activity
20192022
most citedUnderstanding and predicting travel time with spatio-temporal features of network traffic flow, weather and incidents

59 citations · 120 across the 10 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

eess.SP2019★ 4 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…

physics.soc-ph2019★ 1 cited

Measuring and reducing the disequilibrium levels of dynamic networks through ride-sourcing vehicle data

Wei Ma, Sean Qian

Transportation systems are being reshaped by ride-sourcing and shared mobility services in recent years. The transportation network companies (TNCs) have been collecting high-granu…

eess.SY2019★ 9 cited

Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphs

Wei Ma, Xidong Pi, Sean Qian

Transportation networks are unprecedentedly complex with heterogeneous vehicular flow. Conventionally, vehicle classes are considered by vehicle classifications (such as standard p…

stat.AP2019★ 59 cited

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.LG2019★ 9 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…