Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane evacuation

An integrated storm surge modeling and traffic analysis were conducted in this study to assess the effectiveness of hurricane evacuations through a case study of Hurricane Irma. The Category 5 hurricane in 2017 caused a record evacuation with an estimated 6.8 million people relocating statewide in Florida. The Advanced Circulation (ADCIRC) model was applied to simulate storm tides during the hurricane event. Model validations indicated that simulated pressures, winds, and storm surge compared well with observations. Model simulated storm tides and winds were used to estimate the area affected by Hurricane Irma. Results showed that the storm surge and strong wind mainly affected coastal counties in south-west Florida. Only moderate storm tides (maximum about 2.5 m) and maximum wind speed about 115 mph were shown in both model simulations and Federal Emergency Management Agency (FEMA) post-hurricane assessment near the area of hurricane landfall. Storm surges did not rise to the 100-year flood elevation level. The maximum wind was much below the design wind speed of 150–170 mph (Category 5) as defined in Florida Building Code (FBC) for south Florida coastal areas. Compared with the total population of about 2.25 million in the six coastal counties affected by storm surge and Category 1–3 wind, the statewide evacuation of approximately 6.8 million people was found to be an over-evacuation due mainly to the uncertainty of hurricane path, which shifted from south-east to south-west Florida. The uncertainty of hurricane tracks made it difficult to predict the appropriate storm surge inundation zone for evacuation. Traffic data were used to analyze the evacuation traffic patterns. In south-east Florida, evacuation traffic started 4 days before the hurricane’s arrival. However, the hurricane path shifted and eventually landed in south-west Florida, which caused a high level of evacuation traffic in south-west Florida. Over-evacuation caused Evacuation Traffic Index (ETI) to increase to 200% above normal conditions in some sections of highways, which reduced the effectiveness of evacuation. Results from this study show that evacuation efficiency can be improved in the future by more accurate hurricane forecasting, better public awareness of real-time storm surge and wind as well as integrated storm surge and evacuation modeling for quick response to the uncertainty of hurricane forecasting.

The Effect of Regret-Based Risky Route Choice on the Traffic Equilibrium for Emergency Evacuation

Following the research on human decision-making under risk and uncertainty, the purpose of this paper is to analyze evacuees’ risky route decision behavior and its effect on traffic equilibrium. It examines the possibility of applying regret theory to model travellers’ regret-taking behavior and network equilibrium in emergency context. By means of modifying the utility function in expected utility theory, a regret-based evacuation traffic equilibrium model is established, accounting for the evacuee’s psychological behavior of regret aversion and risk aversion. Facing two parallel evacuation routes choice situation, the effect of evacuees’ risk aversion and regret aversion on traffic equilibrium is numerically investigated as well as the road capacity reduction from natural disaster. The findings reveal that evacuees prefer the riskless route with the lower travel time as the increase of the regret aversion degree. The equilibrium tends to be achieved when more evacuees choose the safer route jointly affected by risk aversion and regret aversion. Moreover, an optimization model for disaster occurring possibility is formulated to assess the traffic system performance for evacuation management. These findings are helpful for understanding how the regret aversion and risk aversion influence traffic equilibrium.

An Improved Critical-Edge Model for Finding Optimal Contraflow Links Considering the Influence of Intersections

A reversible roadway (contraflow) is one in which the direction of traffic flow in one or more lanes is reversed to the opposing direction for some period of time. Reversible roadways are most commonly used for accommodating directionally imbalanced traffic associated with daily commuter periods. Reversible lanes also have been widely used, in recent years, for evacuating major metropolitan regions threatened by hurricanes and other disasters. One important problem in the practice of evacuation traffic organization is the choice of road links for contraflow. Most research on the choice of contraflow links does not consider the influence of intersections, which leads to overestimation of evacuation capacity especially in congested urban road networks. We abstract an evacuation road network as a special network with directional node-weights by considering the capacity of intersection movements as directed weights of nodes. We define the critical edge for increasing the maximum flow value of such network as the one that can maximize the range of flow value increase by expanding its capacity. We obtain alternative links for contraflow by searching critical edges in such network. We presented a modified algorithm for finding such critical edges on the basis of the maximal capacity path algorithm for the classical maximum flow problem. We also provided a numerical example and tested the effects through traffic simulation. Our results show that the results considering the influence of intersections are more reasonable than those ignoring it and that taking the intersection effects into account enables us to reduce the total evacuation time.

Effect of Trip Generation Time on Evacuation Time Estimates

The entry of evacuation traffic into a road network plays an important role in how efficiently a threatened area will clear prior to the onset of hazardous conditions. Trip generation times are also instrumental in forecasting evacuation times when using traffic simulation. Given the dependency on loading rates, there is a need to understand the sensitivity of evacuation time estimates (ETEs) to network loading behavior. The contribution of this work is to quantify the impact of faster and slower loading of the population over a range of population sizes and network topologies during a nuclear power plant emergency. Examining the impact of trip generation time on ETEs, the results of this research show consistency with both prior research and observation. Specifically, in smaller, more confined areas, clearance times generally follow the loading curves that precipitated them. In larger population areas, however, it is likely that longer loading times tend to meter departures thereby limiting the formation of significant congestion despite significantly higher demand. These results suggest that clearance times generally follow loading patterns, unless there are other demand or capacity conditions (localized or global) which lead to the formation of congestion and propagate delay thereby increasing travel times. Although this research was focused on evacuations associated with nuclear power plant emergencies, it is assumed that these findings could be directly applicable to other emergencies and non-emergency scenarios where unbalanced surges in demand occur.

Evacuation Traffic Management under Diffusion of Toxic Gas Based on an Improved Road Risk Level Assessment Method

Toxic gas leakage has diffusion characteristics and thus dynamically affects surrounding zones. Most of current evacuation traffic management models set the road risk level as a static value, which is related to the distance to the hazard source, or a dynamic value, which is determined by the toxic gas concentration. However, the toxic gas propagation direction is not considered, and this may lead some evacuees driving from less dangerous regions to higher dangerous regions. To address the shortcomings of traditional evacuation traffic management models, this paper proposes an improved road risk level assessment method based on the difference of the risk levels of upstream and downstream zones of road and develops a safer evacuation traffic management model under the diffusion of toxic gas. The Cell Transmission Model (CTM) is used to depict the evacuation traffic loading process. A numerical test is carried out on Nguyen and Dupuis Network. The test results show that the improved road risk level assessment method can avoid the evacuees driving into higher risk level regions from less dangerous regions.

A Case Study on the Impacts of Connected Vehicle Technology on No-Notice Evacuation Clearance Time

No-notice evacuations of metropolitan areas can place significant demands on transportation infrastructure. Connected vehicle (CV) technology, with real-time vehicle to vehicle and vehicle to infrastructure communications, can help emergency managers to develop efficient and cost-effective traffic management plans for such events. The objectives of this research were to evaluate the impacts of CVs on no-notice evacuations using a case study of a downtown metropolitan area. The microsimulation software VISSIM was used to model the roadway network and the evacuation traffic. The model was built, calibrated, and validated for studying the performance of traffic during the evacuation. The researchers evaluated system performance with different CV penetration rates (from 0 to 30 percent CVs) and measured average speed, average delays, and total delays. The findings suggest significant reductions in total delays when CVs reached a penetration rate of 30 percent, albeit increases in delays during the beginning of the evacuation. Additionally, the benefits could be greater for evacuations that last longer and with higher proportions of CVs in the vehicle stream.