Lei Lin

Recently, there has been increased interest in quantifying and modeling the impact of inclement weather on transportation system performance. One problem that the majority of research studies on the topic have faced was the great dependence on weather data merely from atmospheric weather stations, which lack information about road surface condition...

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two ar-chitectures of the GCNN-DDGF model are explored; GCNNreg-DDGF is a regular GCNN...

The advent of connected and autonomous vehicles (CAVs) will change driving behavior and travel environment, and provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicles (HDVs) to a fully CAV traffic environment, the road traffic will consist of a “mixed” traffic flow of...

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels....

Fast fashion is a well-established and timely business model in the fashion industry. The common characteristics of fast fashion include a short product life cycle and the need for quick production and distribution of highly trendy products. In order to support the fast fashion business model, fast fashion companies have to conduct quick prediction...

COVID-19 has been affecting every aspect of societal life including human mobility since December, 2019. In this paper, we study the impact of COVID-19 on human mobility patterns at the state level within the United States. From the temporal perspective, we find that the change of mobility patterns does not necessarily correlate with government pol...

Nowadays social media platforms such as Twitter provide a great opportunity to understand public opinion of climate change compared to traditional survey methods. In this paper, we constructed a massive climate change Twitter dataset and conducted comprehensive analysis using machine learning. By conducting topic modeling and natural language proce...

Accurate prediction of traffic status in real time is critical for advanced traffic management and travel navigation guidance. There are many attempts to predict short-term traffic flows using various deep learning algorithms. Most existing prediction models are only tested on spatiotemporal data assuming no missing data entries. However, this idea...

COVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is no...

In operating rooms, excessive cognitive stress can impede the performance of a surgeon, while low engagement can lead to unavoidable mistakes due to complacency. As a consequence, there is a strong desire in the surgical community to be able to monitor and quantify the cognitive stress of a surgeon while performing surgical procedures. Quantitative...

There are a large number of optimization problems in physical models where the relationships between model parameters and outputs are unknown or hard to track. These models are named as black-box models in general because they can only be viewed in terms of inputs and outputs, without knowledge of the internal workings. Optimizing the black-box mod...

Accurate vehicle trajectory prediction can benefit a variety of intelligent transportation system applications ranging from traffic simulations to driver assistance. The need for this ability is pronounced with the emergence of autonomous vehicles as they require the prediction of nearby vehicles’ trajectories to navigate safely and efficiently. Re...

COVID-19 has been affecting every social sector significantly, including human mobility and subsequently road traffic safety. In this study, we analyze the impact of the pandemic on traffic accidents safety using two cities, namely Los Angeles and New York City in the U.S., as examples. Specifically, we have analyzed traffic accidents associated wi...

COVID-19 has been affecting every aspect of societal life including human mobility since December, 2019. In this paper, we study the impact of COVID-19 on human mobility patterns at the state level within the United States. From the temporal perspective, we find that the change of mobility patterns does not necessarily correlate with government pol...

In power systems, having accurate device models is crucial for grid reliability, availability, and resiliency. Existing model calibration methods based on mathematical approaches often lead to multiple solutions due to the ill-posed nature of the problem, which would require further interventions from the field engineers in order to select the opti...

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations among stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored: GCNNreg-DDGF is a regular GCNN-DD...

We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data. To that end, we propose MCFlow, a deep framework for imputation that leverages normalizing flow generative models and Monte Carlo sampling. We address the causality dilemma that arises when training models with incomplete data...

Keywords: Cross-border e-commerce Multi-product newsvendor Logistics service capacity (LSC) allocation Third-party forwarding logistics (3PFL) Deep learning A B S T R A C T With the rise of "cross-border-e-commerce", the third-party-forwarding-logistics (3PFL) service becomes increasingly popular. Different from the traditional third-party-logistic...

Connected vehicles (CVs) can capture and transmit detailed data such as vehicle position and speed through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve safety and mobility of transportation systems, which can overburden storage and communication systems. To mitigate thi...

With billions of personal images being generated from social media and cameras of all sorts on a daily basis, security and privacy are unprecedentedly challenged. Although extensive attempts have been made, existing face image de-identification techniques are either insufficient in photo-reality or incapable of balancing privacy and usability quali...

Myotonia, which refers to delayed muscle relaxation after contraction, is the main symptom of myotonic dystrophy patients. We propose a hierarchical attention-based temporal convolutional network (HA-TCN) for myotonic dystrohpy diagnosis from handgrip time series data, and introduce mechanisms that enable model explainability. We compare the perfor...

The advent of connected autonomous vehicles provides opportunities for safer, smoother, and smarter transportation. However, broadcasting information to surrounding vehicles and infrastructures risks security and privacy. Moreover, control decisions relying on such information are vulnerable to malicious attacks. In this paper , we propose a cooper...

An input vector composed of various features plays an important role in short-term traffic forecasting. However, there is limited research on the optimal feature selection for an input vector for a certain forecasting task. To fill the gap, this paper proposes a cohesion-based heuristic feature selection method by analyzing the nature of forecastin...

Connected vehicles (CVs) can capture and transmit detailed data through vehicle-to-vehicle and vehicle-to-infrastructure communications, which bring new opportunities to improve the safety, mobility, and sustainability of transportation systems. However, the potential data explosion is likely to over-burden storage and communication systems. We des...

Recommendation systems that automatically generate personalized music playlists for users have attracted tremendous attention in recent years. Nowadays, most music recommendation systems rely on item-based or user-based collaborative filtering or content-based approaches. In this paper, we propose a novel mixture hidden Markov model (HMM) for music...

Vehicle-to-vehicle communications can change the driving behavior of drivers significantly by providing them rich information on downstream traffic flow conditions. This study seeks to model the varying car-following behaviors involving connected vehicles and human-driving vehicles in mixed traffic flow. A revised car-following model is developed u...

In this paper, we aim to quantify uncertainty in short-term traffic volume prediction by enhancing a hybrid machine learning model based on Particle Swarm Optimization (PSO) and Extreme Learning Machine (ELM) neural network. Different from the previous studies, the PSO-ELM models require no statistical inference nor distribution assumption of the m...

Connected vehicles (CVs) can capture and transmit detailed data such as vehicle position and speed through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data brings new opportunities to improve the safety, mobility, and sustainability of transportation systems. However, the potential data explosion is likely to o...

Connected vehicles (CVs) can capture and transmit detailed data like vehicle position, speed and so on through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve the safety, mobility, and sustainability of transportation systems. However, the potential data explosion likely w...

Border crossing delays between New York State and Southern Ontario cause problems like enormous economic loss and massive environmental pollutions. In this area, there are three border-crossing ports: Peace Bridge (PB), Rainbow Bridge (RB) and Lewiston-Queenston Bridge (LQ) at Niagara Frontier border. The goals of this paper are to figure out wheth...

Border crossing delays cause problems like huge economics loss and heavy environmental pollutions. To understand more about the nature of border crossing delay, this study applies a dictionary-based compression algorithm to process the historical Niagara Frontier border wait times data. It can identify the abnormal spatial-temporal patterns for bot...

With the development of and advances in smartphones and global positioning system (GPS) devices, travelers’ long-term travel behaviors are not impossible to obtain. This study investigates the pattern of individual travel behavior and its correlation with social-demographic features. For different social-demographic groups (e.g., full-time employee...

Traffic accident data are usually noisy, contain missing values, and heterogeneous. How to select the most important variables to improve real-time traffic accident risk prediction has become a concern of many recent studies. This paper proposes a novel variable selection method based on the Frequent Pattern tree (FP tree) algorithm. First, all the...

Short-term traffic volume prediction models have been extensively studied in the past few decades. However, most of the previous studies only focus on single-value prediction. Considering the uncertain and chaotic nature of the transportation system, an accurate and reliable prediction interval with upper and lower bounds may be better than a singl...

The duration of freeway traffic accidents duration is an important factor, which affects traffic congestion, environmental pollution, and secondary accidents. Among previous studies, the M5P algorithm has been shown to be an effective tool for predicting incident duration. M5P builds a tree-based model, like the traditional classification and regre...

This paper introduces an Android smartphone application called the Toronto Buffalo Border Wait Time (TBBW) app, designed to collect, share and predict waiting time at the three Niagara Frontier border crossings, namely the Lewiston-Queenston Bridge, the Rainbow Bridge, and the Peace Bridge. The innovative app offers the user three types of waiting...

The field of traffic accident analysis has long been dominated by traditional statistical analysis. With the recent advances in data collection, storage and archival methods, the size of accident datasets has grown significantly. This in turn has motivated research on applying data mining and complex network analysis algorithms, which are specifica...

The field of traffic accident analysis has long been dominated by traditional statistical analysis. With the recent advances in data collection, storage, and archival methods, the size of accident data sets has grown significantly. This result in turn has motivated research on applying data mining and complex network analysis algorithms to traffic...

Although several methods for short-term forecasting of traffic volume have recently been developed, the literature lacks studies that focus on how to choose the appropriate prediction method on the basis of the statistical characteristics of the data set. This study first diagnosed the predictability of four traffic volume data sets on the basis of...

This paper presents a forecasting method called k nearest neighbor based local linear wavelet neural network (kNN-LLWNN) for the on-line, short-term prediction of five-minute traffic volumes at westbound of Interstate 64 in Hampton Road in Virginia. The method is based on combining k nearest neighbor (k-NN), with local linear wavelet neural network...