GIS and Remote Sensing Support for Evacuation Analysis

Transcription

1 GIS and Remote Sensing Support for Evacuation Analysis Presented to GIS for Transportation Symposium Rapid City, South Dakota March 28-31, 2004 Demin Xiong Oak Ridge National Laboratory 2360 Cherahala Blvd NTRC, Room I-04, MS-6472 Knoxville, Tennessee

2 Presentation Outline 1. Introduction 2. Network data preparation 3. Daytime and nighttime population estimation 4. Evacuation analysis and simulation 5. Conclusion and discussions

3 1. Introduction Evacuation Analysis There are various kinds of evacuations. The evacuations that we are concerned with are those that are caused by major man-made or natural disasters in large geographic scales, such as nuclear power plan failures, forest fires, hurricanes, or terrorism. In order to reduce risks and enhance safety and security, evacuation planning has been implemented in many sensitive areas, e.g., nuclear power plan sites, chemical stockpiles, hurricane-prone costal areas. With the recent concerns of homeland security, an extended use of evacuation planning as a tool for safety and security is expected for places such as convergence of transportation infrastructures, population centers, and concentration of government facilities. Extensive analysis procedures must be followed in order to develop effective evacuation plans. This analysis may include at-risk population estimation, transportation vulnerability, and the impacts of potential hazards. Consequently mitigation and prevention activities take place and evacuation plans are developed for worst scenarios.

4 The Use of Remote Sensing and GIS Provide a basic information infrastructure to reference relevant data for evacuation analysis (data about administrative boundaries, terrains, water bodies, land use and cover, and so on). Identify locations of critical facilities (schools, hospitals, churches, shopping centers, government facilities, and emergency services). Prepare and analyze transportation networks (geographic location, connectivity, capacity, and traffic control characteristics). Estimate population distributions and changes (daytime, nighttime, and overtime). Provide spatial decision support and assessment. Deliver and dissimulate information to decision makers, responders, and general public.

5 Overall Approach Population Estimation Information Extraction from Image Sources Census Data & Travel Statistics Traffic Simulation and Analysis GIS Databases Data Instigation

6 2. Road Network Preparation Transportation Network and Traffic Control Information Network Geometry Connectivity Traffic lanes and speed limits Turning restrictions, traffic controls, and intersection characteristics Imagery GIS Databases Field Survey Data Integration Flow-enabled Analytical Network

7 With high resolution imagery, road network locations, connectivity, roadway attributes and intersection characteristics can be effectively identified.

8 Using this imagery, traffic flow directions, the presence of turning pockets, and the number of lanes can be mostly extracted.

9 One of the research topics we have been working with is automated road network extraction. The purpose is to provide an ability to rapidly acquire network information in large geographic areas.

10 Another research topic we are working with is automated network matching, which may provide a means to quickly match, merge, and integrate data from different sources.

11 3. Daytime and Nighttime Population Estimation Census Population Building Classification Nighttime Population Estimation Transportation Statistics Daytime Population Estimation

12 Building classification (or land use classification) is an essential step toward daytime and nighttime population estimation for the proposed approach.

13 With the building footprints, we are able to fuse the data with existing parcel records so that buildings can be classified into different types (e.g., residential, commercial, industrial, etc.).

14 Census population data are first utilized to correlate with the residential buildings, which forms the detailed nighttime population distribution estimation.

15 During the daytime, people go to work, school, shopping, and other activities, which represent a shift from residential locations to services and industrial facilities.

16 A comparison between daytime and nighttime population distributions.

17 Long term population changes can be tracked with images that are obtained in different times.

18 4. Evacuation Analysis and Simulation Network Imagery Trip Origins and Destinations Evacuation Simulation Result Analysis (Volume, Travel Time, and Delay)

19 The Sequoyah Nuclear Power Plant in Hamilton County along the Tennessee River was selected for the case study. The effort is a collaboration between ORNL, University of Tennessee and Hamilton County GIS Department. The current effort is mainly focused on a feasibility study.

20 The very first step is to convert the analytical network into a simulation network.

21 The next major step is to develop the O-D matrices as the simulation input. Population counts were first established at a very detailed level, then aggregated to Census block groups, and finally attached to network nodes.

22 Scenes of simulated traffic conditions on transportation network

23 5. Conclusion and Discussions Remote sensing and GIS technologies provide a major means to acquire and maintain information for evacuation analysis. Much of the information needed for evacuation analysis, particularly, transportation networks, and daytime and nighttime population distributions, can be effectively derived from remote sensing and GIS sources. The current research focuses mainly on a feasibility study. Additional verification and validation are required before we can move into the practical implementation phase. Timely provision of geographic data to decision makers, first responders, and nthe general republic is critical in an emergency. Very limited effort has bee directed on this issues. Some of the on-going activities, such as the geospatial one-stop, automated feature extraction, and automated data matching and integration, might provide useful solutions to address the data needs for evacuation problems, not only for evacuation planning, but also for real-time response to emergencies or disasters.

24 Contact Information: Demin Xiong Oak Ridge National Laboratory NTRC, Room I-04, MS Cherahala Blvd Knoxville, Tennessee