Development and implementation of a real-time big-data management architecture for effective adaptive traffic signal control

Wuping Xin, Elena S. Prassas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Adaptive traffic signal control dynamically adjusts traffic signals based on prevailing traffic conditions. The acquisition and processing of real-time traffic data play a crucial role. The emerging trend of "big data", characterized as "three Vs'", i.e., Volume, Velocity and Variety, potentially enables novel signal control concepts and more effective adaptive signal control implementations. However, there has been a lack of relevant real-time big-data management architecture - an architecture that recognizes the disadvantages of existing general-purpose big-data technologies such as Hadoop/MapReduce or NoSQL, an architecture that is specifically targeted and optimized for adaptive signal control, capable of managing big-data that is very large (volume), very fast (velocity), and diverse (variety), and an architecture that allows real-time predictive analysis and performs regional adaptive traffic control that calls for parallel executions of relevant signal optimization algorithms for different sub-areas of complex traffic networks. This paper first examines the historical evolvement of adaptive traffic signal control, and points out the challenges and opportunities in nowadays data-rich environment. The relevance of the generalpurpose big-data technologies (MapReduce/Hadoop and NoSQL) is discussed from the signal control perspective. A new real-time big-data management architecture is proposed, considering massive realtime traffic data available nowadays and new types of data emerging in future. These data are generally collected at high frequency, in large amount, and supplied from different sources in realtime. The proposed architecture is specifically targeted for adaptive signal control applications. It features a hybrid design with both centralized and distributed elements, taking into account the efficient data archival and retrieval at physical disk sectors and memory levels, real-time traffic data fusion and synthetizing, in-memory caching and indexing, and a set of customized analytics supporting the novel concept of Signal Optimization Repository in adaptive traffic control. This architecture has been implemented as the core technology of the ACDSS system, which is a multiregime, variable-objective adaptive traffic control system developed by KLD. A case study is presented showing the real-life application of the proposed architecture in ACDSS operations with hundreds of signalized intersections of New York City arterials and grid networks.

Original languageEnglish (US)
Title of host publication21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World
PublisherIntelligent Transport Systems (ITS)
StatePublished - 2014
Event21st World Congress on Intelligent Transport Systems: Reinventing Transportation in Our Connected World, ITSWC 2014 - Detroit, United States
Duration: Sep 7 2014Sep 11 2014

Other

Other21st World Congress on Intelligent Transport Systems: Reinventing Transportation in Our Connected World, ITSWC 2014
CountryUnited States
CityDetroit
Period9/7/149/11/14

Fingerprint

Traffic signals
traffic control
Information management
management
Traffic control
traffic
Data storage equipment
Data fusion
Big data
time
Control systems
indexing
Processing
control system

Keywords

  • ACDSS
  • Adaptive traffic signal control
  • Big-data
  • Grid network
  • New York City traffic signal control
  • Real-time traffic control
  • Traffic management

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Automotive Engineering
  • Transportation
  • Electrical and Electronic Engineering

Cite this

Xin, W., & Prassas, E. S. (2014). Development and implementation of a real-time big-data management architecture for effective adaptive traffic signal control. In 21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World Intelligent Transport Systems (ITS).

Development and implementation of a real-time big-data management architecture for effective adaptive traffic signal control. / Xin, Wuping; Prassas, Elena S.

21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World. Intelligent Transport Systems (ITS), 2014.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Xin, W & Prassas, ES 2014, Development and implementation of a real-time big-data management architecture for effective adaptive traffic signal control. in 21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World. Intelligent Transport Systems (ITS), 21st World Congress on Intelligent Transport Systems: Reinventing Transportation in Our Connected World, ITSWC 2014, Detroit, United States, 9/7/14.
Xin W, Prassas ES. Development and implementation of a real-time big-data management architecture for effective adaptive traffic signal control. In 21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World. Intelligent Transport Systems (ITS). 2014
Xin, Wuping ; Prassas, Elena S. / Development and implementation of a real-time big-data management architecture for effective adaptive traffic signal control. 21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World. Intelligent Transport Systems (ITS), 2014.
@inproceedings{3a7ae3ca36204160a37462bb4fc6c882,
title = "Development and implementation of a real-time big-data management architecture for effective adaptive traffic signal control",
abstract = "Adaptive traffic signal control dynamically adjusts traffic signals based on prevailing traffic conditions. The acquisition and processing of real-time traffic data play a crucial role. The emerging trend of {"}big data{"}, characterized as {"}three Vs'{"}, i.e., Volume, Velocity and Variety, potentially enables novel signal control concepts and more effective adaptive signal control implementations. However, there has been a lack of relevant real-time big-data management architecture - an architecture that recognizes the disadvantages of existing general-purpose big-data technologies such as Hadoop/MapReduce or NoSQL, an architecture that is specifically targeted and optimized for adaptive signal control, capable of managing big-data that is very large (volume), very fast (velocity), and diverse (variety), and an architecture that allows real-time predictive analysis and performs regional adaptive traffic control that calls for parallel executions of relevant signal optimization algorithms for different sub-areas of complex traffic networks. This paper first examines the historical evolvement of adaptive traffic signal control, and points out the challenges and opportunities in nowadays data-rich environment. The relevance of the generalpurpose big-data technologies (MapReduce/Hadoop and NoSQL) is discussed from the signal control perspective. A new real-time big-data management architecture is proposed, considering massive realtime traffic data available nowadays and new types of data emerging in future. These data are generally collected at high frequency, in large amount, and supplied from different sources in realtime. The proposed architecture is specifically targeted for adaptive signal control applications. It features a hybrid design with both centralized and distributed elements, taking into account the efficient data archival and retrieval at physical disk sectors and memory levels, real-time traffic data fusion and synthetizing, in-memory caching and indexing, and a set of customized analytics supporting the novel concept of Signal Optimization Repository in adaptive traffic control. This architecture has been implemented as the core technology of the ACDSS system, which is a multiregime, variable-objective adaptive traffic control system developed by KLD. A case study is presented showing the real-life application of the proposed architecture in ACDSS operations with hundreds of signalized intersections of New York City arterials and grid networks.",
keywords = "ACDSS, Adaptive traffic signal control, Big-data, Grid network, New York City traffic signal control, Real-time traffic control, Traffic management",
author = "Wuping Xin and Prassas, {Elena S.}",
year = "2014",
language = "English (US)",
booktitle = "21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World",
publisher = "Intelligent Transport Systems (ITS)",

}

TY - GEN

T1 - Development and implementation of a real-time big-data management architecture for effective adaptive traffic signal control

AU - Xin, Wuping

AU - Prassas, Elena S.

PY - 2014

Y1 - 2014

N2 - Adaptive traffic signal control dynamically adjusts traffic signals based on prevailing traffic conditions. The acquisition and processing of real-time traffic data play a crucial role. The emerging trend of "big data", characterized as "three Vs'", i.e., Volume, Velocity and Variety, potentially enables novel signal control concepts and more effective adaptive signal control implementations. However, there has been a lack of relevant real-time big-data management architecture - an architecture that recognizes the disadvantages of existing general-purpose big-data technologies such as Hadoop/MapReduce or NoSQL, an architecture that is specifically targeted and optimized for adaptive signal control, capable of managing big-data that is very large (volume), very fast (velocity), and diverse (variety), and an architecture that allows real-time predictive analysis and performs regional adaptive traffic control that calls for parallel executions of relevant signal optimization algorithms for different sub-areas of complex traffic networks. This paper first examines the historical evolvement of adaptive traffic signal control, and points out the challenges and opportunities in nowadays data-rich environment. The relevance of the generalpurpose big-data technologies (MapReduce/Hadoop and NoSQL) is discussed from the signal control perspective. A new real-time big-data management architecture is proposed, considering massive realtime traffic data available nowadays and new types of data emerging in future. These data are generally collected at high frequency, in large amount, and supplied from different sources in realtime. The proposed architecture is specifically targeted for adaptive signal control applications. It features a hybrid design with both centralized and distributed elements, taking into account the efficient data archival and retrieval at physical disk sectors and memory levels, real-time traffic data fusion and synthetizing, in-memory caching and indexing, and a set of customized analytics supporting the novel concept of Signal Optimization Repository in adaptive traffic control. This architecture has been implemented as the core technology of the ACDSS system, which is a multiregime, variable-objective adaptive traffic control system developed by KLD. A case study is presented showing the real-life application of the proposed architecture in ACDSS operations with hundreds of signalized intersections of New York City arterials and grid networks.

AB - Adaptive traffic signal control dynamically adjusts traffic signals based on prevailing traffic conditions. The acquisition and processing of real-time traffic data play a crucial role. The emerging trend of "big data", characterized as "three Vs'", i.e., Volume, Velocity and Variety, potentially enables novel signal control concepts and more effective adaptive signal control implementations. However, there has been a lack of relevant real-time big-data management architecture - an architecture that recognizes the disadvantages of existing general-purpose big-data technologies such as Hadoop/MapReduce or NoSQL, an architecture that is specifically targeted and optimized for adaptive signal control, capable of managing big-data that is very large (volume), very fast (velocity), and diverse (variety), and an architecture that allows real-time predictive analysis and performs regional adaptive traffic control that calls for parallel executions of relevant signal optimization algorithms for different sub-areas of complex traffic networks. This paper first examines the historical evolvement of adaptive traffic signal control, and points out the challenges and opportunities in nowadays data-rich environment. The relevance of the generalpurpose big-data technologies (MapReduce/Hadoop and NoSQL) is discussed from the signal control perspective. A new real-time big-data management architecture is proposed, considering massive realtime traffic data available nowadays and new types of data emerging in future. These data are generally collected at high frequency, in large amount, and supplied from different sources in realtime. The proposed architecture is specifically targeted for adaptive signal control applications. It features a hybrid design with both centralized and distributed elements, taking into account the efficient data archival and retrieval at physical disk sectors and memory levels, real-time traffic data fusion and synthetizing, in-memory caching and indexing, and a set of customized analytics supporting the novel concept of Signal Optimization Repository in adaptive traffic control. This architecture has been implemented as the core technology of the ACDSS system, which is a multiregime, variable-objective adaptive traffic control system developed by KLD. A case study is presented showing the real-life application of the proposed architecture in ACDSS operations with hundreds of signalized intersections of New York City arterials and grid networks.

KW - ACDSS

KW - Adaptive traffic signal control

KW - Big-data

KW - Grid network

KW - New York City traffic signal control

KW - Real-time traffic control

KW - Traffic management

UR - http://www.scopus.com/inward/record.url?scp=84929179696&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84929179696&partnerID=8YFLogxK

M3 - Conference contribution

BT - 21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World

PB - Intelligent Transport Systems (ITS)

ER -