A Scalable Agent Based Multi-modal Modeling Framework Using Real-Time Big-Data Sources for Cities

This paper presents a framework for using real-time big-data to inform a transport Agent Based Model (ABM) for a range of scenario testing applications. Computational advances have enabled for increasingly complex, bottom-up, fine resolution simulations to be carried out over long time horizons at fine spatial and temporal resolution. This has hinted at the possibility of connecting scales of what has been historically been fine resolution operational models and coarse resolution strategic models. The value of any fine resolution dynamic model is limited by the quality of its inputs. The wave of new geospatially connected devices has enabled the harvesting of fine resolution spatial and temporal data on travellers’ and even the infrastructure itself. This crowd-sourced data can be used to inform dynamic models with real-world and real-time data, bypassing the need for generalised functions and/or expensive survey data. In this paper, Google Directions API data and Transport for London data feeds are presented in a framework for London. The use of decentralised data structures is also presented and comment is made on the possibilities of using parallel computing advances in Computer Science to scaling up fine resolution scenario testing transportation models and enabling support for a range of agent decision making methodologies. Such data structures offer performance improvements in the storing of dynamic data that may be manipulated in order to simulate local and global hard infrastructure scenarios alone or in tandem with traditional policy or dynamic policy making scenarios.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Casey, Gerard
  • Soga, Kenichi
  • Silva, Elisabete
  • Guthrie, Peter
  • Kumar, Krishna
• Conference:
  • Transportation Research Board 96th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2017-1-8 to 2017-1-12
• Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: 18p
• Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Intelligent agents; Methodology; Parallel processing; Real time information; Traffic data; Traffic models
• Uncontrolled Terms: Agent based models
• Geographic Terms: Greater London
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01628049
• Record Type: Publication
• Report/Paper Numbers: 17-05941
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2017 4:08PM