Urban Networks, Mobility, and Dynamics Lab

UNMD Lab @ UCF, Orlando, Florida

We analyze large-scale geo-location data

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 developing data-driven methods to understand and solve problems related to urban mobility and activity behavior, infrastructure networks, traffic congestion, and emergency response. 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 papers

Predicting Traffic Demand during Hurricane Evacuation Using Real-time Data from Transportation Systems and Social Media

Read Here

Towards Reducing the Number of Crashes during Hurricane Evacuation: Assessing the Potential Safety Impact of Adaptive Cruise Control Systems

Read Here

Assessing the Crash Risks of Evacuation: A Matched Case-control Approach Applied over Data Collected during Hurricane Irma

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 (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, taxicab GPS observations and traditional datasets such as probe vehicles, transportation sensros data can be fused to understand and model urban traffic networks and congestion. We have analyzed large-scale datasets from Orlando, New York City, and Beijing to model travel time of urban links.

Disaster Analytics & Resilience

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

Featured Projects

Transport Investment for Community Building

Sponsor: FDOT

Quantifying the impact of transportation infrastructures using social media analytics such as sentiment, polarity, and topic analysis of Twitter data.

Arterial Traffic Management

Sponsor: FDOT

Developing a framework known as SMART-Feed to improve the effectiveness of real-time traffic information sharing via social media.

Disaster Analytics

Sponsor: Southeast Transportation Center (UTC)

Developing data analytics approaches using Twitter data to predict hurricane evacuation behavior and evacuation traffic. Status: Complete.

Assessing Crash Risks of Evacuation Traffic

Sponsor: SAFER-SIM (UTC)

Analyzing crash and traffic sensor data from Hurricane Irma to assess risks of incidents and crashes due to evacuation. Status: Complete, see report here.

ORDER-CRISP

Sponsor: NSF

Collaborator: FIU, V Tech, WVU, GWU

Analyzing interdependent infrastructure failures in energy, water, transportation, and telecommunication sectors during recent hurricanes in Florida and Texas.

Transit Smart Card Transaction

Collaborator: SUNY Buffalo

Investigating how large-scale smart card transaction data from transit systems can be used to learn riders' mobility behavior.

Forecasting Intersection Traffic

Sponsor: FDOT District 5

Developed a hybrid machine learning framework to forecast intersection-level traffic using ATSPM data. Status: Complete, see report here.

Publications

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

Journal Papers

Roy, K. C., Hasan, S., Culotta, A. and Eluru, N. 2021. Predicting Traffic Demand during Hurricane Evacuation Using Real-time Data from Transportation Systems and Social Media. Transportation Research Part C: Emerging Technologies, 131, 103339. [preprint ]
Rahman, R., Hasan, S. and Zaki, M. H. 2021. Towards reducing the number of crashes during hurricane evacuation: Assessing the potential safety impact of adaptive cruise control systems. Transportation Research Part C: Emerging Technologies, 128, 103188. [preprint ]
Rahman, R., Bhowmik, T., Eluru, N., and Hasan, S. Assessing the crash risks of evacuation: A matched case-control approach applied over data collected during Hurricane Irma. Accident Analysis and Prevention, 159, 106260. [preprint ]
Roy, K. C. and Hasan, S. Modeling the dynamics of hurricane evacuation decisions from twitter data: An input output hidden Markov modeling approach. Transportation Research Part C: Emerging Technologies, 123, 102976. [preprint]
Rahman, R., Shabab, R. K., Roy, K. C., Zaki, M. H., and Hasan, S. Real-time Twitter data mining approach to infer user perception toward active mobility. Transportation Research Record. [preprint]
Rahman, R. and Hasan, S. 2021. Real-time signal queue length prediction using long short-term memory neural network. Neural Computing and Applications, 33, 3311–3324. [preprint]
Roy, K. C., Hasan, S., and Mozumder, P. 2020. A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data. Computer‐Aided Civil and Infrastructure Engineering, 35 (12), 1387– 1402. [preprint]
Roy, K. C., Hasan, S., Sadri, A. M., and Cebrian, M. 2020. Understanding the efficiency of social media based crisis communication during hurricane Sandy. International Journal of Information Management, 52, 102060. [preprint]
Sadri, A. M., Hasan, S., Ukkusuri, S. V., and Cebrian, M. 2020. Exploring network properties of social media interactions and activities during Hurricane Sandy. Transportation Research Interdisciplinary Perspectives, 6, 100143.
Yu, X., Ivey, C., Huang, Z., Gurram, S., Sivaraman, V., Shen, H., Eluru, N., Hasan, S., Henneman, L., Shi, G., Zhang, H., Yu, H., Zheng, J. 2020. Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data. Environment International, 141, 105772.
Yue, L., Abdel-Aty, M., Wu, Y., Hasan, S., and Zheng, O. 2020. Identifying pedestrian crash contributing factors using association analysis and their implications for development of active pedestrian safety system. Transportation Research Record, 2674(8), 861-874.
Tonmoy, F. N., Hasan, S., and Tomlinson, R., 2020. Increasing coastal disaster resilience using smart city frameworks: Current state, challenges, and opportunities. Frontiers in Water, 2, 3.
Roy, K. C., Cebrian, M., and Hasan, S. 2019. Quantifying human mobility resilience to extreme events using geo-located social media data. EPJ Data Science, 8(1), 18. [pdf]
Hasnat, M. M., Faghih-Imani, A., Eluru, N., and Hasan, S. 2019. Destination choice modeling using location-based social media data. Journal of Choice Modelling, 31, 22-34. [pdf]
Rahman, R., Roy, K. C., Abdel-Aty, M., and Hasan, S. 2019. Sharing Real-time Traffic Information with Travelers using Twitter: An Analysis of Effectiveness and Information Content. Frontiers in Built Environment, 5, 83.
Rahman, S., Abdel-Aty, M., Hasan, S., and Cai, Q. 2019. Applying machine learning approaches to analyze the vulnerable road-users’ crashes at statewide traffic analysis zones. Journal of Safety Research, 01585.
Yu, X., Stuart, A. L., Liu, Y., Ivey, C. E., Russell, A. G., Kan, H., Henneman, L. R.F., Sarnat, S. E., Hasan, S., Sadmani, A., Yang, X., and Yu, H. 2019. On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies. Environmental Pollution, 252, 924-930.
Sadri, A. M., Hasan, S., and Ukkusuri, S. V. 2019. Joint inference of user community and interest patterns in social interaction networks. Social Network Analysis and Mining, 9(1), 11.
Hasnat, M. M., and Hasan, S. 2018. Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data. Transportation Research Part C, 96, 38-54.
Sadri, A. M., Hasan, S., Ukkusuri, S. V., and Lopez, J. E. S. 2018. Analysis of social interaction network properties and growth on Twitter. Social Network Analysis and Mining , 8(1), 56.
Sadri, A. M., Hasan, S., Ukkusuri, S. V., and Cebrian, M. 2018. Crisis communication patterns in social media during hurricane Sandy. Transportation Research Record, 2672(1), 125–137.
Hasan, S., and Ukkusuri, S. V. 2018. Reconstructing activity location sequences from incomplete check-in data: A semi-Markov continuous-time Bayesian network model. IEEE Transactions on Intelligent Transportation Systems, 19(3), 687-698.
Hasan, S., Wang, X., Khoo, Y. B., and Foliente, G. 2017. Accessibility and socio-economic development of human settlements. PloS One, 12(6), e0179620.
Ebadi, N., Kang, J. E., and Hasan, S. 2017. Constructing activity–mobility trajectories of college students based on smart card transaction data. International Journal of Transportation Science and Technology, 6(4), 316-329.
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. Transportation Research Part C. 75, 197-211.
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. Frontiers in ICT. 3, 10
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]
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]
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]
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]
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]
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]
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]
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]
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]
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.
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.
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]
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]
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]
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]
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

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Hasan, S. and Hoque, M. S. Developing a trip generation model for Dhaka City. 11th World Conference on Transport Research (WCTR), Berkeley, USA, 2007.