Our lab received Masaryk University Award in Science and Humanities to implement a project "Digital City", building Urban Data Engine and Urban AI to support data-driven urban decision making and innovation in transportation, energy, healthcare and other areas. Over three years starting Jan, 1, 2022 It will allow us to build a branch of the lab in Brno, Czech Republic, adding more PhD students, postdocs and visiting scholars to our team. Exciting opportunity!
Presenting at The 23rd International Conference on Computational Science and Its Applications in Athens, Greece
The 23rd International Conference on Computational Science and Its Applications was held on July 3 - 6, 2023, in collaboration with the National Technical University of Athens and the University of the Aegean, Athens, Greece. Four of our papers were presented at the conference:
Mobility Networks as a Predictor of Socioeconomic Status in Urban Systems.
Prediction of Urban Population-Facilities Interactions with Graph Neural Network.
Urban Zoning Using Intraday Mobile Phone-Based Commuter Patterns in the City of Brno.
Comparative Analysis of Community Detection and Transformer-Based Approaches for Topic Clustering of Scientific Papers.
Our paper on Charging Demand for Commercial Electric Vehicle to be presented at 2019 IEEE Intelligent Transportation Systems Society Conference in New Zealand
Our paper together with colleagues from Purdue University Stationary Spatial Charging Demand Distribution for Commercial Electric Vehicles in Urban Area to be presented at 2019 IEEE Intelligent Transportation Systems Society Conference, Auckland, New Zealand
Starting a project "Impact Of Ride-Sharing In New York City"
This new collaborative project with NYU C2SMART Center just received support from US Department of Transportation and Arcadis!
The project will develop a citywide data-driven transportation simulation modeling framework for probabilistic assessment of the associated mode-shift and resulting environmental, social and economic impacts of ride-sharing solutions (e.g. UberPOOL, Lyft shared etc) on urban transportation system in New York City efficiently leveraging available partial transportation data. The impacts in question include: travel time cut for passengers, reduction of traffic, gas consumption/ emissions by type (CO, NOx, PM2.5), travel time/cost savings for passengers, increased earnings for Lyft and Uber drivers, jobs for for-hire-vehicle drivers. Once developed, the new framework is readily applicable to the predictive assessment of the impacts of many other transportation pricing and policy decisions.
Starting the project METS-R: Multi-modal Energy-optimal Trip Scheduling in Real-time for Transportation Hubs
This ongoing collaborative project focuses on development and evaluation of the real-time energy-efficient autonomous vehicle solutions to serve major transportation hubs of NYC (such as JFK, LaGuardia, Penn station). Our lab’s role includes anomaly detection in transportation demand data as well as implementing dynamic ride-sharing and routing solutions for shared autonomous mobility.
The project is conducted in collaboration with Purdue university under support of US Department Of Energy.
Our new approach for anomaly detection in temporal networks presented on NetSci’2019
