TITLE:

Evacuation Route Planning: Novel Spatio-temporal Network Models and Algorithms

PRESENTER:

Shashi Shekhar : Biography , Homepage , Picture

AFFILIATION:

Computer Science Department, University of Minnesota.

URL:

http://www.cs.umn.edu/~shekhar

SLIDES:

ABSTRACT for general audience:

Evacuation planning is critical for important applications, e.g., emergency management, to evacuate affected populations to safety in the event of natural disasters, industrial accidents or terrorist attacks. Currently, evacuation plans are often hand-crafted using table-top exercises. These rely on participantsí knowledge of local transportation networks and populations as well skills to evaluate alternative routes by travel times, capacities and transportation chokepoints. These are time and labor intensive as well as expensive limiting the number of scenarios during planning and adjustments to unanticipated events during disaster repose. Computerized tools may help address these limitations. In addition, these may recommend novel routes not yet considered and assist with alternative strategies (e.g., contraflow, phased evacuation).

Traditional computerized methods for evacuation route (and schedule) planning are based on either linear programming paradigm or game-theoretic models (e.g., Wardrop equilibrium) of commuter traffic. These are effective for small scenarios, e.g., small office building, villages and small towns. However, these do not scale up to large (e.g., >50,000 nodes) transportation networks as they use either microscopic simulations or time-expanded networks requiring large amount of computer storage and incurring exorbitant computational costs. We describe a novel geo-spatial approach, namely Capacity Constrained Route Planning (CCRP) approach, to quickly identify feasible evacuation plans. This approach can provide an efficient decision support tool for emergency management officials to evaluate existing evacuation plans or to determine novel plausible evacuation plans for large-scale evacuation scenario.

We also discuss case-studies for scenarios relating to Nuclear Power Plant, large metropolitan areas and large gatherings. First case study with 10-mile radius evacuation zone around Monticello nuclear power plant near Minneapolis/St. Paul Twin Cities metropolitan area showed that computerized methods lowered evacuation time by almost 30% relative to existing hand-crafted plans by relieving transportation bottleneck chokepoints and by choosing shorter routes. Second case study with numerous homeland security scenarios in Minneapolis/St. Paul metropolitan area spanning hundreds of square mile and over 2 Million people studied effects of incident locations, shelter choices, transportation modes (driving, public transportation, walking), incident locations, incident time of the day, transportation networks etc. It helped identify areas which are difficult to evacuation and may need enrichment of transportation networks. It also showed that walking able-bodied the first mile often speeded up evacuation significantly. A third case study in context of Minnesota state fair, a gathering of 100,000 to 125,000 people, reconfirmed the value of walking able-bodied about a mile in speeding up evacuation. Another case study is in progress with Hajj scenarios near Mecca, Saudi Arabia.

ABSTRACT for Computer Science Audience:

Efficient tools are needed to identify routes and schedules to evacuate affected populations to safety in face of natural disasters or terrorist attacks. Challenges arise due to violation of key assumptions (e.g. stationary ranking of alternative routes, Wardrop equilibrium) behind popular shortest path algorithms (e.g. Dijktra's, A*) and microscopic traffic simulators (e.g. DYNASMART). Time-expanded graphs (TEG) based mathematical programming paradigm does not scale up to large urban scenarios due to excessive duplication of transportation network across time-points. We present a new approach, namely Capacity Constrained Route Planner (CCRP), advancing ideas such as Time-Aggregated Graph (TAG) and an ATST function to provide earliest-Arrival-Time given any Start-Time. Laboratory experiments and field use in Twincities for DHS scenarios (e.g. Nuclear power plant, terrorism) show that CCRP is much faster than the state of the art. A key Transportation Science insight suggests that walking the first mile, when appropriate, may speed-up evacuation by a factor of 2 to 3 for many scenarios. Geographic Information Science (e.g. Time Geography) contributions include a novel representation (e.g. TAG) for spatio-temporal networks. Computer Science contributions include graph theory limitations (e.g. non-stationary ranking of routes, non-FIFO behavior) and scalable algorithms for traditional routing problems in time-varying networks, as well as new problems such as identifying the best start-time (for a given arrival-time deadline) to minimize travel-time.

Experiments with real and synthetic transportation networks show that the proposed approach scales up to much larger networks, where software based on linear programming method crashes. Evaluation of our methods for evacuation planning for a disaster at the Monticello nuclear power plant near Minneapolis/St. Paul Twin Cities metropolitan area shows that the new methods lowered evacuation time relative to existing plans by identifying and removing bottlenecks, by providing higher capacities near the destination and by choosing shorter routes. In 2005, CCRP was used for evacuation planning (transportation component) for the Minneapolis-St. Paul twin-cities metropolitan area. It facilitated explorations of scenarios (e.g. alternative locations and times) as well as options (e.g. alternative transportation modes of pedestrian and vehicle). It also led to an interesting discovery that walking able-bodied evacuees (instead of letting them drive) reduces evacuation time significantly for small area (e.g. 1-mile radius) evacuations.

In future work, we plan to formally characterize the quality of solutions identified by the CCRP approach. We will explore new ideas, e.g. phased evacuations and contra-flow, to further reduce evacuation times. In addition, we would like to improve modeling of other transportation modes such as public transportation.

KEYWORDS: Evacuation, Routing, Shortest path, Capacity constraints, Emergency planning, Homeland defense, Intelligent Transportation Systems.
NOTE 1: Following recent technical publications uncover details of the results discussed in this talk:

  1. Intelligent Shelter Allotment for Emergency Evacuation Planning: A Case Study of Makkah, (pdf at publisher and at local server ), Intelligent Systems, IEEE, 30(5):66-76, September-October, 2015. (doi: 10.1109/MIS.2015.39).
  2. S. Shekhar, K. S. Yang, V. Gunturi, L. Manikonda, D. Oliver, X. Zhou, B. George, S. Kim, J. Wolff, Q. S. Lu, Experiences with evacuation route planning algorithms (pdf at publisher and at local server ), International Journal of Geographical Information Science, 26(12), pp: 2253-2265, Taylor and Francis, December 2012.
  3. X. Zhou, B. George, S. Kim, J. Wolff, Q. Lu, S. Shekhar, Evacuation Planning: A Spatial Network Database Approach. , IEEE Data Eng. Bulletin, 33(2): 26-31 (2010).
  4. K. Yang, F. Ur Rehman, H. Lahza, S. Basalamah, S. Shekhar, I. Ahmed, and A. Ghafoor, Intelligent Shelter Allotment for Emergency Evacuation Planning: A Case Study of Makkah , Technical Report No. P1104-T1, Center of Research Excellence in Hajj and Omrah (Hajjcore), Umm Al-Qura University, Makkah, Saudi Arabia, Oct. 2012.
  5. Q. Lu, B. George, S. Shekhar, Capacity Constrained Routing Algorithms for Evacuation Planning: A Summary of Results ( local pdf , SpringerLink page ), Proc. 9th Intl. Symposium on Spatial and Temporal Databases, 2005, Springer LNCS 3633 , isbn: 3-540-28127-4. (Full paper titled Evacuation route planning: a case study in semantic computing appeared in Int. J. Semantic Computing, vol. 1, no. 2, pp. 249\226303, 2007.)
  6. S. Kim, S. Shekhar, M. Min, Contraflow Transportation Network Reconfiguration for Evacuation Route Planning, IEEE Transactions on Knowledge and Data Eng., 20(8): 1115-1129, 2008 ( local pdf, ieeexplore.ieee.org link). It is also detailed in a related Mn/Dot report 2006-21 from Center for Transportation Studies, University of Minnesota. A summary of results appeared in Proc. ACMGIS 2005.
  7. S. Kim, B. George, and S. Shekhar, Evacuation Route Planning: Scalable Heuristics , Proceedings of the 15th annual ACM International Symposium on Advances in Geographic Information Systems, 2007.
  8. B. George, S. Shekhar, and S. Kim, Spatio-temporal Network Databases and Routing Algorithms, University of Minnesota - CSE TR 08-039, 2008. (A summary of results appeared in 2007 Symposium on Spatial and Temporal Databases).
  9. S. Shekhar, Q. Lu, S. Kim, A Novel Approch to Evacuation Route Planning, in Army AHPCRC Research Center Bulletin, 15(4), 2005.
  10. Q. Lu, S. Shekhar, Capacity Constrained Routing for Evacuation Planning, in Proceeding of Intelligent Transportation Systems Safety and Security Conference, Miami, Florida, March 24-25, 2004.
  11. Q. Lu, Y. Huang and S. Shekhar, Evacuation Planning: A Capacity Constrained Routing Approach , in Proceeding of the 1st NSF/NIJ Symposium on Intelligence and Security Informatics, Tucson, Arizona, June 2-3, 2003.


NOTE 2: Following general interest publications highlight some of the results discussed in this talk:
  1. Evacuation project wins award , The CTS Report, Center for Transportation Systems, University of Minnesota, May 2006.
  2. News media coverage:
  3. S. Shekhar, and Q. Lu, Evacuation Planning for Homeland Security , Homeland Security Emergency Management Metro Regions Newsletter, Volume 18, October 2004, Minnesota Public Safety.
  4. S. Shekhar, Evacuation Planning for Homeland Defense, an invited presentation at the UCGIS Congressional Breakfast on Homeland Security and GIS, , February 2004 ( abstract (html), slides (ppt)). CTS Report published a brief coverage of the event. Directions Magazine also published an article describing this event.
  5. Efficient Evacuation Route Planning and Emergency Management , Office of Technology Commercialization, University of Minnesota.