We conduct our research together with multiple academic and industrial partners. As innovation often comes from synergy we are always looking for new collaborators and team members.
Our most recent projects:
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. A related capstone project of our students’ team on anomaly detection in temporal network could be found at Anomaly_detection_capstone |
Impact Of Ride-Sharing In New York City | ![]() 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. The project is conducted in collaboration with NYU C2SMART Center under support of US Department of Transportation and Arcadis.A project page could be accessed at impact-ride-sharing-nyc A related capstone project of our students’ team on assessing the impact of Manhattan Congestion Surcharge could be found at Manhattan_Congestion_Capstone_Website |
HITPACER (Hierhical Trajectory Partitioning and Clustering for Mining Recurrent Travel Behavior) | ![]() This ongoing collaborative project focuses on mining recurrent travel patterns at variable spatio-temporal scale from mobility data that includes three major components: a) Refinement of the trajectories with incomplete or noisy observations, b) qualitative symbolic representation of refined trajectories enabling significant data compression and computational costs reduction while preserving key original features, c) hierarchical trajectory partitioning with subtrajectory clustering revealing recurrent travel behavior at multiple spatial and temporal scales.The project is conducted in collaboration with Lockheed Martin under support of National Geospatial Intelligence Agency |
Impact of Urban Deployments | ![]() |
The Formulae Of Social Resilience | ![]() |
Disruptive synergy of NYC subway | ![]() |
Signature Of Urban Concerns | ![]() |