Projects

I am a strong advocate of open-source and reproducible research. My research projects are available on GitHub.

Granted Research Funding

  • City-Scale Traffic Data Imputation and Forecasting with Tensor Learning
    ○ Abstract: With recent advances in sensing technologies, large-scale and multidimensional urban traffic data are collected on a continuous basis from both traditional fixed traffic sensing systems (e.g., loop detectors and video cameras) and emerging crowdsourcing/floating sensing systems (e.g., GPS trajectory from taxis/buses and Google Waze). These data sets have provided us with unprecedented opportunities for sensing and understanding urban traffic dynamics and developing efficient and reliable smart transportation solutions. For example, forecasting the demand and states (e.g., speed and volume) of urban traffic is essential to a wide range of intelligent transportation system applications such as trip planning, travel time estimation, route planning, traffic signal control, to name just a few. However, there are two critical issues that undermine the use of these data sets in real-world applications: (1) the missing data and noisy nature make it difficult to get the true signal, and (2) it is computationally expensive to process large-scale data sets for online applications (e.g., traffic prediction). The goal of this project is to develop new framework to better model local consistencies in spatiotemporal traffic data, such as the sensor dependencies and temporal dependencies resulting from traffic flow dynamics. The scientific objectives are to: (1) develop nonconvex low-rank matrix/tensor completion models considering spatiotemporal dependencies/correlations (e.g., graph Laplacian [spatial] and time series [temporal]) and traffic domain knowledge (e.g., fundamental diagram, traffic equilibrium, and network flow conservation); (2) incorporate Gaussian process kernels and neural network structure.
    ○ Main contributors: Xinyu Chen (principal investigator), Nicolas Saunier (supervisor)
    ○ Funding from: Institute for Data Valorisation (IVADO)
    ○ Amount: $100,000

Open-Source Research Projects

  • transdim: Machine learning for transportation data imputation and prediction.
    ○ Question: Machine learning models make important developments in the field of spatiotemporal data modeling - like how to forecast near-future traffic states of road networks. But what happens when these models are built on incomplete data commonly collected from real-world systems (e.g., transportation system)?
    ○ Main contributors: Xinyu Chen, Jinming Yang, Yixian Chen, Nicolas Saunier, Lijun Sun
    ○ Time: September 23, 2018 - present
    ○ Impacts: 1,000+ stars & 280+ forks on GitHub.
    ○ Homepage: https://transdim.github.io

  • tracebase: Multivariate time series forecasting on high-dimensional and sparse Uber movement speed data.
    ○ About this project: Uber movement project provides data and tools for cities to more deeply understand and address urban transportation problems and challenges. Uber movement speed data measure hourly street speeds across a city (e.g., New York City, Seattle, and London) to enable data-driven city planning and decision making. These data are indeed multivariate time series with N road segments and T time steps (hours), and are featured as high-dimensional, sparse, and nonstationary. To overcome the challenge created by these complicated data behaviors, we propose a temporal matrix factorization framework for multivariate time series forecasting on high-dimensional and sparse Uber movement speed data.
    ○ Main contributors: Xinyu Chen
    ○ Time: November 29, 2020 - present
    ○ Impacts: 40+ stars on GitHub.

Open-Source Book/Tutorial Projects