ADMSv2: A modern architecture for transportation data management and analysis

Chrysovalantis Anastasiou; Jianfa Lin; Chaoyang He; Yao Yi Chiang; Cyrus Shahabi

doi:10.1145/3356395.3365544

ADMSv2: A modern architecture for transportation data management and analysis

Chrysovalantis Anastasiou, Jianfa Lin, Chaoyang He, Yao Yi Chiang, Cyrus Shahabi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Scopus citations

Abstract

This paper presents ADMSv2, an end-to-end data-driven system that enables real-time and historical data analytics and machine learning tasks over big, streaming, spatiotemporal data. ADMSv2 employs a unified multi-layered architecture that integrates several open-source frameworks to collect, store, manage, and analyze a variety of data sources, including massive traffic sensor data, bus trajectory data, transportation network data, and traffic incidents data. ADMSv2 enables numerous applications in intelligent transportation, urban planning, public policy, and emergency response, all of which are critical for city resilience. Here, we demonstrate three application scenarios running on top of ADMSv2 to showcase the efficiency of its capabilities of query processing on real-world streaming and historical data as well as real-time data analysis using deep learning for traffic forecasting.

Original language	English (US)
Title of host publication	Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019
Editors	Bandana Kar, Olufemi A. Omitaomu, Xinyue Ye, Shima Mohebbi, Guangtao Fu
Publisher	Association for Computing Machinery, Inc
Pages	25-28
Number of pages	4
ISBN (Electronic)	9781450369541
DOIs	https://doi.org/10.1145/3356395.3365544
State	Published - Nov 5 2019
Externally published	Yes
Event	2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019 - Chicago, United States Duration: Nov 5 2019 → …

Publication series

Name	Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019

Conference

Conference	2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019
Country/Territory	United States
City	Chicago
Period	11/5/19 → …

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

Analytics
Datasets
Neural networks
Smart city
Transportation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Publisher link

10.1145/3356395.3365544

Find It

Find It at U of M Libraries

Cite this

Anastasiou, C., Lin, J., He, C., Chiang, Y. Y., & Shahabi, C. (2019). ADMSv2: A modern architecture for transportation data management and analysis. In B. Kar, O. A. Omitaomu, X. Ye, S. Mohebbi, & G. Fu (Eds.), Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019 (pp. 25-28). (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3356395.3365544

ADMSv2: A modern architecture for transportation data management and analysis. / Anastasiou, Chrysovalantis; Lin, Jianfa; He, Chaoyang et al.
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019. ed. / Bandana Kar; Olufemi A. Omitaomu; Xinyue Ye; Shima Mohebbi; Guangtao Fu. Association for Computing Machinery, Inc, 2019. p. 25-28 (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Anastasiou, C, Lin, J, He, C, Chiang, YY & Shahabi, C 2019, ADMSv2: A modern architecture for transportation data management and analysis. in B Kar, OA Omitaomu, X Ye, S Mohebbi & G Fu (eds), Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019, Association for Computing Machinery, Inc, pp. 25-28, 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019, Chicago, United States, 11/5/19. https://doi.org/10.1145/3356395.3365544

Anastasiou C, Lin J, He C, Chiang YY, Shahabi C. ADMSv2: A modern architecture for transportation data management and analysis. In Kar B, Omitaomu OA, Ye X, Mohebbi S, Fu G, editors, Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019. Association for Computing Machinery, Inc. 2019. p. 25-28. (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019). doi: 10.1145/3356395.3365544

Anastasiou, Chrysovalantis ; Lin, Jianfa ; He, Chaoyang et al. / ADMSv2 : A modern architecture for transportation data management and analysis. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019. editor / Bandana Kar ; Olufemi A. Omitaomu ; Xinyue Ye ; Shima Mohebbi ; Guangtao Fu. Association for Computing Machinery, Inc, 2019. pp. 25-28 (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019).

@inproceedings{139c6ac1f88c41fe9caa1977b8e0ff30,

title = "ADMSv2: A modern architecture for transportation data management and analysis",

abstract = "This paper presents ADMSv2, an end-to-end data-driven system that enables real-time and historical data analytics and machine learning tasks over big, streaming, spatiotemporal data. ADMSv2 employs a unified multi-layered architecture that integrates several open-source frameworks to collect, store, manage, and analyze a variety of data sources, including massive traffic sensor data, bus trajectory data, transportation network data, and traffic incidents data. ADMSv2 enables numerous applications in intelligent transportation, urban planning, public policy, and emergency response, all of which are critical for city resilience. Here, we demonstrate three application scenarios running on top of ADMSv2 to showcase the efficiency of its capabilities of query processing on real-world streaming and historical data as well as real-time data analysis using deep learning for traffic forecasting.",

keywords = "Analytics, Datasets, Neural networks, Smart city, Transportation",

author = "Chrysovalantis Anastasiou and Jianfa Lin and Chaoyang He and Chiang, {Yao Yi} and Cyrus Shahabi",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019 ; Conference date: 05-11-2019",

year = "2019",

month = nov,

day = "5",

doi = "10.1145/3356395.3365544",

language = "English (US)",

series = "Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019",

publisher = "Association for Computing Machinery, Inc",

pages = "25--28",

editor = "Bandana Kar and Omitaomu, {Olufemi A.} and Xinyue Ye and Shima Mohebbi and Guangtao Fu",

booktitle = "Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019",

}

TY - GEN

T1 - ADMSv2

T2 - 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019

AU - Anastasiou, Chrysovalantis

AU - Lin, Jianfa

AU - He, Chaoyang

AU - Chiang, Yao Yi

AU - Shahabi, Cyrus

PY - 2019/11/5

Y1 - 2019/11/5

N2 - This paper presents ADMSv2, an end-to-end data-driven system that enables real-time and historical data analytics and machine learning tasks over big, streaming, spatiotemporal data. ADMSv2 employs a unified multi-layered architecture that integrates several open-source frameworks to collect, store, manage, and analyze a variety of data sources, including massive traffic sensor data, bus trajectory data, transportation network data, and traffic incidents data. ADMSv2 enables numerous applications in intelligent transportation, urban planning, public policy, and emergency response, all of which are critical for city resilience. Here, we demonstrate three application scenarios running on top of ADMSv2 to showcase the efficiency of its capabilities of query processing on real-world streaming and historical data as well as real-time data analysis using deep learning for traffic forecasting.

AB - This paper presents ADMSv2, an end-to-end data-driven system that enables real-time and historical data analytics and machine learning tasks over big, streaming, spatiotemporal data. ADMSv2 employs a unified multi-layered architecture that integrates several open-source frameworks to collect, store, manage, and analyze a variety of data sources, including massive traffic sensor data, bus trajectory data, transportation network data, and traffic incidents data. ADMSv2 enables numerous applications in intelligent transportation, urban planning, public policy, and emergency response, all of which are critical for city resilience. Here, we demonstrate three application scenarios running on top of ADMSv2 to showcase the efficiency of its capabilities of query processing on real-world streaming and historical data as well as real-time data analysis using deep learning for traffic forecasting.

KW - Analytics

KW - Datasets

KW - Neural networks

KW - Smart city

KW - Transportation

UR - http://www.scopus.com/inward/record.url?scp=85077329267&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85077329267&partnerID=8YFLogxK

U2 - 10.1145/3356395.3365544

DO - 10.1145/3356395.3365544

M3 - Conference contribution

AN - SCOPUS:85077329267

T3 - Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019

SP - 25

EP - 28

BT - Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019

A2 - Kar, Bandana

A2 - Omitaomu, Olufemi A.

A2 - Ye, Xinyue

A2 - Mohebbi, Shima

A2 - Fu, Guangtao

PB - Association for Computing Machinery, Inc

Y2 - 5 November 2019

ER -

ADMSv2: A modern architecture for transportation data management and analysis

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Publisher link

Find It

Other files and links

Fingerprint

Cite this