@inproceedings{996e873c0039474eaf73af3cae00d949,
title = "Realistic Transport Simulation with Open Data",
abstract = "This poster aims to conduct a transportation simulation on MATSim, the state-of-the-art open source software for agent-based transportation simulation, for the region of Birmingham, AL, where a synthetic population is generated from a survey of 451 people with their day-plans of traveling. To tackle the small data problem, we seek the assistance of abundant open data such as US Census data, OpenStreetMap, OpenAddresses and Birmingham Business Alliance to complete the fine details realistically. We also utilize data science and machine learning techniques as well as iterative proportional fitting to build models that utilize these open data to generate a realistic population. Good accuracy of the simulation is achieved; see https://youtu.be/ZIm0WsmKB4E for a demo.",
author = "Guimu Guo and Khalil, {Jalal Majed} and Da Yan and Virginia Sisiopiku",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",
year = "2019",
month = dec,
doi = "10.1109/BigData47090.2019.9006291",
language = "English (US)",
series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6066--6068",
editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",
booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
address = "United States",
}