Trip Distribution Model for Flood Disaster Evacuation Operation

Transcription

1 Trip Distribution Model for Flood Disaster Evacuation Operation The devastating consequences of disasters in both developed and developing countries indicate significant lack or absence of disaster management systems. The importance of applying an efficient disaster management system lies in minimizing or avoiding the damaging consequences of such disasters. Therefore, an applicable trip distribution model for evacuation operations before Introduction The devastating consequences of disasters in both developed and developing countries point to a significant lack or complete absence of disaster management systems. No country is immune to natural disasters and their consequences. The World Bank states that the March 11, 2012, Japan earthquake caused 14,135 deaths and 11,601 missing, compared with the 1995 Kobe earthquake death toll of 6, The World Bank estimates the total damage cost of the November 2010 Pakistan floods at US$10 billion. 2 The official records of the United Nations indicate that the Indian Ocean earthquake/tsunami was one of the biggest catastrophes in history, causing almost 230,000 deaths. 3 The Australian Emergency Management database indicates that the flood disaster that struck Canberra in 1971 caused 7 deaths and 15 injuries, 4 but a 2010 Australian Capital Territory Planning and Land Authority (ACTPAL) study refers to an increase in the intensity of heavy rainfall of one day s duration, and its flood modeling does not take climate change into consideration. 5 and during a flood disaster has Importance of Efficient Disaster Management Systems been developed. Implementing efficient disaster management systems to minimize the catastrophic consequences of disasters such as floods is a matter of life and death. The lack of a disaster management system hampers the efforts of rescue and relief workers and makes it more difficult to cope with the consequences of a disaster. Well-planned evacuation operations BY AHMED T. EL-SERGANY AND SAMEER ALAM, PH.D. and rescue and relief routes before and during the disaster, play a significant role in saving lives and minimizing casualties, but most transportation planning considers transport and traffic during peak periods on normal workdays and on special occasions. It is hard to find transportation plans for disaster and emergency situations. Efficient disaster management systems minimize or avoid the devastating consequences of a disaster; improve evacuation, rescue, and relief efforts; provide important spatial information to the rescue and relief workers; avoid disaster-related epidemics; and make it possible to apply mitigation measures at the right times and in the right places. Approach The main objective of this paper is to provide a systematic method to minimize the impact of flood disasters on lives, property, and the environment. Developing a framework for an efficient flood disaster management system is a milestone for developing an applicable model of evacuation operations. This paper contributes toward developing an efficient flood disaster management system and a transport distribution model of evacuation operations for the affected population. Methodology The core of this paper lies in developing an applicable model for evacuation operations before and during flood disasters. In order to formulate this model, the following steps are required: system framework that illustrates the components of such a system; transportation planning and disaster management system planning; operation trip distribution matrix model; and Flood Disaster Management System Framework In 2011 the National strategy for disaster resilience was launched, which recognises that a national, coordinated, and cooperative effort is needed to enhance Australia s capacity to withstand and recover from emergencies 42 ITE JOURNAL / OCTOBER 2012

2 and disasters. Disaster resilience is the collective responsibility of all sectors of society, including all levels of government, business, the non-government sector, and individuals. If all these sectors work together with a united focus and a shared sense of responsibility to improve disaster resilience, they will be far more effective than the individual efforts of any one sector. 6 This paper identifies a disaster management system in three phases (before, during, and after disaster) in terms of the related management system activities and associated specific geographic information system (GIS) and planned applications. Disaster Management System Components 7 Emergency/Disaster Planning: Activities necessary to analyze the possibility of an emergency or disaster, and the potential consequences Mitigation: Activities to eliminate or reduce the probability of a disaster Preparedness: Activities necessary to the extent that mitigation measures have not prevented, or cannot prevent, disasters Response: Activities to provide emergency assistance for victims, seek to stabilize the situation, reduce the probability of secondary damage, and speed recovery operations Recovery: Activities necessary to return all systems to normal or better Disaster Management System Framework A disaster management system framework has been developed to outline the causal relationship among the disaster phases, the activities of the disaster management system, and the related GIS-based applications, as shown in Figure 1. First Phase (Predisaster) The first phase of a disaster management system concerns planning, mitigation, and preparedness activities that concentrate on the following: quences; sources; early warning system locations; response plans; evacuation, Figure 1. Flood disaster management system framework. rescue, and relief routes; and relocation plans; and countermeasures to avoid disasters or minimize their impacts; and determining potential disaster areas, disaster simulation and consequence models, low accessible areas. Second Phase (During Disaster) The second phase of a disaster management system concerns response, real-time monitoring, and damage assessment activities. Activities in this phase are meant to apply the response plans; launch rescue, evacuation, and relief efforts; monitor the disaster; and conduct preliminary damage assessment in terms of lives, property, environment, and infrastructure. The main purpose of this phase is to coordinate, update, and improve the evacuation, relocation facilities, and response and relief plans to cope with the disaster s consequences. Third Phase (Postdisaster) The third phase of a disaster management system concerns short-term and longterm recovery activities. Short-term activities are meant to complete rescue and relief efforts and restore vital life-support systems. Long-term activities are meant to return life to normal or improved levels. Transportation Planning Traditionally, planning for transportation systems has focused on creating capacity and building future infrastructure to meet a predicted or forecasted travel demand. In the traditional model, transportation planning focuses on infrastructure projects, relying on an analysis of long-range travel demand, transportation goals, and funding constraints but often with limited consideration of shortterm and on-going operational issues. 8 In a disaster situation, transportation needs are completely different from those during normal situations. In such situations, the purpose of travel is emergency movement, such as evacuation from affected areas, rescue operations, and relief efforts. The spontaneous movement of people during a disaster creates a greater concentration of travel demand, and consequently, widespread, severe congestion on the entire road network. 9 Transportation Planning for Flood Disasters The most appropriate way to deal with transportation issues during a disaster is to plan for operations techniques that strengthen the relationship among planners, decision-makers, managers, and operators by providing better coordination and collaboration. ITE JOURNAL / OCTOBER

3 Canberra Travel Time Matrix Linear Programming Simplex Model (LPS) Comparing the Results of LPS and GRG Models: Minimized Total Travel Time Statistical Analysis Visual and Geographical Analysis Figure 2. Trip distribution matrix model flow chart. Transportation Problem Model The transportation problem model was proposed by Hitchcock in 1941 and has an important role in logistics and supply chain management for reducing cost and improving service. It deals with situations in which a commodity is shipped from source to destination. 10 Trip Distribution Matrix Model The transportation problem model is used for planning the evacuation operations to estimate the trip distribution matrix, reflecting the purpose of emergency movement. Figure 2 shows the trip distribution Flood Affected Zones Extracting Travel Time Matrix matrix model flow chart, which describes the process sequences of the proposed model. To formulate the variables in a transportation problem model, the following steps are required: Objective Function Minimize the travel time for transporting the directly affected population from affected areas to relocation shelters as follows: Relocation Shelter Zones Generalized Reduced Gradient Model (GRG) Table 1. Assumed travel time matrix (minutes). RS1 RS2 RS3 RS4 RS5 RS6 RS7 RS8 RS9 RS10 Population AP AP AP AP AP AP AP AP AP AP AP AP Capacity Note: AP = affected population; RS = relocation shelter Where: TP = Total travel time of affected population I = Affected areas J = Relocation shelters TT ij = Travel time from affected area i to relocation shelter j AP ij = Affected population from affected area i in relocation shelter j RS ij = Relocation shelter occupation from affected area i in relocation shelter j Constraints: Affected Population Area Constraint The population transported from affected area i to relocation shelter j is less than or equal to the population in affected area i Where: AP ij = Affected population from affected area i in relocation shelter j AP i = Affected population in affected area i Relocation Shelter Constraint The population transported from affected area i to relocation shelter j is less than or equal to the relocation shelter s capacity j Where: AP ij = Affected population from affected area i in relocation shelter j RS j = Relocation shelter capacity in relocation shelter j Non-Negativity Constraint The non-negativity constraint prevents the model from transporting a negative number of affected people from affected area i to relocation shelter j Case Study This case study has been developed to utilize the adopted transportation problem model to estimate the trip distribution 44 ITE JOURNAL / OCTOBER 2012

4 matrix f 12 affected populated zones and 10 relocation shelters. Model Assumptions The matrix of affected population, relocation shelters, and travel time is shown in Table 1. The travel time matrix was extracted from the SMEC-ACT Strategic Transportation Model 11 to reflect more reliable real-world travel time assumptions among affected zones and relocation shelters. Evacuation Distribution Matrix Model Linear programming simplex (LPS) and generalized reduced gradient (GRG) methods were used to minimize total travel times of population from affected zones to relocation shelters. Linear programming, 12 a branch of applied mathematics, deals with solving optimization problems in which the form of objective and constraints functions are linear, while GRG13 deals with nonlinear problems. The results show that the LPS method reduced the total travel time of the affected population (1,341,317) more than the GRG method (2,113,134). Both methods satisfied the constraints of affected zone population and relocation shelter capacity. Descriptive Statistical Analysis The descriptive statistical analysis of travel time for both LPS and GRG solutions, summarized in Table 2, shows that the LPS method produced a better operational trip distribution matrix than the GRG method, as LPS average travel time (as a measure of tendency) is min., while GRG average travel time is min. In addition, the standard deviation and variation (as a measures of discrepancy) of LPS are less than those of GRG, which means that the LPS average travel time is a considerably more representative value than that of GRG. Visual Assessment of Geographical Flood Evacuation Trip Distribution The SMEC-ACT TransCAD Strategic Transportation Planning Model was used to produce the geographical distributions of LPS and GRG flood distribution matrices, to demonstrate the capacity of GIS applications (such as TransCAD) Developing a framework for an efficient flood disaster management system is a milestone for developing an applicable model of evacuation operations. to handle such flood disaster transportation planning models. To facilitate the visual assessment of geographical flood evacuation trip distribution, the following themes were implemented: a red theme; with a green theme; relocation shelters were represented by desire lines; and desire lines width. Figures 3 and 4 illustrate the LPS and GRG trip distributions in desire lines, with the transported population volume between affected zones and relocation shelters represented by the lines width. Figure 3 shows that LPS trip distribution has few desire lines, while Figure 4 shows that GRG trip distribution has many complicated and nested desire lines among affected zones and relocation shelters. In terms of operational performance, LPS trip distribution is more likely to be well managed, planned, and controlled than GRG trip distribution. It is worth noting that the comparison of the two methods is not globalized or generalized, and applies only to this case study. In addition, we propose testing these two methods or other methods for emergency trip planning, to get a better trip distribution matrix. Flood Disaster Evacuation Model Comparison This section covers the features and limitations of a practical evacuation model. It also describes how the proposed evacuation model could improve and integrate/ substitute these models. State Emergency Service Timeline Modeling Flood Evacuation Operation 14 Since New South Wales (NSW) State Emergency Service (SES) became involved in the Hawkesbury-Nepean Floodplain Management Strategy in 1997 in Australia, timeline modeling flood evacuation operation has been developed to address the flood risk for up to 80,000 people living on the floodplain downstream of Warragamba Dam to the west of Sydney, Australia. Timeline analysis is being used as a functional tool to assess the evacuation requirements of the existing community or the impact of new urban development proposals. SES Timeline Modeling Flood Evacuation Features This tool is a graphic method for analyzing flood warning and evacuation scenarios, adapted from basic timeline Table 2. Model descriptive statistical indicators comparisons. Statistical Indicator Normal Case LP Simplex GRG Nonlinear Min Travel Time Max Travel Time Average Travel Time Median Travel Time Standard Deviation Variance ITE JOURNAL / OCTOBER

5 Figure 3. Linear programming simplex flood trip distribution desire lines and flow. Figure 4. Generalized reduced gradient flood trip distribution desire lines and flow. management or critical path diagrams. This model depends on the relationships among flood prediction, evacuation decisions, emergency service response and community actions, and the passage of time in a flood. SES Timeline Modeling Flood Evacuation Assumptions The following assumptions must be considered when implementing the SES timeline modeling for flood evacuation operations: Bureau of Meteorology has some indication that a severe flood is developing (time zero). usable evacuation route marks the end of the available evacuation time. and safety factors exceeds the available time between time zero and route closure, a time deficit must be corrected to manage the risk. be the most reliable option. Either the route must be made to last or the number of available lanes must be increased. tial timeline, but some overlapping could occur if elements of the timeline are treated as concurrent events. the elements most likely to take place concurrently in a real flood event. There is a critical relationship between these two elements. has a direct impact on the time available for all other elements. SES Timeline Modeling Flood Evacuation Results the expected available time before the rate of river rise will influence the evacuation operation. The vertical lines represent the evacuation operation s tasks: start warning evacuation evacuation completed. Figure 5. NSW SES timeline modeling for flood evacuation operation. SES Timeline Modeling Limitation SES timeline modeling for flood evacuation is dynamic in terms of considering the flood s effect on evacuation routes and operation due to traffic congestion and road closures. However, the SES model 46 ITE JOURNAL / OCTOBER 2012

6 does not consider the following important variables: areas and distribution to relocation shelters that directly affects evacua tion route selection and congestion; areas to relocation shelters; and nificantly in visual assessment and Integrating Proposed Transportation Model with SES Timeline Modeling proved if the proposed transportation eration is updated to distribution among affected popu evacuated population; determine the available evacuation operation time; and Conclusion build an evacuation operation trip distri bution matrix of emergency of travel from steps begin with identifying the level of sources, and end with the implementing a reliable model to estimate the evacuation operation trip distribution matrix that achieves the minimum total travel time and satisfies the constraints of transport to estimate the total travel time of the statistical analysis shows that the aver and controlled significantly better than Acknowledgments References Human Settlement Vulnerability and Adaptive Capacity Assessment-Spatial Planning Evaluation Disaster Health GIS Technology for Disaster and Emergency Management Transportation Planning Handbook: Planning for Operation ITE Journal International Journal of Innovative Computing, Information and Control Linear Programming for Optimization Nonlinear Optimization Using the Generalized Reduced Gradient Method The Application of Timeline to Evacuation Planning AHMED EL-SERGANY is a senior transport planning consultant in Egypt, Saudi Arabia, and Australia. He is experienced in transportation planning, strategic modeling, traffic microsimulation, and transport impact assessments. He holds an M.Sc. in transportation policy planning and management from Westminster University in the United Kingdom. He is a member of ITE. SAMEER ALAM, Ph.D., is a lecturer and senior research associate in the School of Engineering and IT at the University of New South Wales in Canberra, Australia. He holds a Ph.D. in computer science. His research interests are simulation and modeling, air transport management, and multi-objective evolutionary risk assessment. ITE JOURNAL / OCTOBER