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.
|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.
|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||Under support of Future Cities Catapult we performed assessment of economic, social and environmental impacts of urban technology deployments, such as CitiBike, UberPOOL, LinkNYC, Midtown-In-Motion etc. using multiple sources of big urban data on human mobility, commercial activity etc|
|The Formulae Of Social Resilience||We leveraged social media data for defining urban communities in NYC and quantified their susceptibility to possible malicious social influences. The formulae of social resilience has been discovered – a set of socio-economic and network characteristics making community more resilient/susceptible for such influences|
|Disruptive synergy of NYC subway||We quantify delays experienced by subway passengers arising from disruptive events,particularly from those that occur simultaneously. We determine if and to what extent the effect under such simultaneous scenarios is greater than it would be had the same disruptions occurred as separate events. The findings can be useful in supporting the planning decisions necessary to prepare for the harmful consequences arising from disruptions to complexurban networks.|
|Signature Of Urban Concerns||We leverage the structure of 311 service requests to build a unique signature of the local urban context, thus being able to serve as a low-cost ol for urban stakeholders. Considering examples of New York City, Boston and Chicago, we demonstrate how 311 Service Requests recorded and categorized by type in each neighborhood can be utilized to generate a meaningful classification of locations across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based classification of urban neighborhoods can present sufficient information to model various socioeconomic features. Finally, we show that these characteristics are capable of predicting future trends in comparative local real estate prices. We demonstrate 311 Service Requests data can be used to monitor and predict socioeconomic performance of urban neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions.|