activity
20192022
most citedA deep learning approach to real-time parking occupancy prediction in spatio-temporal networks incorporating multiple spatio-temporal data sources

9 citations · 23 across the 5 of their papers we have counts for

collaborators

6 papers

stat.AP2022

Statistical inference of travelers' route choice preferences with system-level data

Pablo Guarda, Sean Qian

Traditional network models encapsulate travel behavior among all origin-destination pairs based on a simplified and generic utility function. Typically, the utility function consis…

eess.SY20221 cited

Estimating probabilistic dynamic origin-destination demands using multi-day traffic data on computational graphs

Wei Ma, Sean Qian

System-level decision making in transportation needs to understand day-to-day variation of network flows, which calls for accurate modeling and estimation of probabilistic dynamic…

eess.SP20209 cited

Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction

Weiran Yao, Sean Qian

The main question to address in this paper is to recommend optimal signal timing plans in real time under incidents by incorporating domain knowledge developed with the traffic sig…

eess.SP20194 cited

High-Resolution Traffic Sensing with Autonomous Vehicles

Wei Ma, Sean Qian

The last decades have witnessed the breakthrough of autonomous vehicles (AVs), and the perception capabilities of AVs have been dramatically improved. Various sensors installed on…

stat.AP2019

Understanding and predicting travel time with spatio-temporal features of network traffic flow, weather and incidents

Shuguan Yang, Sean Qian

Travel time on a route varies substantially by time of day and from day to day. It is critical to understand to what extent this variation is correlated with various factors, such…

cs.LG20199 cited

A deep learning approach to real-time parking occupancy prediction in spatio-temporal networks incorporating multiple spatio-temporal data sources

Shuguan Yang, Wei Ma, Xidong Pi +1

A deep learning model is applied for predicting block-level parking occupancy in real time. The model leverages Graph-Convolutional Neural Networks (GCNN) to extract the spatial re…