Intelligent traffic management platform research and design based on big data

2018 ◽  
pp. 133-137
Keyword(s):  
Big Data ◽  
Traffic Management ◽  
Management Platform ◽  
Research And Design

Telecommunications- and Information Technology–Inspired Analyses: Review of an Intelligent Transportation Systems Experience

2017 ◽  
Vol 2658 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Yupo Chan
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Real Time ◽  
Traffic Management ◽  
Transportation Systems ◽  
Lessons Learned ◽  
Connected Vehicles ◽  
Ongoing Research ◽  
Real Time Information ◽  
Time Information

This paper reviews both the author’s experience with managing highway network traffic on a real-time basis and the ongoing research into harnessing the potential of telecommunications and information technology (IT). On the basis of the lessons learned, this paper speculates about how telecommunications and IT capabilities can respond to current and future developments in traffic management. Issues arising from disruptive telecommunications technologies include the ready availability of real-time information, the crowdsourcing of information, the challenges of big data, and the need for information quality. Issues arising from transportation technologies include autonomous vehicles and connected vehicles and new taxi-like car- and bikesharing. Illustrations are drawn from the following core functions of a traffic management center: ( a) detecting and resolving an incident (possibly through crowdsourcing), ( b) monitoring and forecasting traffic (possibly through connected vehicles serving as sensors), ( c) advising motorists about routing alternatives (possibly through real-time information), and ( d) configuring traffic control strategies and tactics (possibly though big data). The conclusion drawn is that agility is the key to success in an ever-evolving technological scene. The solid guiding principle remains innovative and rigorous analytical procedures that build on the state of the art in the field, including both hard and soft technologies. The biggest modeling and simulation challenge remains the unknown, including such rapidly emerging trends as the Internet of things and the smart city.

scholarly journals Computer Data Storage and Management Platform Based on Big Data

2021 ◽  
Vol 2066 (1) ◽  
pp. 012022
Author(s):  
Cheng Luo
Keyword(s):  
Big Data ◽  
Data Management ◽  
Performance Improvement ◽  
Data Storage ◽  
Nonvolatile Memory ◽  
Linear Structure ◽  
Ring Structure ◽  
Computer Data ◽  
Management Platform ◽  
A Performance

Abstract Due to the continuous development of information technology, data has increasingly become the core of the daily operation of enterprises and institutions, the main basis for decision-making development. At the same time, due to the development of network, the storage and management of computer data has attracted more and more attention. Aiming at the common problems of computer data storage and management in practical work, this paper analyzes the object and content of data management, investigates the situation of computer data storage and management in China in recent two years, and interviews and tests the data of programming in this design platform. At the same time, in view of the related problems, the research results are applied to practice. On the basis of big data, the storage and management platform is designed. The research and design adopts a special B+ tree node linear structure of CIRC tree, and the linear node structure is changed into a ring structure, which greatly reduces the number of data persistence instructions and the performance overhead. The results show that compared with the most advanced B+ tree design for nonvolatile memory, crab tree has 3.1 times and 2.5 times performance improvement in reading and writing, respectively. Compared with the previous NV tree designed for nonvolatile memory, it has a performance improvement of 1.5 times, and a performance improvement of 8.4 times compared with the latest fast-fair. In the later stage, the expansion of the platform functions is conducive to the analysis and construction of data related storage and management functions, and further improve the ability of data management.

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