the imperative need to gather, process, analyze, store, and streamline vast quantities of CAV datasets to characterize large-scale traffic environment for maximizing safety and efficiency of the roadway network. If this challenge is successfully addressed, such an intelligent data fusion could have a great potential to be streamlined with other infrastructural monitoring data collected at different spatial locations to perform large-scale self-adaptive traffic control. With the support of edge/fog and cloud computing technologies, those dynamic traffic operational datasets can flow to the traffic management agencies, as a big data asset, by synergizing them with other heterogeneous land-use and environmental monitoring measurements. This also is a way to provide robust and reliable information sources for sustainability assessment.A comprehensive revelation and profound comprehension of the underlying mechanism and rationale for processing and interconnecting the CAV-enabled data sources with control infrastructures will establish the groundwork to advance the development and deployment of future Cyber-Physical Systems for Transportation (CPS-T). This, in turn, will contribute to achieving safer, more efficient, and environmentally friendly transportation.Generation of parameters for traffic control, management and related performance evaluation, as well as sustainability measurement require solid methods of scalable intelligence analytics aiming at data integration and consistency across a distributed data repository for efficient traffic management. Their often-complex nature is exacerbated by a mix of different types of vehicles, changes in traffic flow characteristics under impacts of various possible factors such as unharmonious driving behaviors and inclement weather. The emerging data sources are generally featured with concurrent operations while all data components are fault-tolerant (i.e., they would fail independently of each other). The identified challenges are therefore expected to be addressed via developing distributed data intelligence and Informatics related technologies and paradigms.The dissertation is hence motivated to develop theoretical framework for modeling the Distributed Computing Brainbox Analytics (DcBa-X). The DcBa-X is aimed to direct the development of computing algorithms to achieve the major functions as follows:• Data process and analytics of probe-based data sources and directions for developing computing algorithms towards self-adaptive traffic operation and performance information production.• Development of the reference framework for building distributed intelligent transportation data fusion for large-scale performance diagnosis and sustainability assessment.

English

ISBN: 9798384035442Subjects--Topical Terms:

3174102
Architectural engineering.
Subjects--Index Terms:

Cyber-physical systems

Distributed CAV Mobility Data Intelligence and Informatics Towards Self-Adaptive Traffic Operation and Sustainability Assessment /
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520 $a Emerging transportation technologies, such as connected and autonomous vehicle (CAV) technologies with the support of cooperative vehicle-to-everything (C-V2X) wireless communications, are bringing up diversity of real-time data sources which will ultimately ubiquitously available over roadway networks. Examples include CAV mobility trajectory datasets, basic safety message (BSM) for vehicle safety state, and map data describing the physical geometry of road configurations such as intersections. Apparently, those emerging data sources can be used to depict the main characteristics of traffic flow operations and provide a solid basis to derive profiles of traffic patterns. The quality and scalability of the CAV mobility data really matters to the effect of CAVs cooperative driving behaviors and systematic traffic control schemes, aligned with the top priority of safety assurance. A convergent challenge hence arises from the imperative need to gather, process, analyze, store, and streamline vast quantities of CAV datasets to characterize large-scale traffic environment for maximizing safety and efficiency of the roadway network. If this challenge is successfully addressed, such an intelligent data fusion could have a great potential to be streamlined with other infrastructural monitoring data collected at different spatial locations to perform large-scale self-adaptive traffic control. With the support of edge/fog and cloud computing technologies, those dynamic traffic operational datasets can flow to the traffic management agencies, as a big data asset, by synergizing them with other heterogeneous land-use and environmental monitoring measurements. This also is a way to provide robust and reliable information sources for sustainability assessment.A comprehensive revelation and profound comprehension of the underlying mechanism and rationale for processing and interconnecting the CAV-enabled data sources with control infrastructures will establish the groundwork to advance the development and deployment of future Cyber-Physical Systems for Transportation (CPS-T). This, in turn, will contribute to achieving safer, more efficient, and environmentally friendly transportation.Generation of parameters for traffic control, management and related performance evaluation, as well as sustainability measurement require solid methods of scalable intelligence analytics aiming at data integration and consistency across a distributed data repository for efficient traffic management. Their often-complex nature is exacerbated by a mix of different types of vehicles, changes in traffic flow characteristics under impacts of various possible factors such as unharmonious driving behaviors and inclement weather. The emerging data sources are generally featured with concurrent operations while all data components are fault-tolerant (i.e., they would fail independently of each other). The identified challenges are therefore expected to be addressed via developing distributed data intelligence and Informatics related technologies and paradigms.The dissertation is hence motivated to develop theoretical framework for modeling the Distributed Computing Brainbox Analytics (DcBa-X). The DcBa-X is aimed to direct the development of computing algorithms to achieve the major functions as follows:• Data process and analytics of probe-based data sources and directions for developing computing algorithms towards self-adaptive traffic operation and performance information production.• Development of the reference framework for building distributed intelligent transportation data fusion for large-scale performance diagnosis and sustainability assessment.
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650 4 $a Sustainability. $3 1029978
650 4 $a Transportation. $3 555912
653 $a Cyber-physical systems
653 $a Connected and autonomous vehicle
653 $a Distributed Computing Brainbox Analytics
653 $a Transportation technology
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710 2 $a University of Cincinnati. $b Engineering and Applied Science: Civil Engineering. $e degree granting institution. $3 3824213
720 1 $a Wei, Heng $e degree supervisor.
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