439 Papers
624 Citations
Xiao Qin is an academic researcher from University of Wisconsin–Milwaukee. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 23, co-authored 158 publications. Previous affiliations of Xiao Qin include University of Connecticut & KAIST.
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Papers
An extension of the theory of planned behavior to predict pedestrians' violating crossing behavior using structural equation modeling
TL;DR: The results showed that people had a negative attitude toward the behavior of violating road-crossing rules; they perceived social influences from their family and friends; and they believed that this kind of risky behavior would potentially harm them in a traffic accident.
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An Exploratory Shockwave Approach to Estimating Queue Length Using Probe Trajectories
TL;DR: An innovative approach for signalized intersection performance measurement using probe vehicle trajectory data, focusing on queue length estimation and a threshold-based critical point extraction algorithm, which has the potential to reduce the communication cost in future real-time probe data collection application.
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Cycle-by-cycle queue length estimation for signalized intersections using sampled trajectory data
TL;DR: This method is able to provide cycle-by-cycle queue length estimation for signalized intersections with sampled vehicle trajectories as the only input, and the results indicate that this trajectory-based approach is promising.
112
Dynamic Origin-Destination Demand Estimation with Multiday Link Traffic Counts for Planning Applications
TL;DR: A dynamic origin–destination demand estimation model for planning applications with real-time link counts from multiple days is presented and illustrates considerable benefits in analyzing the demand dynamics with multiday data.
92
Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data
Mohammad Razaur Rahman Shaon,Xiao Qin,Mohammadali Shirazi,Dominique Lord,Srinivas R. Geedipally +4 more
TL;DR: In this article, a combination of the NB-L and RPNB-L models is proposed to account for underlying heterogeneity and address excess over-dispersion in crash data.
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