3,910 research outputs found

    Generic Incident Model for Investigating Traffic Incident Impacts on Evacuation Times in Large-Scale Emergencies

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    Traffic incidents cause a ripple effect of reduced travel speeds, lane changes, and the pursuit of alternative routes that results in gridlock on the immediately affected and surrounding roadways. The disruptions caused by the secondary effects significantly degrade travel time reliability, which is of great concern to the emergency planners who manage evacuations. Outcomes forecast by a generic incident model embedded in a microscopic evacuation simulation, the Real-Time Evacuation Planning Model (RtePM), were examined to quantify the change in time required for an emergency evacuation that results from traffic incidents. The incident model considered vehicle miles traveled on each individual segment of the studied road network model. The two scenarios considered for this investigation were evacuations of (a) Washington, D.C., after a simulated terrorist attack and (b) Virginia Beach, Virginia, in response to a simulated hurricane. These results could help the emergency planning community understand and investigate the impact of traffic incidents during an evacuation

    Temporal-Spatial Analysis of Emergency Evacuation Traffic

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    Mass evacuations, particularly those at a statewide level, represent the largest single-event traffic movements that exist. These complex events can last several days, cover thousands of miles of roadway, and include hundreds of thousands of people and vehicles. Often, they are also marked by enormous delay and congestion and are nearly always criticized for their inefficiency and lack of management. However, there are no standardized methods by which to systematically quantify traffic characteristics at the proper scale. This paper describes research to develop and apply an analytical method to measure and describe statewide mass-evacuations in a practical, cost-effective manner. The research methods are based on simple, yet widely available, and easily understood traffic count datasets that support both qualitative and quantitative analyses. By spatially and temporally arranging sensor-based statewide traffic volume data from Hurricane Irma (2017), Michael (2018), Tubbs Fire (2017), and Thomas Fire (2018) evacuations, these methods were able to describe and address several key questions about these events. The methods described herein estimate the start and end of the auto-based evacuation, the loading and peaking characteristics of traffic, and the total number of vehicles used in the evacuation process, as well as the effective start and end of the auto-based reentry. Among the key findings of this work were that the Hurricane Irma and Michael evacuations began several days before landfall, peaking two to three days prior to the storm, suggesting a heightened perception of risk; and that the Thomas and Tubbs fire evacuations traffic were impacted by subsequent fires nearby. It is expected that state departments of transportation and emergency management officials would be able to reproduce the procedure presented here to analyze future evacuations

    Operational Impact of Shadow Evacuation on Regional Road Networks During Short-Notice Emergency Evacuations

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    As part of evacuation planning, development of effective tactical and operational strategies are essential to safely and efficiently mobilize the public away from the threat. Evacuations are classified by the time between notification and the anticipated arrival of the threat which can be categorized as short, or no-notice emergencies. Emergencies involve the computation of the time required to evacuate the area of risk, which is the time to clear a radius of up to about 10-miles around the nuclear power plant, known as the emergency planning zone (EPZ). These evacuation time estimates (ETE) also account for the evacuation of the public outside the defined area of risk. Typically, this area extends five miles outside the EPZ boundary and it is commonly referred to as the shadow evacuation region. Although shadow evacuation could create significant traffic congestion that affects the EPZ clearance process, there is limited research quantifying this effect. The objective of this research was to study the impacts of shadow evacuation to the overall EPZ clearance process. To accomplish this, the research used microscopic traffic simulation to assess the effect of different shadow participation rates for three hypothetical nuclear power plants with distinct population sizes surrounding the plant (small, medium, and large) and roadway characteristics. The guidance in NUREG/CR-7002 for ETE studies recommends a 20 percent participation rate that was based on previous studies, research related to ETE demographics, public response, and other contributing factors. However, the 20 percent recommendation may be conservative. The results suggested that small population sites are not impacted significantly by varying the shadow participation rates. However, medium and large population sites showed a noticeable effect, particularly in those corridors with less capacity. If the shadow evacuation participation rate is increased to 40 percent, the ETE to evacuate 90 percent of the population is increased by up to 10 percent in medium-sized areas, and up to 19 percent in large areas. Under the same conditions, the ETE to evacuate 100 percent of the population increases by less than 5 percent for medium-sized areas and less than 3 percent for large areas

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

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    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

    Modeling Decision Making Related to Incident Delays During Hurricane Evacuations

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    Successful evacuations from metropolitan areas require optimizing the transportation network, monitoring conditions, and adapting to changes. Evacuation plans seek to maximize the city\u27s ability to evacuate traffic to flee the endangered region, but once an evacuation begins, real time events degrade even the best plans. To better understand behavioral responses made during a hurricane evacuation, a survey of potential evacuees obtained data on demographics, driving characteristics, and the traffic information considered prior to and during an evacuation. Analysis showed significant levels of correlation between demographic factors (e.g., gender, age, social class, etc.) and self-assessed driver characteristics, but limited correlation with the decision to take an alternate route. Survey results suggest evacuees\u27 decisions to divert are functions of the length of time a driver has been in congestion, the amount of travel information provided, and its method of delivery. This association differs significantly from those identified by other studies that focused on routine, non-evacuation, conditions. A decision-making model that forecasts decision tendencies using these factors was created. The model was integrated in and tested using a dynamic evacuation simulation. The combined model and simulation allow assessment of the impacts traveler information content, timing, and method of delivery have on traffic flow and evacuation times, imitating the impact of traffic information systems. The effectiveness of alternate route use was assessed by measurements of total vehicle volumes processed and queue persistence. Effectiveness was highly dependent on the road network in the immediate vicinity, especially the number of accesses to the alternate route and vehicle capacity on the alternate route and accesses. Integration of the decision-making model in a dynamic hurricane evacuation simulation is unique to this study. This study yields a greater understanding of evacuee decisions and factors associated with related travel decisions. It provides the novel integration of a behavioral model and a dynamic evacuation simulation, increasing the realism of evacuation planning and providing a valuable tool supporting the decision process. Understanding gained may contribute to reduced evacuation times and enhanced public safety

    Development of dynamic travel demand models for hurricane evacuation

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    Little attention has been given to estimating dynamic travel demand in transportation planning in the past. However, when factors influencing travel are changing significantly over time – such as with an approaching hurricane - dynamic demand and the resulting variation in traffic flow on the network become important. In this study, dynamic travel demand models for hurricane evacuation were developed with two methodologies: survival analysis and sequential choice model. Using survival analysis, the time before evacuation from a pending hurricane is modeled with those that do not evacuate considered as censored observations. A Cox proportional hazards regression model with time-dependent variables and a Piecewise Exponential model were estimated. In the sequential choice model the decision to evacuate in the face of an oncoming hurricane is considered as a series of binary choices over time. A sequential logit model and a sequential complementary log-log model were developed. Each model is capable of predicting the probability of a household evacuating at each time period before hurricane landfall as a function of the household’s socio-economic characteristics, the characteristics of the hurricane (such as distance to the storm), and policy decisions (such as the issuing of evacuation orders). Three datasets were used in this study. They were data from Southwest Louisiana collected following Hurricane Andrew, data from South Carolina collected following Hurricane Floyd, and stated preference survey data collected from New Orleans area. Based on the analysis, the sequential logit model was found to be the best alternative for modeling dynamic travel demand for hurricane evacuation. The sequential logit model produces predictions which are superior to those of current evacuation participation rate models with response curves. Transfer of the sequential logit model estimated on the Floyd data to the Andrew data demonstrated that the sequential logit model is capable of estimating dynamic travel demand in a different environment than the one in which it was estimated, with reasonable accuracy. However, more study is required on the transferability of models of this type, as well as the development of procedures that would allow the updating of transferred model parameters to better reflect local evacuation behavior

    SIMULATION AND MATHEMATICAL MODELING TO SUPPORT COMMUNITY-WIDE EVACUATION DECISIONS FOR MULTIPLE POPULATION GROUPS

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    Evacuating a large population from an at-risk area has been the subject of extensive research over the past few decades. In order to measure trip completion and total evacuation times accurately, most researchers have implemented some combination of simulation and optimization methods to provide vehicular flow and congestion data. While the general at-risk population comprises the majority of travelers on the road network, there are often specific groups to consider when assessing the ability to evacuate an entire population. In particular, healthcare facilities (e.g., hospitals) may require evacuation, and the trip times may become an important health issue for patients being evacuated. Emergency vehicles from these facilities will share the same roadways and exit paths that are used by the local community, and it becomes increasingly important to minimize long travel times when patient care must be provided during transport. As the size of the area to model grows larger, predicting individual vehicle performance becomes more difficult. Standard transportation-specific micro-simulation, which models vehicle interactions and driver behaviors in detail, may perform very well on road networks that are smaller in size. In this research, a novel modeling approach, based on cell transmission and a speed-flow relationship, is proposed that combines the \u27micro\u27 and \u27meso\u27 approaches of simulation modeling. The model is developed using a general purpose simulation software package. This allows for an analysis at each vehicle level in the travel network. In addition, using these method and approaches, we can carry out dynamic trip planning where evacuees decide their route according to current road and traffic conditions. By translating this concept to an actual implementation, a traffic management center could identify current best travel routes between several origins and destinations, while continuing to update this list periodically. The model could suggest routings that favor either a user-optimal or system-optimal objective. This research also extended the concept of dynamic traffic assignment while modeling evacuation traffic. This extension includes the utilization of Wardrop\u27s System Optimum theory, where flow throughout the network is controlled in order to lower the risk of traffic congestion. Within this framework traffic flow is optimized to provide a route assignment under dynamic traffic conditions. This dissertation provides a practical and effective solution for a comprehensive evacuation analysis of a large, metropolitan area and the evacuation routes extending over 100 miles. Using the methodologies in this dissertation, we were able to create evacuation input data for general as well as special needs populations. These data were fed into the tailored simulation model to determine critical evacuation start times and evacuation windows for both the community-wide evacuation. Moreover, our analysis suggested that a hospital evacuation would need to precede a community-wide evacuation if the community-wide evacuation does not begin more than 24 hours before a hurricane landfall. To provide a more proactive approach, we further suggested a routing strategy, through a dynamic traffic assignment framework, for supporting an optimal flow of traffic during an evacuation. The dynamic traffic assignment approach also provides a mechanism for recommending specific time intervals when traffic should be diverted in order to reduce traffic congestion

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

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    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

    Modeling of Household Evacuation Decision, Departure Timing, and Number of Evacuating Vehicles from Hurricane Matthew

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    This dissertation investigates households’ evacuation decision, number of household vehicles used in evacuation, and departure timing from Hurricane Matthew. Regarding the evacuation decision, this dissertation takes a step further by presenting three level evacuation decision models that include full, partial, and no evacuation alternatives rather than the binary evacuate/stay decision. Multinomial (MNL) regression and random parameter MNL techniques were utilized to develop the prediction models. Results showed that some of the variables which affect the evacuate/stay decision have different influences on the three alternatives. The preferred MNL model was tested for random parameters and one random parameter (age of the respondent) was identified for the utility expression pertaining to the no evacuation alternative. For the vehicle choice study, zero truncated Poisson regression was utilized with the survey data. This modeling approach has rarely been applied to the evacuation context and the prediction of the number of household vehicles used is relatively understudied, compared to other evacuation-related decisions. The final preferred model contains three significant variables (marital status, gender, and evacuation timing from 6 am to noon). The final part of this dissertation investigates the factors affecting departure timing choice. Having an accurate estimate of the departure time will allow the prediction of dynamic evacuation demand and developing effective evacuation strategies which will enhance the overall evacuation planning and management. A Cox proportional-hazards model was utilized to model the evacuation departure timing. Four significant variables were identified in the final model, two of them are related to uncertainty. This part of the dissertation also studies evacuees’ stated preference about whether or not they would change their evacuation timing if they relived the hurricane event. In our study, almost 34% of respondents reported that they would change their departure timing if they relived the hurricane event. A binary logit model was utilized in this part and the preferred model contains five significant variables related to past experience, the type of evacuation order received, and the evacuation destination
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