Songhua Hu

Songhua Hu

CrowdSensing, Human Mobility, GeoAI, Network Science

Big-data-driven human mobility estimation

Traditional travel demand models heavily rely on travel surveys, which are costly, laborious, and suffer from small-sample and infrequent issues. My Ph.D. dissertation focuses on building a big-data-driven digital twin model for estimating and forecasting human mobility at both collective and individual levels using location data from over 100 million monthly active mobile devices in the US. Specifically, My work is intended to leverage emerging big data, such as mobile device location data, digital social structure, traffic sensor records, and mobility transaction data, to extract population-representative trip itineraries, estimate multi-modal Origin-Destination matrices, and analyze individual travel behaviors.

Journals

  1. Hu, Songhua, Chenfeng Xiong, Peng Chen, and Paul Schonfeld. “Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models”, Transportation Research Part A: Policy and Practice 174 (2023): 103743.
  2. Hu, Songhua, Chenfeng Xiong, Mofeng Yang, Hannah Younes, Weiyu Luo, and Lei Zhang. “A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic.” Transportation Research Part C: Emerging Technologies 124 (2021): 102955.
  3. Zhang, Lei, Aref Darzi, Sepehr Ghader, Michael L. Pack, Chenfeng Xiong, Mofeng Yang, Qianqian Sun, AliAkbar Kabiri, and Songhua Hu. “Interactive covid-19 mobility impact and social distancing analysis platform.” Transportation Research Record (2020): 03611981211043813.
  4. Chen, Peng, Songhua Hu*, Qing Shen, Hangfei Lin, and Chi Xie. “Estimating traffic volume for local streets with imbalanced data.” Transportation research record 2673, no. 3 (2019): 598-610.

Conferences

  1. Zhao, Guangchen, Chenfeng Xiong, Songhua Hu, Mofeng Yang, Aliakbar Kabiri, Aref Darzi, and Yixuan Pan. A novel measurement of job accessibility based on mobile device location data, Transportation Research Board 102th Annual Meeting (2023), Washington DC.
  2. Sun, Qianqian, Yixuan Pan, Weiyi Zhou, Aliakbar Kabiri, Mofeng Yang, Guangchen Zhao, Songhua Hu, Mohammad Ashoori, Saeed Saleh Namadi, and Aref Darzi. National truck travel demand estimation using GPS data, Transportation Research Board 102th Annual Meeting (2023), Washington DC.
  3. Yang, Mofeng, Weiyu Luo, Mohammad Ashoori, Jina Mahmoudi, Chenfeng Xiong, Jiawei Lu, Guangchen Zhao, Saeed Saleh Namadi, Songhua Hu, and Aliakbar Kabiri, A big-data driven framework to estimating vehicle volume based on mobile device location data, Transportation Research Board 102th Annual Meeting (2023), Washington DC.
  4. Jing, Yi, Songhua Hu, and Hangfei Lin. Estimating Traffic Volume with Limited Observations: A Combination of Sampling Expansion and Geographically Weighted Poisson Regression, Transportation Research Board 100th Annual Meeting (2021), Washington DC.