TRB Pubsindex
Text Size:
x

« Prev Next »

Return to Results | New Search

Bookmark and Share

Title:

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

Accession Number:

01628049

Record Type:

Component

Abstract:

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.

Monograph Accession #:

01618707

Report/Paper Numbers:

17-05941

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Casey, Gerard
Soga, Kenichi
Silva, Elisabete
Guthrie, Peter
Kumar, Krishna

Pagination:

18p

Publication Date:

2017

Conference:

Transportation Research Board 96th Annual Meeting

Location: Washington DC, United States
Date: 2017-1-8 to 2017-1-12
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References

Uncontrolled Terms:

Agent based models

Geographic Terms:

Greater London

Subject Areas:

Data and Information Technology; Highways

Source Data:

Transportation Research Board Annual Meeting 2017 Paper #17-05941

Files:

TRIS, TRB, ATRI

Created Date:

Dec 8 2016 12:23PM