POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations

Authored by Bo Xu, Joshua Auld, Michael Hope, Hubert Ley, Vadim Sokolov, Kuilin Zhang

Date Published: 2016

DOI: 10.1016/j.trc.2015.07.017

Sponsors: No sponsors listed

Platforms: C++

Model Documentation: Other Narrative Flow charts

Model Code URLs: Model code not found

Abstract

This paper discusses the development of an agent-based modeling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database 10 library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typically done with separate stand-alone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a long standing issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Initial tests with case studies involving traffic management center impacts on various network events such as accidents show the potential of the system. (C) 2013 Elsevier Ltd. All rights reserved.

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Choice Real-time information Scheduling adapts model Management-systems