We develop data-driven methods for smart and resilient cities.

About Us

Our goal is to contribute to the new science of cities studying
dynamics and complexity of urban systems and building smart and resilient city applications.

Mobility Simulator

Our Focus

At UNMD Lab, we work on urban data science problems developing data-driven methods to understand dynamics in urban mobility and activity behavior, infrastructure networks, traffic congestion, emergency response, and land use patterns.

We are also studying infrastructure resilience in the delivery of critical urban services such as energy, water, transportation, and communication and potential impacts of a failure to these systems under extreme events.

Our latest paper

Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges

Read Here


Our Research

Urban Mobility & Activity Patterns

We are developing machine-learning algorithms for inferring mobility and activity patterns using emerging datasets such as mobile phones, social media (Foursquare, Twitter), subway smart card transactions, and taxicab GPS observations. These patterns provide deeper insights on urban human mobility and activity choices.

Urban Networks & Traffic Flows

We are investigating how emerging datasets such as social media check-in data, taxicab GPS observations, automatic number plate recognition can be used to understand and model urban traffic networks and congestion. We have analyzed datasets from London and New York City to model travel time of urban links.

Disaster Analytics & Evacuation

We are using social media data for understanding disaster response and recovery issues. Using social sensor data, our algorithms can identify incidents and assist in disaster resposne and recovery operations. We develop models to understand household evacuation behavior and simulate large-scale evacuation traffic to understand the emergent traffic pattern.

Fatured Projects

Social Media for Community Building

Funded by FDOT

Transit Smart Card Transaction

Collaborator: SUNY Buffalo

Social Media in Disaster

Collaborator: Purdue

Publications

For an updated list of our publications
please follow us at ResearchGate or Google Scholar

Journal Papers

  1. Rashidi, T. H., Abbasi, A., Maghrebi, M., Hasan, S., and Waller T. S. 2017. Exploring the Capacity of Social Media Data for Modelling Travel Behaviour: Opportunities and Challenges. Accepted in Transportation Research Part C.
  2. Hasan, S. and Ukkusuri, S. V. 2016 Understanding social influence in activity-location choice and life-style patterns using geo-location data from social media. Accepted in Frontiers in ICT.
  3. Ukkusuri, S. V., Hasan, S., Luong, B., Doan, K., Zhan, X., Murray-Tuite, and Yin, W. 2016. A-RESCUE: An Agent based Regional Evacuation Simulator Coupled with User Enriched Behavior. Accepted in Networks and Spatial Economics. [pdf]
  4. Hasan, S. and Foliente, G. 2015. Modeling infrastructure system interdependencies and socio-economic impacts of failure in extreme events: Emerging R&D challenges. Natural Hazards 78(3), 2143-2168. [pdf]
  5. Hasan, S. and Ukkusuri, S. V. 2015. Location contexts of user check-ins to model urban geo life-style patterns. PLOS One 10(5): e0124819. [pdf]
  6. Hasan, S. and Ukkusuri, S. V. 2014. Urban activity pattern classification using topic models from online geo-location data. Transportation Research Part C 44, 363-381. [pdf]
  7. Hasan, S., Schneider, C., Ukkusuri, S. V., and Gonzalez, M. 2013. Spatiotemporal patterns of urban human mobility. Journal of Statistical Physics 151, 304-318. [pdf]
  8. Zhan, X., Hasan, S., Ukkusuri, S. V., and Kamga, C. 2013. Urban link travel time estimation using large-scale taxi data with partial information. Transportation Research Part C 33, 37-49. [pdf]
  9. Collins, C., Hasan, S., and Ukkusuri, S. V. 2013. A novel transit rider satisfaction metric: Rider sentiments measured from online social media data. Journal of Public Transportation 16(2), 21-45. [pdf]
  10. Hasan, S., Mesa-Arango, R., and Ukkusuri, S. V. 2013. A random-parameter hazard-based model to understand household evacuation timing behavior. Transportation Research Part C 27, 108-116. [pdf]
  11. Aziz, H., Ukkusuri, S. V., and Hasan, S. 2013. Exploring the determinants of pedestrian-vehicle crash severity in New York City. Accident Analysis and Prevention 50, 1298-1309. [pdf]
  12. Mesa-Arango, R.,Hasan, S., Ukkusuri, S. V., and Murray-Tuite, P. 2013. Household-level model for hurricane evacuation destination type choice using Hurricane Ivan data. ASCE Natural Hazards Review 14(1), 11-20. [pdf]
  13. Hasan, S. and Ukkusuri, S. V. 2013. Social contagion process in informal warning networks to understand evacuation timing behavior. Journal of Public Health Management & Practice 19, S68-S69.
  14. Hasan, S., Mesa-Arango, R., Ukkusuri, S. V., and Murray-Tuite, P. 2012. Transferability of hurricane evacuation model: Joint model estimation combining multiple data sources. ASCE Journal of Transportation Engineering 138(5), 548-556. [pdf]
  15. Hasan, S. and Ukkusuri, S. V. 2011. A threshold model of social contagion process for evacuation decision making. Transportation Research Part B 45(10), 1590-1605. [pdf]
  16. Hasan, S., Ukkusuri, S. V., Gladwin. H., and Murray-Tuite, P. 2011. Behavioral model to understand household-level hurricane evacuation decision making. ASCE Journal of Transportation Engineering 137(5), 341-348. [pdf]
  17. Hasan, S., Choudhury, C., Ben-Akiva, M., and Emmonds, A. 2011. Modeling of travel time variations on urban links in London. Transportation Research Record 2260, 1-7. [pdf]
  18. Ukkusuri, S. V.,Hasan, S., and Aziz, H. 2011. Random parameter model used to explain effects of built-environment characteristics on pedestrian crash frequency. Transportation Research Record 2237, 98-106. [pdf]

Conference Papers

  1. Hasan, S., Tonmoy, F., El-Zein, A., and Foliente, G. Modelling infrastructure interdependency at a local scale: value, methodologies and challenges. In Proceedings of the 21st International Congress on Modelling and Simulation (MODSIM2015), Gold Coast, Australia, December 2015.
  2. Hasan, S., Foliente, G., and Higgins, A. Assessing the direct economic impacts of disruptions in transport networks. In Proceedings of the 21st International Congress on Modelling and Simulation (MODSIM2015), Gold Coast, Australia, December 2015.
  3. Hasan, S., and Foliente, G. Modeling the potential impacts of infrastructure system interdependencies and cascading failures: current status and research needs. In Proceedings of abstracts of the Climate Adaptation Future Challenges Conference, Gold Coast, Australia, October 2014.
  4. Hasan, S., Foliente, G., and Wang, X. Assessing economic impacts of disruptions in transport networks. In Proceedings of abstracts of the 3rd International Climate Change Adaptation Conference, Fortaleza Ceara, Brazil, May 2014.
  5. Hasan, S. and Ukkusuri, S. V. Developing strategies to manage transport infrastructures under extreme weather events: An agent-based simulation model. In Proceedings of abstracts of the 3rd International Climate Change Adaptation Conference, Fortaleza Ceara, Brazil, May 2014.
  6. Hasan, S., Zhan, X., and Ukkusuri, S. V. Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, Chicago, August 2013.
  7. Collins, C., Hasan, S., and Ukkusuri, S. V. A novel transit rider satisfaction metric: Riders sentiment measured from online social media. In Proceedings of the 91st Transportation Research Board Meeting, Washington D.C., January 2012.
  8. Ukkusuri, S. V., Hasan, S., and Aziz, A. A random-parameter model to explain the built environment effects of pedestrian accident frequency. In Proceedings of 90th Transportation Research Board Meeting, Washington D.C., January 2011.
  9. Hasan, S., Choudhury, C., Ben-Akiva, M., and Emmonds, A. Modeling travel time variations on urban links in London. In Proceedings of 90th Transportation Research Board Meeting, Washington D.C., January 2011.
  10. Hasan, S. and Ukkusuri, S. V. Threshold model of social contagion process on random networks: Application to evacuation decision making. In Proceedings of the 7th Triennial Symposium of Transportation Systems Analysis (TRISTAN VII), Tromso, Norway, June 2010.
  11. Hasan, S. and Hoque, M. S. A simplified travel demand modeling framework: In the context of a developing country city. In Proceedings of Sustainable Development Challenges of Transport in Cities of the Developing World (CODATU XIII), Ho Chi Minh city, Vietnam, 2008.
  12. Hasan, S. and Hoque, M. S. Developing a trip generation model for Dhaka City. 11th World Conference on Transport Research (WCTR), Berkeley, USA, 2007.

Meet The Team

Samiul Hasan

Lab Director

PhD, Purdue University

Kamol C. Roy

Graduate Research Assistant

BSc, Civil Engineering, BUET

Mehedi Hasnat

Graduate Research Assistant

BSc, Civil Engineering, BUET

Facts

Why do we need smart and resilient cities?
Cities are responsible for

of world’s surface
of world’s population
of global energy consumption
of CO2 emissions

What We Are Sharing

Please follow us
@UNMDLab @SamiulHasn

Get in Touch

We are always open to new ideas.
If you are interested to join our team or collaborate with us, please write to us.

Contact Info

Civil, Environmental, and Construction Engineering
12800 Pegasus Drive, Suite 211
Orlando, FL 32816
P: (407) 823-2480