Author name cluster

Zhaobin Mo

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAMAS Conference 2024 Conference Paper

PI-NeuGODE: Physics-Informed Graph Neural Ordinary Differential Equations for Spatiotemporal Trajectory Prediction

Zhaobin Mo
Yongjie Fu
Xuan Di

It is challenging to predict a group of individuals’ spatiotemporal trajectories in continuous time and space, due to various environmental and intrinsic factors. Especially, social dynamics such as driving or crowding behaviors could be hard to predict due to heterogeneous and complex mapping from high-dimensional inputs to an output driven by the decision-making processes of other agents. To tackle this challenge, neural ordinary differential equations (neural ODEs) have been developed to predict continuous-time longterm dynamics with constant memory cost and high computational efficiency. Furthermore, scientific communities have developed a rich set of physics models to describe how individuals interactively make decisions. With a rapidly growing trend of employing physics-informed deep learning (PIDL) for dynamical systems in science and engineering, its application to social dynamics is understudied. This paper aims to develop an integrated framework, named “PI-NeuGODE, ” that encodes physics models, complemented by symbolic regression, into neural ODEs. In the proposed model, physics informs the training of neural ODEs, while neural ODEs guide knowledge discovery. Symbolic regression is used to uncover physics knowledge from complex data. We further use graph neural networks to learn the topological interaction of individuals. The proposed method is tested on two applications, human driving and platooning, as well as crowding, which demonstrate the algorithmic accuracy and efficiency against baselines including existing social deep learning models.

PDF

AIIM Journal 2023 Journal Article

Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score

Xuan Di
Yiqiao Yin
Yongjie Fu
Zhaobin Mo
Shaw-Hwa Lo
Carolyn DiGuiseppi
David W. Eby
Linda Hill

Several recent studies indicate that atypical changes in driving behaviors appear to be early signs of mild cognitive impairment (MCI) and dementia. These studies, however, are limited by small sample sizes and short follow-up duration. This study aims to develop an interaction-based classification method building on a statistic named Influence Score (i. e. , I-score) for prediction of MCI and dementia using naturalistic driving data collected from the Longitudinal Research on Aging Drivers (LongROAD) project. Naturalistic driving trajectories were collected through in-vehicle recording devices for up to 44 months from 2977 participants who were cognitively intact at the time of enrollment. These data were further processed and aggregated to generate 31 time-series driving variables. Because of high dimensional time-series features for driving variables, we used I-score for variable selection. I-score is a measure to evaluate variables’ ability to predict and is proven to be effective in differentiating between noisy and predictive variables in big data. It is introduced here to select influential variable modules or groups that account for compound interactions among explanatory variables. It is explainable regarding to what extent variables and their interactions contribute to the predictiveness of a classifier. In addition, I-score boosts the performance of classifiers over imbalanced datasets due to its association with the F1 score. Using predictive variables selected by I-score, interaction-based residual blocks are constructed over top I-score modules to generate predictors and ensemble learning aggregates these predictors to boost the prediction of the overall classifier. Experiments using naturalistic driving data show that our proposed classification method achieves the best accuracy (96%) for predicting MCI and dementia, followed by random forest (93%) and logistic regression (88%). In terms of F1 score and AUC, our proposed classifier achieves 98% and 87%, respectively, followed by random forest (with an F1 score of 96% and an AUC of 79%) and logistic regression (with an F1 score of 92% and an AUC of 77%). The results indicate that incorporating I-score into machine learning algorithms could considerably improve the model performance for predicting MCI and dementia in older drivers. We also performed the feature importance analysis and found that the right to left turn ratio and the number of hard braking events are the most important driving variables to predict MCI and dementia.

Details DOI

AAAI Conference 2021 Conference Paper

Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models

Rongye Shi
Zhaobin Mo
Xuan Di

Traffic state estimation (TSE) reconstructs the traffic variables (e. g. , density or average velocity) on road segments using partially observed data, which is important for traffic managements. Traditional TSE approaches mainly bifurcate into two categories: model-driven and data-driven, and each of them has shortcomings. To mitigate these limitations, hybrid TSE methods, which combine both model-driven and data-driven, are becoming a promising solution. This paper introduces a hybrid framework, physics-informed deep learning (PIDL), to combine second-order traffic flow models and neural networks to solve the TSE problem. PIDL can encode traffic flow models into deep neural networks to regularize the learning process to achieve improved data efficiency and estimation accuracy. We focus on highway TSE with observed data from loop detectors and probe vehicles, using both density and average velocity as the traffic variables. With numerical examples, we show the use of PIDL to solve a popular second-order traffic flow model, i. e. , a Greenshields-based Aw-Rascle-Zhang (ARZ) model, and discover the model parameters. We then evaluate the PIDL-based TSE method using the Next Generation SIMulation (NGSIM) dataset. Experimental results demonstrate the proposed PIDL-based approach to outperform advanced baseline methods in terms of data efficiency and estimation accuracy.

PDF Details