ADMSv2: A Modern Architecture for Transportation Data Management and Analysis

By Chrysovalantis Anastasiou, Jianfa Lin, Chaoyang He, Yao-Yi Chiang, Cyrus Shahabi. 2019

13 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Transportation, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

  1. Murali Annavaram
  2. Chaoyang He
  3. Qiang Yang
  4. Yang Liu

In Transportation

  1. Peilin Zhao
  2. Murali Annavaram
  3. Ramesh Raskar
  4. Qiang Yang
  5. Praneeth Vepakomma
  6. Chaoyang He
  7. Yang Liu
  8. Yu Zheng

In Signal Processing

  1. S. Avestimehr
  2. Peilin Zhao
  3. Murali Annavaram
  4. Ramesh Raskar
  5. Li Shen
  6. Qiang Yang
  7. Abhishek Singh
  8. Praneeth Vepakomma

Citations in the corpus