Effects of Data Resolution and Human Behavior on Large Scale Evacuation Simulations

Traffic Analysis Zones (TAZ) based macroscopic simulation studies are mostly applied in evacuation planning and operation areas. The large size in TAZ and aggregated information of macroscopic simulation underestimate the real evacuation performance. To take advantage of the high resolution demographic data LandScan USA (the zone size is much smaller than TAZ) and agent-based microscopic traffic simulation models, many new problems appeared and novel solutions are needed. A series of studies are conducted using LandScan USA Population Cells (LPC) data for evacuation assignments with different network configurations, travel demand models, and travelers compliance behavior. First, a new Multiple Source Nearest Destination Shortest Path (MSNDSP) problem is defined for generating Origin Destination matrix in evacuation assignments when using LandScan dataset. Second, a new agent-based traffic assignment framework using LandScan and TRANSIMS modules is proposed for evacuation planning and operation study. Impact analysis on traffic analysis area resolutions (TAZ vs LPC), evacuation start times (daytime vs nighttime), and departure time choice models (normal S shape model vs location based model) are studied. Third, based on the proposed framework, multi-scale network configurations (two levels of road networks and two scales of zone sizes) and three routing schemes (shortest network distance, highway biased, and shortest straight-line distance routes) are implemented for the evacuation performance comparison studies. Fourth, to study the impact of human behavior under evacuation operations, travelers compliance behavior with compliance levels from total complied to total non-complied are analyzed.Comment: PhD dissertation. UT Knoxville. 130 pages, 37 figures, 8 tables. University of Tennessee, 2013. http://trace.tennessee.edu/utk_graddiss/259

Building Resilience in a Major City Evacuation Plan Using Simulation Modeling

This study provides data on the optimal staff, materials, space, and time resources required to operate a regional hub reception center, a “short-term facility with the goal to process and transport displaced survivors (evacuees) to temporary or permanent shelters following a catastrophic incident,” (Regional Catastrophic Planning Team, 2012). The facility will process approximately 20,000 evacuees over its entire seven-day duration following a disaster to assist in community resilience. The study was performed using a model created using the computer simulation software, AnyLogic. The results of the study demonstrated that the goals set forth by the Illinois-Indiana-Wisconsin Regional Catastrophic Planning Team could be improved upon and that the largest contributing factor to optimizing the RHRC is finding the optimal number of total staff members to operate the facility

A Proposed Framework for Simultaneous Optimization of Evacuation Traffic Distribution and Assignment

In the conventional evacuation planning process, evacuees are assigned to fixed destinations based mainly on the criterion of geographical proximity. However, such pre-specified destinations (OD table) almost always lead to sub-optimal evacuation efficiencies due to uncertain road conditions such as congestion, road blockage, and other hazards associated with the emergency. By relaxing the constraint of assigning evacuees to pre-specified destinations, a one-destination evacuation (ODE) concept has the potential of greatly improving the evacuation efficiency. A framework for simultaneous optimization of evacuation traffic distribution and assignment is therefore proposed in this study. Based on the concept of ODE, the optimal destination and route assignment can be determined by solving a one-destination (1D) traffic assignment problem on a modified network representation. When tested on real-world networks for evacuation studies, the proposed 1D model presents substantial improvement over the conventional multiple-destination (nD) model. For instance, for a hypothetical county-wide evacuation, a nearly 80% reduction in the overall evacuation time can be achieved when modeling of traffic routing with en route information in the 1D framework, and the 1D optimization results can also be used to improve the planning OD tables, resulting in an up to 60% reduction in the overall evacuation time. More importantly, this framework can be actually implemented, and its efficiency enhancement can be realized simply by instructing evacuees to head for more efficient destinations determined from the 1D optimization performed beforehand

Evaluation of Transportation Network Reliability under Emergency Based on Reserve Capacity

There are differences between the requirements for traffic network for traffic demand in daily and emergency situations. In order to evaluate how the network designed for daily needs can meet the surging demand for emergency evacuation, the concept of emergency reliability and corresponding evaluation method is proposed. This paper constructs a bilevel programming model to describe the proposed problem. The upper level problem takes the maximum reserve capacity multiplier as the optimization objective and considers the influence of reversible lane measures taken under emergency conditions. The lower level model adopts the combined traffic distribution/assignment model with capacity limits, to describe evacuees’ path and shelter choice behavior under emergency conditions and take into account the traits of crowded traffic. An iterative optimization method is proposed to solve the upper level model, and the lower level model is transformed into a UE assignment problem with capacity limits over a network of multiple origins and single destination, by adding a dummy node and several dummy links in the network. Then a dynamic penalty function algorithm is used to solve the problem. In the end, numerical studies and results are provided to demonstrate the rationality of the proposed model and feasibility of the proposed solution algorithms. Document type: Articl

Large-Scale Evacuation Network Model for Transporting Evacuees with Multiple Priorities

There are increasing numbers of natural disasters occurring worldwide, particularly in populated areas. Such events affect a large number of people causing injuries and fatalities. With ever increasing damage being caused by large-scale natural disasters, the need for appropriate evacuation strategies has grown dramatically. Providing rapid medical treatment is of utmost importance in such circumstances. The problem of transporting patients to medical facilities is a subject of research that has been studied to some extent. One of the challenges is to find a strategy that can maximize the number of survivors and minimize the total cost simultaneously under a given set of resources and geographic constraints. However, some existing mathematical programming methodologies cannot be applied effectively to such large-scale problems. In this thesis, two mathematical optimization models are proposed for abating the extensive damage and tragic impact by large-scale natural disasters. First of all, a mathematical optimization model called Triage-Assignment-Transportation (TAT) model is suggested in order to decide on the tactical routing assignment of several classes of evacuation vehicles between staging areas and shelters in the nearby area. The model takes into account the severity level of the evacuees, the evacuation vehicles’ capacities, and available resources of each shelter. TAT is a mixed-integer linear programming (MILP) and minimum-cost flow problem. Comprehensive computational experiments are performed to examine the applicability and extensibility of the TAT model. Secondly, a MILP model is addressed to solve the large-scale evacuation network problem with multi-priorities evacuees, multiple vehicle types, and multiple candidate shelters. An exact solution approach based on modified Benders’ decomposition is proposed for seeking relevant evacuation routes. A geographical methodology for a more realistic initial parameter setting is developed by employing spatial analysis techniques of a GIS. The objective is to minimize the total evacuation cost and to maximize the number of survivors simultaneously. In the first stage, the proposed model identifies the number and location of shelters and strategy to allocate evacuation vehicles. The subproblem in the second stage determines initial stock and distribution of medical resources. To validate the proposed model, the solutions are compared with solutions derived from two solution approaches, linear programming relaxation and branch-and-cut algorithm. Finally, results from comprehensive computational experiments are examined to determine applicability and extensibility of the proposed model. The two evacuation models for large-scale natural disasters can offer insight to decision makers about the number of staging areas, evacuation vehicles, and medical resources that are required to complete a large-scale evacuation based on the estimated number of evacuees. In addition, we believe that our proposed model can serve as the centerpiece for a disaster evacuation assignment decision support system. This would involve comprehensive collaboration with LSNDs evacuation management experts to develop a system to satisfy their needs

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

EVACUATION ROUTE MODELING AND PLANNING WITH GENERAL PURPOSE GPU COMPUTING

This work introduces a bilevel, stochastic optimization problem aimed at robust, regional evacuation network design and shelter location under uncertain hazards. A regional planner, acting as a Stackelberg leader, chooses among evacuation-route contraflow operation and shelter location to minimize the expected risk exposure to evacuees. Evacuees then seek an equilibrium with respect to risk exposure in the lower level. An example network is solved exactly with a strategy that takes advantage of a fast, low-memory, equilibrium algorithm and general purpose computing on graphical processing units

Discrete Time Dynamic Traffic Assignment Models with Lane Reversals for Evacuation Planning

In an event of a natural or man-made disaster, an evacuation is likely to be called for to move residents away from potentially hazardous areas. Road congestion and traffic stalling is a common occurrence as residents evacuate towns and cities for safe refuges. Lane reversal, or contra-flow, is a remedy to increase outbound flow capacities from disaster areas which in turn will reduce evacuation time of evacuees during emergency situations. This thesis presents a discrete-time traffic assignment system with lane reversals which incorporates multiple sources and multiple destinations to predict optimal traffic flow at various times throughout the entire planning horizon. With the realization of lane reversals, naturally the threat of potential head-on collisions emerges. To avoid the occurrence of such situations, a collision prevention constraint is introduced to limit directional flow on lanes based on departure time.;This model belongs to the class of dynamic traffic assignment (DTA) problems. Initially the model was formulated as a discrete-time system optimum dynamic traffic assignment (DTA-SO) problem, which is a mixed integer nonlinear programming problem. Through various proven theorems, a linearized upper bound was derived that is able to approximate the original problem with very high precision. The result is an upper bound mixed integer linear programming problem (DTA-UB). The discrete-time DTA model is suitable for evacuation planning because the model is able to take care of dynamic demands, and temporal ow assignment. Also, simultaneous route and departure is assumed and an appropriate travel time function is used to approximate the minimum and maximum travel time on an arc.;This thesis discusses the different attributes that relates to Dynamic Traffic Assignment. DTA model properties and formulation methodology are also expounded upon. A model analysis that breaks down each output into individual entities is provided to further understand the computational results of small networks. A no reversal DTA-UB model (NRDTA-UB) is formulated and its computational results are compared to DTA-UB. Through the extensive computational results, DTA-UB is proven to obtain much better results than NRDTA-UB despite having longer solving time. This is a step toward realizing the supremacy of having lane reversals in a real-life evacuation scenario

Modeling transportation impacts of natural disasters

Natural disasters such as hurricanes and pandemics cause significant disruption in people's lives. This research aims to model such disasters' transportation impacts using state-of-the-art simulation methods, statistical and machine learning algorithms. Specifically, two case studies of disasters were studied. First, the effects of various travel demand management and network control strategies on hurricane evacuation of the Hampton Roads region in Virginia were modeled. A mesoscopic simulation model was updated using demand data generated from a household survey effort. The results indicated that phased evacuation scenarios performed the best in terms of travel times, evacuating volumes, and clearance times. Also, the use of lane-reversal on a major interstate evacuation route was shown to be effective in several scenarios. The household survey also asked respondents to provide their preferred route types in the event of a hypothetical Category 4 hurricane evacuation. The responses were used to understand better which factors contribute to evacuees selecting freeway vs. non-freeway evacuation routes. A mixed (random parameters) logit model was developed to determine factors that influence evacuees deciding between a freeway and a non-freeway route. The study found that several factors contribute to evacuees choosing a freeway over other routes. In the descending order of importance (i.e., marginal effects), these factors are willing to use the official recommended route, living in a single-family or duplex housing, expected travel time to reach the destination, being employed, and possessing prior evacuation experience. Conversely, a few factors had a negative effect on choosing a freeway. These factors are willingness to evacuate two days before landfall and evacuating to a public shelter or a second home. This study's findings can help emergency management and transportation agencies design effective demand management and traffic control plans to evacuate regions during a hurricane safely. The second case study involved the modeling of travel impacts of COVID-19 pandemic. Using New York County (i.e. Manhattan) as an example, publicly available location-based mobility data from Google and COVID-19 data from government sources were used to build mobility prediction models. Three machine learning algorithms, Regression Tree, Random Forest, and Extreme gradient boosting (XGBoost) were used to develop different models. Among the three models, the Random Forest models performed the best at predicting mobility index with mean absolute percentage errors of 5.3% and 5.8% at transit stations, 6.5% and 7.1 % for retail and recreation activities. These models enable accurate forecasting of expected mobility by taking into account time series data of activity and COVID variables.Includes bibliographical reference

A Framework for Developing and Integrating Effective Routing Strategies Within the Emergency Management Decision-Support System, Research Report 11-12

This report describes the modeling, calibration, and validation of a VISSIM traffic-flow simulation of the San José, California, downtown network and examines various evacuation scenarios and first-responder routings to assess strategies that would be effective in the event of a no-notice disaster. The modeled network required a large amount of data on network geometry, signal timings, signal coordination schemes, and turning-movement volumes. Turning-movement counts at intersections were used to validate the network with the empirical formula-based measure known as the GEH statistic. Once the base network was tested and validated, various scenarios were modeled to estimate evacuation and emergency vehicle arrival times. Based on these scenarios, a variety of emergency plans for San José’s downtown traffic circulation were tested and validated. The model could be used to evaluate scenarios in other communities by entering their community-specific data