TITLE:

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

PRESENTER:

Shashi Shekhar : Biography , Homepage

AFFILIATION:

Computer Science Department, University of Minnesota.

URL:

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

SLIDES:

ABSTRACT:

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.

DETAILS: TAG models node/edge attributes as functions of time rather than fixed numbers. Thus node/edge capacities, node occupancies, etc. are modeled as time-series. Second, it iteratively considers all pairs of sources and destinations. In each iteration, it schedules evacuation of a group of evacuees across the closest source-destination pair. Special graphs construction is used eliminate redundant computation in this step. Non-stationary ranking of alternative routes during a evacuation is addressed by a linear-cost earliest-arrival-index on input TAG with travel-time-series. 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. In addition, linear programming approach needs an estimate of upper bound on total evacuation time to construct TEG representation by replicating transportation network for every time-instant during evacuation. Incorrect estimate of upper bound on total evacuation time may lead to either a failure to produce any solution or excessive computational costs. For smaller networks, where software based on linear programming can be used, CCRP produces high quality solutions with evacuation times comparable to those achieved by linear programming methods.

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 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)). GIS/LIS news published a brief coverage of the event.

NOTE 2: Following recent technical publications uncover details of the results discussed in this talk:
  1. A scientific approach to evacuation planning , The Sensor Newsletter, Volume 6, Number 3, Intelligent Transportation Systems Institute, University of Minnesota, Winter 2006. Also highlighted ( see this link ) by Office of the Vice President of Research at the University of Minnesota.
  2. Q. Lu, B. George, S. Shekhar, Capacity Constrained Routing Algorithms for Evacuation Planning: A Summary of Results , Proc. 9th Intl. Symposium on Spatial and Temporal Databases , 2005, isbn: 3-540-28127-4.
  3. 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 ieeexplore.ieee.org link . report Mn/Dot 2006-21 , Center for Transportation Studies, University of Minnesota. A summary of results appeared in Proc. ACMGIS 2005.
  4. 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).
  5. 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.
  6. 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.