Satish V Ukkusuri
Biographic Data
| ID | 1799191 |
|---|---|
| NAME | Satish V Ukkusuri |
| GIVEN NAMES | Satish V |
| FAMILY NAME | Ukkusuri |
| SIGNATURE | UKKUSURI S V |
| AFFILIATIONS | Purdue University West Lafayette |
| ORCID | 0000-0001-8754-9925 |
| VERIFIED | Yes |
| TOTAL WORKS | 27 |
| TOTAL CITATIONS | 7 |
| AUTHOR COUNT | 27 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Real-world charging data reveals spatial heterogeneity in electric vehicle charging station usage
Developing a framework for resilience assessment of electric vehicle charging station networks
Modeling hurricane evacuation/return under compound risks—Evidence from Hurricane Ida
Quantifying post-disaster community well-being
Household evacuation decision making during simultaneous events
Post-disaster recovery policy assessment of urban socio-physical systems
Mapping sidewalks on a neighborhood scale from street view images
Although reliable and accurate inventorying of sidewalks is time consuming, it can aid urban planners in decision making for infrastructure development. Recent advancements in computer vision and machine learning algorithms have improved the reliability and accuracy of automated inventorying. This research uses a deep learning architecture-based semantic segmentation model (i.e., HRNet + OCR) to automate sidewalk identification using Google Stree…
An agent-based model of post-disaster recovery in multilayer socio-physical networks
What determines travel time and distance decay in spatial interaction and accessibility
Effects of risk perception and perceived certainty on evacuate/stay decisions
Patterns of Social Support and Trajectories of Household Recovery after Superstorm Sandy
Understanding how vulnerability conditions are related to disruptions in social support and trajectories of recovery after disasters is important for promoting resilience. Based on household survey data from New Jersey counties impacted by Superstorm Sandy, a hierarchical clustering method was utilized to classify recovery trajectories as well as common patterns of social support reflecting contrasting dimensions of social capital over time. Resi…
Mobile phone location data for disasters
Early warning of Covid-19 hotspots using human mobility and web search query data
Spatiotemporal impacts of human activities and socio-demographics during the Covid-19 outbreak in the US
Through a mobility-augmented spatiotemporal modeling approach, we could quantify the time and space varying impacts of non-epidemiological factors on COVID-19 cases. The results suggest that the effects of population density, socio-demographic attributes, and travel-related attributes will differ significantly depending on the time of the pandemic and the underlying location. Moreover, policy restrictions on human contact are not universally effe…
Regional differences in resilience of social and physical systems
Physical infrastructure networks in diverse urban settlements are designed to be robust and reliable, while the socio-economic systems offer the necessary adaptive capacity at household, city, and regional scales to recover from major service disruptions resulting from disasters. Here, our urban resilience analyses are based on exploring explicit links between the physical infrastructure/assets and the socio-economic systems. Increased availabili…
Resilience of cities to external shocks
Resilience of Interdependent Urban Socio-Physical Systems using Large-Scale Mobility Data
Understanding Spatiotemporal Variations of Ridership by Multiple Taxi Services
Recent years have seen the big growth of app-based taxi services by not only competing for rides with street-hailing taxi services but also generating new taxi rides. Moreover, the innovation in dynamic pricing also makes it competitive in both passenger and driver sides. However, current literature still lacks better understandings of induced changes in spatiotemporal variations in multiple taxi ridership after app-based taxi service launch. Thi…
Impact of transportation network companies on urban congestion
Influencing factors and heterogeneity in ridership of traditional and app-based taxi systems
Joint modeling of evacuation departure and travel times in hurricanes
Joint inference of user community and interest patterns in social interaction networks
The role of social capital, personal networks, and emergency responders in post-disaster recovery and resilience
A statistical analysis of the dynamics of household hurricane-evacuation decisions
Analysis of social interaction network properties and growth on Twitter
Spatial variation of the urban taxi ridership using GPS data
Impacts of urban built environment on empty taxi trips using limited geolocation data
The role of social capital, personal networks, and emergency responders in post-disaster recovery and resilience
A statistical analysis of the dynamics of household hurricane-evacuation decisions
Analysis of social interaction network properties and growth on Twitter
Joint modeling of evacuation departure and travel times in hurricanes
Joint inference of user community and interest patterns in social interaction networks
Understanding Spatiotemporal Variations of Ridership by Multiple Taxi Services
Recent years have seen the big growth of app-based taxi services by not only competing for rides with street-hailing taxi services but also generating new taxi rides. Moreover, the innovation in dynamic pricing also makes it competitive in both passenger and driver sides. However, current literature still lacks better understandings of induced changes in spatiotemporal variations in multiple taxi ridership after app-based taxi service launch. Thi…
Impact of transportation network companies on urban congestion
Influencing factors and heterogeneity in ridership of traditional and app-based taxi systems
Regional differences in resilience of social and physical systems
Physical infrastructure networks in diverse urban settlements are designed to be robust and reliable, while the socio-economic systems offer the necessary adaptive capacity at household, city, and regional scales to recover from major service disruptions resulting from disasters. Here, our urban resilience analyses are based on exploring explicit links between the physical infrastructure/assets and the socio-economic systems. Increased availabili…
Resilience of cities to external shocks
Resilience of Interdependent Urban Socio-Physical Systems using Large-Scale Mobility Data
Effects of risk perception and perceived certainty on evacuate/stay decisions
Patterns of Social Support and Trajectories of Household Recovery after Superstorm Sandy
Understanding how vulnerability conditions are related to disruptions in social support and trajectories of recovery after disasters is important for promoting resilience. Based on household survey data from New Jersey counties impacted by Superstorm Sandy, a hierarchical clustering method was utilized to classify recovery trajectories as well as common patterns of social support reflecting contrasting dimensions of social capital over time. Resi…
Mobile phone location data for disasters
Early warning of Covid-19 hotspots using human mobility and web search query data
Spatiotemporal impacts of human activities and socio-demographics during the Covid-19 outbreak in the US
Through a mobility-augmented spatiotemporal modeling approach, we could quantify the time and space varying impacts of non-epidemiological factors on COVID-19 cases. The results suggest that the effects of population density, socio-demographic attributes, and travel-related attributes will differ significantly depending on the time of the pandemic and the underlying location. Moreover, policy restrictions on human contact are not universally effe…
Modeling hurricane evacuation/return under compound risks—Evidence from Hurricane Ida
Quantifying post-disaster community well-being
Household evacuation decision making during simultaneous events
Post-disaster recovery policy assessment of urban socio-physical systems
Mapping sidewalks on a neighborhood scale from street view images
Although reliable and accurate inventorying of sidewalks is time consuming, it can aid urban planners in decision making for infrastructure development. Recent advancements in computer vision and machine learning algorithms have improved the reliability and accuracy of automated inventorying. This research uses a deep learning architecture-based semantic segmentation model (i.e., HRNet + OCR) to automate sidewalk identification using Google Stree…
An agent-based model of post-disaster recovery in multilayer socio-physical networks
What determines travel time and distance decay in spatial interaction and accessibility
Computer Science (17 works) · Engineering (15 works) · Geography (14 works) · Transport engineering (11 works) · Disaster Management and Resilience (8 works) · Business (7 works) · Transportation Planning and Optimization (7 works) · Urban Transport and Accessibility (7 works) · Cartography (6 works) · Economics (6 works)