Sensing Flooded Roads to Support Roadway Mobility during Flooding: A Web-Based Tool and Insights from Needs Assessment Interviews
Abstract
Reliable sensing of roadway conditions during flooding is a long-standing, challenging problem with societal importance for roadway safety. Tools that provide real-time data on road conditions during floods can facilitate safer mobility, reduce vehicle-related drownings, enhance flood response efficiency, and support emergency response decision-making. Following the tenets of user-centered design, such tools ideally should address the needs of diverse stakeholders involved in flood response. Currently, the existing literature lacks a thorough understanding of stakeholder needs to guide situational awareness tool development in the area of roadway mobility during flood events. This paper addresses this gap by studying the needs of stakeholders responsible for managing flood response in Houston. Semi structured one-on-one interviews were conducted with stakeholders from different Houston-based organizations responsible for managing and responding to flood hazard events in the downtown metropolitan area. Interview responses were systematically analyzed to identify (1) data needs for facilitating efficient and safe emergency response, (2) the most and least valuable information available during flooding, (3) communication and visualization strategies, (4) factors influencing stakeholder trust, and (5) factors influencing occupational stress during flood response. Finally, interview insights were used to develop a conceptual situational awareness framework and a prototype map-based tool that provides real-time road condition data during flood events. This study elucidates vital information for improving existing tools and providing preliminary guidance for future mobility-centric situational awareness tools that promote safer mobility and facilitate emergency response decision-making during flooding. Although the study focused on Houston, insights gained may be useful for comparable flood-prone regions.
Practical Applications
In developed countries, 40%–60% of flood fatalities are attributed to vehicle-related incidents. Flooded roads and lack of real-time road condition data pose safety risks to first responders and reduce emergency response efficiency. Understanding stakeholder needs and developing tools that address them are essential for improving the safety and efficiency of emergency response, especially considering a potential increase in flood risk to urban mobility due to climate change and other factors. Following the tenets of the user-centered design process, this study identified stakeholder needs, conceptualized a framework for sensing road conditions, and developed an open-source prototype tool in the context of flood response in Houston. Insights gained in this study can improve the efficacy of existing mobility-centric situational awareness tools and provide preliminary guidance for quick prototyping of new situational awareness tools. Furthermore, organizations can use the insights presented here to help reduce work-related stress among emergency response personnel, thereby improving emergency response efficiency and organizational resilience.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions (e.g., anonymized data).
Acknowledgments
The authors thank all participants of this study for their time and support; this study and the continuing research have benefited greatly from their insights. The authors gratefully acknowledge the support of this research by the National Science Foundation (NSF) Smart and Connected Communities (SCC) program (Award No. 1951821), and the NSF PIRE Coastal Flood Risk Reduction Program: Integrated, multi-scale approaches for understanding how to reduce vulnerability to damaging events (Award No. 1545837). The authors also thank Rice University SCC team members, specifically Dr. Philip Bedient, Dr. Devika Subramanian, Dr. Andrew Juan, and Allison Price, for their input throughout the project. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors.
References
Ahmad, K., K. Pogorelov, M. Riegler, O. Ostroukhova, P. Halvorsen, N. Conci, and R. Dahyot. 2019. “Automatic detection of passable roads after floods in remote sensed and social media data.” Signal Process. Image Commun. 74 (May): 110–118. https://doi.org/10.1016/j.image.2019.02.002.
Alam, F., F. Ofli, and M. Imran. 2018. “CrisisMMD: Multimodal twitter datasets from natural disasters.” In Proc., 12th Int. AAAI Conf. on Web and Social Media (ICWSM). Washington, DC: Association for the Advancement of Artificial Intelligence.
Argyle, E. M., J. J. Gourley, Z. L. Flamig, T. Hansen, and K. Manross. 2017. “Toward a user-centered design of a weather forecasting decision-support tool.” Bull. Am. Meteorol. Soc. 98 (2): 373–382. https://doi.org/10.1175/BAMS-D-16-0031.1.
Arshad, B., R. Ogie, J. Barthelemy, B. Pradhan, N. Verstaevel, and P. Perez. 2019. “Computer vision and IoT-based sensors in flood monitoring and mapping: A systematic review.” Sensors 19 (22): 5012. https://doi.org/10.3390/s19225012.
Blake, E., and D. Zelinsky. 2018. Tropical cyclone report Hurricane Harvey (AL092017) 17 August–1 September 2017. University Park, FL: National Hurricane Center.
Chang, N.-B., and D.-H. Guo. 2006. “Urban flash flood monitoring, mapping and forecasting via a tailored sensor network system.” In Proc., 2006 IEEE Int. Conf. on Networking, Sensing and Control, 757–761. New York: IEEE.
Dempsey, M., R. Dempsey, C. Edds, C. Phillips, and A. Contributors. 2017. “Flooded streets due to #Harvey (No longer updated).” Accessed August 4, 2022. https://www.google.com/maps/d/viewer?mid=1Nzjiw9FHdJPHgNJiVMSHWhBQQd0&hl=en.
Dey, K. C., A. Mishra, and M. Chowdhury. 2015. “Potential of intelligent transportation systems in mitigating adverse weather impacts on road mobility: A review.” IEEE Trans. Intell. Transp. Syst. 16 (3): 1107–1119. https://doi.org/10.1109/TITS.2014.2371455.
Fan, C., M. Esparza, J. Dargin, F. Wu, B. Oztekin, and A. Mostafavi. 2020a. “Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters.” Comput. Environ. Urban Syst. 83 (Aug): 101514. https://doi.org/10.1016/j.compenvurbsys.2020.101514.
Fan, C., F. Wu, and A. Mostafavi. 2020b. “A hybrid machine learning pipeline for automated mapping of events and locations from social media in disasters.” IEEE Access 8 (Aug): 10478–10490. https://doi.org/10.1109/ACCESS.2020.2965550.
Fang, Z., P. B. Bedient, and B. Buzcu-Guven. 2011. “Long-term performance of a flood alert system and upgrade to FAS3: A Houston, Texas, case study.” J. Hydrol. Eng. 16 (10): 818–828. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000374.
FEMA. 2017. “Historic disaster response to Hurricane Harvey in Texas.” Accessed July 3, 2022. https://www.fema.gov/press-release/20210318/historic-disaster-response-hurricane-harvey-texas.
Field, C. B., V. Barros, T. F. Stocker, and Q. Dahe. 2012. Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of the intergovernmental panel on climate change. Cambridge, UK: Cambridge University Press.
Fink, S. 2018. Lost in the storm. New York: The New York Times Company.
Google. 2022. “Driving directions, live traffic & road conditions updates.” Accessed March 18, 2022. https://www.waze.com/live-map/.
Google. 2023a. “Entity extraction.” Accessed March 1, 2023. https://developers.google.com/ml-kit/language/entity-extraction.
Google. 2023b. “Geocoding API overview.” Accessed March 1, 2023. https://developers.google.com/maps/documentation/geocoding/overview.
Gori, A., R. Blessing, A. Juan, S. Brody, and P. Bedient. 2019. “Characterizing urbanization impacts on floodplain through integrated land use, hydrologic, and hydraulic modeling.” J. Hydrol. 568 (Jun): 82–95. https://doi.org/10.1016/j.jhydrol.2018.10.053.
Gori, A., I. Gidaris, J. R. Elliott, J. Padgett, K. Loughran, P. Bedient, P. Panakkal, and A. Juan. 2020. “Accessibility and recovery assessment of Houston’s roadway network due to fluvial flooding during Hurricane Harvey.” Nat. Hazard. Rev. 21 (2): 04020005. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000355.
Gutierrez, G. B. 2019. “Improving the impact of digital volunteers in disaster response user centered design and policy approach.” Master’s thesis, Dept. of Mechanical Engineering, Massachusetts Institute of Technology.
Haddock, C., and S. Kanwar. 2021. “Infrastructure design manual.” Accessed January 3, 2022. https://parkusa.com/index.php/files/132/SW-Harris-County-COH/384/COH-Infrastructure-Design-Manual.pdf.
HCFCD (Harris County Flood Control District). 2022. “Harris County flood warning system.” Accessed June 29, 2022. https://www.harriscountyfws.org/.
He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep residual learning for image recognition.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition, 770–778. New York: IEEE.
He, X., D. Lu, D. Margolin, M. Wang, S. E. Idrissi, and Y.-R. Lin. 2017. “The signals and noise: Actionable information in improvised social media channels during a disaster.” In Proc., 2017 ACM on Web Science Conf., 33–42. New York: Association for Computing Machinery.
Houston TranStar. 2022. “Houston TranStar-traffic map.” Accessed March 27, 2022. https://traffic.houstontranstar.org/layers/.
Islam, M. A., T. Islam, M. A. Syrus, and N. Ahmed. 2014. “Implementation of flash flood monitoring system based on wireless sensor network in Bangladesh.” In Proc., 2014 Int. Conf. on Informatics, Electronics & Vision (ICIEV), 1–6. New York: IEEE.
Jin, F., W. Wang, L. Zhao, E. Dougherty, Y. Cao, C.-T. Lu, and N. Ramakrishnan. 2014. “Misinformation propagation in the age of Twitter.” Computer 47 (12): 90–94. https://doi.org/10.1109/MC.2014.361.
Jongman, B., P. J. Ward, and J. C. J. H. Aerts. 2012. “Global exposure to river and coastal flooding: Long term trends and changes.” Global Environ. Change 22 (4): 823–835. https://doi.org/10.1016/j.gloenvcha.2012.07.004.
Jonkman, S. N., M. Godfroy, A. Sebastian, and B. Kolen. 2018. “Brief communication: Loss of life due to Hurricane Harvey.” Nat. Hazards Earth Syst. Sci. 18 (4): 1073–1078. https://doi.org/10.5194/nhess-18-1073-2018.
Khamaj, A., Z. Kang, and E. Argyle. 2019. “Users’ perceptions of smartphone weather applications’ usability.” In Proc., Human Factors and Ergonomics Society Annual Meeting, 2216–2220. Washington, DC: Human Factors and Ergonomics Society. https://doi.org/10.1177/1071181319631098.
Loftis, D., D. Forrest, S. Katragadda, K. Spencer, T. Organski, C. Nguyen, and S. Rhee. 2018. Stormsense: A new integrated network of IOT water level sensors in the smart cities of Hampton roads, VA. Reston, VA: ASCE.
Lopez-Trujillo, D. 2003. Application of usability engineering method in the analysis, design and implementation of a graphical user interface for flash flood warning system. San Juan, Puerto Rico: Univ. of Puerto Rico.
Mapbox. 2022. “Maps, geocoding, and navigation APIs & SDKs | Mapbox.” Accessed August 26, 2022. https://www.mapbox.com/.
Matgen, P., S. Martinis, W. Wagner, V. Freeman, P. Zeil, and N. McCormick. 2020. Feasibility assessment of an automated, global, satellite-based flood-monitoring product for the Copernicus emergency management service. Luxembourg, Europe: Publications Office of the European Union.
McIntyre, N., and H. Needham. 2017. “Flood-map.” Accessed August 4, 2022. https://github.com/nickmcintyre/flood-map.
Ming, X., Q. Liang, X. Xia, D. Li, and H. J. Fowler. 2020. “Real-time flood forecasting based on a high-performance 2-D hydrodynamic model and numerical weather predictions.” Water Resour. Res. 56 (7): e2019WR025583. https://doi.org/10.1029/2019WR025583.
Mioc, D., et al. 2015. “Natural and man-made flood risk mapping and warning for socially vulnerable populations.” Int. J. Saf. Secur. Eng. 5 (3): 183–202. https://doi.org/10.2495/SAFE-V5-N3-183-202.
Morsy, M. M., J. L. Goodall, G. L. O’Neil, J. M. Sadler, D. Voce, G. Hassan, and C. Huxley. 2018. “A cloud-based flood warning system for forecasting impacts to transportation infrastructure systems.” Environ. Modell. Software 107 (5): 231–244. https://doi.org/10.1016/j.envsoft.2018.05.007.
Mosavi, A., P. Ozturk, and K.-W. Chau. 2018. “Flood prediction using machine learning models: Literature review.” Water 10 (11): 1536. https://doi.org/10.3390/w10111536.
Naulin, J.-P., O. Payrastre, and E. Gaume. 2013. “Spatially distributed flood forecasting in flash flood prone areas: Application to road network supervision in Southern France.” J. Hydrol. 486 (Jul): 88–99. https://doi.org/10.1016/j.jhydrol.2013.01.044.
NOAA (National Oceanic and Atmospheric Administration). 2023. “National Oceanic and Atmospheric Administration.” Accessed March 1, 2023. https://www.noaa.gov/.
Ongori, H., and J. E. Agolla. 2008. “Occupational stress in organizations and its effects on organizational performance.” J. Manage. Res. 8 (3): 123–135.
Opach, T., and J. K. Rød. 2013. “Cartographic visualization of vulnerability to natural hazards.” Cartographica: Int. J. Geogr. Inf. Geovisualization 48 (2): 113–125. https://doi.org/10.3138/carto.48.2.1840.
Panakkal, P. 2022. “Situational awareness frameworks for real-time sensing of flood impacts on road transportation networks.” Ph.D. thesis, Dept. of Civil and Environmental Engineering, Rice Univ.
Panakkal, P., A. Juan, M. Garcia, J. E. Padgett, and P. Bedient. 2019. “Towards enhanced response: Integration of a flood alert system with road infrastructure performance models.” In Proc., Structures Congress 2019: Buildings and Natural Disasters, 294–305. Reston, VA: ASCE.
Panakkal, P., A. Price, J. Padgett, and P. Bedient. 2022. “Opensafe mobility.” Accessed October 4, 2022. https://github.com/Pranavesh-Panakkal/OpenSafe-Mobility.
Perks, M. T., A. J. Russell, and A. R. G. Large. 2016. “Technical note: Advances in flash flood monitoring using unmanned aerial vehicles (UAVs).” Hydrol. Earth Syst. Sci. 20 (10): 4005–4015. https://doi.org/10.5194/hess-20-4005-2016.
Praharaj, S., F. T. Zahura, T. D. Chen, Y. Shen, L. Zeng, and J. L. Goodall. 2021. “Assessing trustworthiness of crowdsourced flood incident reports using Waze data: A Norfolk, Virginia case study.” Transp. Res. Rec. 2675 (12): 650–662. https://doi.org/10.1177/03611981211031212.
Qualtrics International. 2022. “Online survey software.” Accessed June 29, 2022. https://www.qualtrics.com/core-xm/survey-software/.
Retchless, D., W. Mobley, M. Davlasheridze, K. Atoba, A. D. Ross, and W. Highfield. 2021. “Mapping cross-scale economic impacts of storm surge events: Considerations for design and user testing.” J. Maps 17 (1): 123–135. https://doi.org/10.1080/17445647.2021.1940325.
Robinson, A. C., J. Chen, E. J. Lengerich, H. G. Meyer, and A. M. MacEachren. 2005. “Combining usability techniques to design geovisualization tools for epidemiology.” Cartogr. Geogr. Inf. Sci. 32 (4): 243–255. https://doi.org/10.1559/152304005775194700.
Sanh, V., L. Debut, J. Chaumond, and T. Wolf. 2019. “DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter.” Preprint, submitted October 2, 2019. http://arxiv.org/abs/1910.01108.
Sebastian, A., et al. 2017. Hurricane Harvey report: A fact-finding effort in the direct aftermath of Hurricane Harvey in the Greater Houston Region. Delft, Netherlands: Delft Univ.
Sipes, J. L., and M. K. Zeve. 2012. The Bayous of Houston. Dover, New Hampshire: Arcadia.
SJM Ventures. 2021. “Transcription panda.” Accessed June 29, 2022. https://www.transcriptionpanda.com/.
SSPEED Center. 2023. “FAS5 TMC-Rice flood alert system.” Accessed March 1, 2023. https://www.sspeed.rice.edu/flood-alert-systems.
Stephens, S. H., D. E. DeLorme, and S. C. Hagen. 2015. “Evaluating the utility and communicative effectiveness of an interactive sea-level rise viewer through stakeholder engagement.” J. Bus. Tech. Commun. 29 (3): 314–343. https://doi.org/10.1177/1050651915573963.
StormGeo. 2023. “Navigate tomorrow—Today.” Accessed March 1, 2023. https://www.stormgeo.com/.
TDEM (Texas Division of Emergency Management). 2023. “Texas Division of Emergency Management.” Accessed March 1, 2023. https://tdem.texas.gov/.
Texas DOT. 2022. “DriveTexas.” Accessed March 15, 2022. https://drivetexas.org/#/7/32.340/99.500?future=false.
Texas DOT. 2023. “DriveTexas API.” Accessed March 1, 2023. https://api.drivetexas.org/.
Thrun, S., W. Burgard, and D. Fox. 2006. “Probabilistic robotics (intelligent robotics and autonomous agents series).” In Intelligent robotics and autonomous agents. Cambridge, UK: The MIT Press.
Tsou, M.-H., and J. M. Curran. 2008. “User-centered design approaches for web mapping applications: A case study with USGS hydrological data in the United States.” In International perspectives on maps and the internet, 301–321. Berlin: Springer.
Twitter. 2022. “Twitter.” Accessed March 26, 2022. https://twitter.com/.
USGS. 2023. “USGS current water data for the nation.” Accessed March 1, 2023. https://waterdata.usgs.gov/nwis/rt.
Versini, P.-A., E. Gaume, and H. Andrieu. 2010. “Application of a distributed hydrological model to the design of a road inundation warning system for flash flood prone areas.” Nat. Hazards Earth Syst. Sci. 10 (4): 805–817. https://doi.org/10.5194/nhess-10-805-2010.
Wieland, M., and S. Martinis. 2019. “A modular processing chain for automated flood monitoring from multi-spectral satellite data.” Remote Sens. 11 (19): 2330. https://doi.org/10.3390/rs11192330.
Zahura, F. T., J. L. Goodall, J. M. Sadler, Y. Shen, M. M. Morsy, and M. Behl. 2020. “Training machine learning surrogate models from a high-fidelity physics-based model: Application for real-time street-scale flood prediction in an urban coastal community.” Water Resour. Res. 56 (10): e2019WR027038. https://doi.org/10.1029/2019WR027038.
Zhang, C., C. Fan, W. Yao, X. Hu, and A. Mostafavi. 2019. “Social media for intelligent public information and warning in disasters: An interdisciplinary review.” Int. J. Inf. Manage. 49 (Dec): 190–207. https://doi.org/10.1016/j.ijinfomgt.2019.04.004.
Zhang, W., G. Villarini, G. A. Vecchi, and J. A. Smith. 2018. “Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston.” Nature 563 (7731): 384–388. https://doi.org/10.1038/s41586-018-0676-z.
Zoom Video Communications. 2022. “Video conferencing, cloud phone, webinars, chat, virtual events: Zoom.” Accessed June 6, 2022. https://zoom.us/.
Information & Authors
Information
Published In
Natural Hazards Review
Volume 24 • Issue 4 • November 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Sep 2, 2022
Accepted: May 15, 2023
Published online: Jul 25, 2023
Published in print: Nov 1, 2023
Discussion open until: Dec 25, 2023
ASCE Technical Topics:
- Business management
- Decision making
- Design (by type)
- Disaster preparedness
- Disaster risk management
- Emergency management
- Engineering fundamentals
- Floods
- Highway and road conditions
- Highway and road management
- Highway transportation
- Hydraulic design
- Hydrologic data
- Hydrologic engineering
- Hydrology
- Infrastructure
- Measurement (by type)
- Practice and Profession
- Sensors and sensing
- Traffic engineering
- Traffic management
- Traffic safety
- Transportation engineering
- Water and water resources
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