01661409

Component

Walking and bicyling are active transportation modes that offer great potential to reduce carbon footprint from transportation sector as well as pave the way to healthy living. Promoting active transportation increases non-motorized vehicle miles traveled (VMT) which significantly contributes to the reduction of traffic congestion and greenhouse gas emissions from on-road vehicles. This research puts focus on the HPC implementation of the agent-based model for New York City home-to-work commute trips and describes the graph-based technique that has been applied for the execution and its applicability in the context of travel behavior modeling. The authors built a HPC-based ABM simulation framework to model travel related decisions at high spatial and temporal resolution,The HPC-based model uses a graph-partition based technique that can leverage the interaction structure of agents within a geographic proximity and can boost the simulation execution time. Further, the authors offer a flexible framework in terms of computing resources so that the ABM can be used with different levels of computing resources--from multi core workstations to HPC grid. The authors executed the model in Titan Cray XK7 supercomputer of the Oak Ridge Leadership Computing Facility.

This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.

18-05808

English

4p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Bicycling; Graph theory; Mathematical models; Mode choice; Nonmotorized transportation; Simulation; Travel behavior; Walking

Agent based models

New York (New York)

Pedestrians and Bicyclists; Planning and Forecasting

Transportation Research Board Annual Meeting 2018 Paper #18-05808

TRIS, TRB, ATRI

Jan 8 2018 11:29AM