Joint modeling of evacuation departure and travel times in hurricanes

Hurricanes are costly natural disasters periodically faced by households in coastal and to some extent, inland areas. A detailed understanding of evacuation behavior is fundamental to the development of efficient emergency plans. Once a household decides to evacuate, a key behavioral issue is the time at which individuals depart to reach their destination. An accurate estimation of evacuation departure time is useful to predict evacuation demand over time and develop effective evacuation strategies. In addition, the time it takes for evacuees to reach their preferred destinations is important. A holistic understanding of the factors that affect travel time is useful to emergency officials in controlling road traffic and helps in preventing adverse conditions like traffic jams. Past studies suggest that departure time and travel time can be related. Hence, an important question arises whether there is an interdependence between evacuation departure time and travel time? Does departing close to the landfall increases the possibility of traveling short distances? Are people more likely to depart early when destined to longer distances? In this study, we present a model to jointly estimate departure and travel times during hurricane evacuations. Empirical results underscore the importance of accommodating an inter-relationship among these dimensions of evacuation behavior. This paper also attempts to empirically investigate the influence of social ties of individuals on joint estimation of evacuation departure and travel times. Survey data from Hurricane Sandy is used for computing empirical results. Results indicate significant role of social networks in addition to other key factors on evacuation departure and travel times during hurricanes.

     

Behavior-consistent information-based network traffic control for evacuation operations

The evacuation operations problem aims to avoid or mitigate the potential loss of life in a region threatened or affected by a disaster. It is shaped to a large extent by the evolution of evacuation traffic resulting from the demand–supply interactions of the associated transportation network. Information-based control is a strategic tool for evacuation traffic operations as it can enable greater access to the affected population and more effective response. However, comparatively few studies have focused on the implementation of information-based control in evacuation operations. This study develops a control module for evacuation operations centered on addressing the demand–supply interactions by using behavior-consistent information strategies. These strategies incorporate the likely responses of evacuees to the information provided in the determination of route guidance information. The control module works as an iterative computational process involving an evacuee route choice model and a control model of information strategies to determine the route guidance information to direct evacuation traffic so as to approach a desired network traffic flow pattern. The problem is formulated as a fuzzy logic based optimization framework to explicitly incorporate practical concerns related to information dissemination characteristics and social equity in evacuation operations. Numerical experiments highlight the importance of accounting for the demand–supply interactions, as the use of behavior-consistent information strategies can lead evacuee route choices to approach the operator-desired proportions corresponding to the desired traffic pattern. The results also indicate that while a behavior-consistent information strategy can be effective, gaps with the desired route proportions can exist due to the discrete nature of the linguistic messages and the real-world difficulty in accurately modeling evacuees’ actual route choice behavior.     

Evacuation Modelling in the United States: Does the Demand Model Choice Matter?

Abstract

This paper reviews the literature on the evacuation demand problem, with an emphasis on the impact of various modelling approaches on network‐wide evacuation performance measures. First, a number of important factors that affect evacuee behaviour are summarized. Evacuation software packages and tools are also investigated in terms of the demand generation model they use. The most widely used models are then selected for performing sensitivity analysis. Next, a cell‐transmission‐based system optimal dynamic traffic assignment (SO‐DTA) model is employed to assess the effects of the demand model choice on the clearance time and average travel time. It is concluded that evacuation demand models should be selected with care, and policy makers should make sure the selected demand curve can replicate real‐life conditions with relatively high fidelity for the study region to be able to develop reliable and realistic evacuation plans.     

Location planning for transit-based evacuation under the risk of service disruptions

The effectiveness of transit-based emergency evacuation highly depends on the location of pick-up facilities, resource allocation, and management. These facilities themselves are often subject to service disruptions during or after the emergency. This paper proposes a reliable emergency facility location model that determines both pre-emergency facility location planning and the evacuation operations afterwards, while facilities are subject to the risk of disruptions. We analyze how evacuation resource availability leverages individual evacuees’ response to service disruptions, and show how equilibrium of the evacuee arrival process could be reached at a functioning pick-up facility. Based on this equilibrium, an optimal resource allocation strategy is found to balance the tradeoff between the evacuees’ risks and the evacuation agency’s operation costs. This leads to the development of a compact polynomial-size linear integer programming formulation that minimizes the total expected system cost from both pre-emergency planning (e.g., facility set-up) and the evacuation operations (e.g., fleet management, transportation, and exposure to hazardous surroundings) across an exponential number of possible disruption scenarios. We also show how the model can be flexibly used to plan not only pre-disaster evacuation but also post-disaster rescue actions. Numerical experiments and an empirical case study for three coastal cities in the State of Mississippi (Biloxi, Gulfport, and D’lberville) are conducted to study the performance of the proposed models and to draw managerial insights.     

Pick‐up locations and bus allocation for transit‐based evacuation planning with demand uncertainty

This paper develops a decision‐support model for transit‐based evacuation planning under demand uncertainty. Demand uncertainty refers to the uncertainty associated with the number of transit‐dependent evacuees. A robust optimization model is proposed to determine the optimal pick‐up points for evacuees to assemble, and allocate available buses to transport the assembled evacuees between the pick‐up locations and different public shelters. The model is formulated as a mixed‐integer linear program and is solved via a cutting plane scheme. The numerical example based on the Sioux Falls network demonstrates that the robust plan yields lower total evacuation time and is reliable in serving the realized evacuee demand. Copyright © 2012 John Wiley & Sons, Ltd.     

Evacuee behaviors and factors affecting the tsunami trip generation model: A case study in phang‐nga,Thailand

A significant amount of research has focused on various types of evacuations, but little attention has been given to tsunami evacuation in the past. The purpose of this study was to investigate evacuee behaviors and factors affecting tsunami evacuation. The intention was also to analyze tsunami trip generation models. A data set of evacuation behavior was collected in an affected area, Baan Namkhem, Phang‐Nga Province, Thailand, following the Indian Ocean tsunami of December 26, 2004. The study was undertaken to determine evacuee response patterns in different conditions. Tsunami trip generation models were employed, using a binary logistic regression technique, to estimate the likelihood of evacuees being involved in each response pattern. It was found that the patterns of evacuee response to an emergency are different among the three conditions. Six factors (education level, ownership of the residence, distance to nearest seashore, disaster knowledge, number of household members, and status of respondent — permanent or transient) were found to be statistically significant. The results of this study can be used to improve the efficiency and effectiveness of future evacuation systems in Thailand.     

Evaluation of different contraflow strategies for hurricane evacuation in Charleston,South Carolina

Abstract

Limited specific evidence is available on the effectiveness of using contraflow as an evacuation traffic management tool. This study was conducted to determine the best combination of strategy options for evacuating Charleston, SC, along route I-26 during the event of a hurricane or other events. PARAMICS microscopic traffic simulator was used to evaluate the impact of each combination of evacuee response timing and traffic control strategy, such as contraflow, with respect to average vehicular travel time and evacuation duration. Analysis revealed the combination of management strategies that created the lowest evacuation durations and travel times for several types of anticipated evacuee responses. Furthermore, a proposed reconfiguration of the I-526/I-26 interchange for contraflow operations produced additional savings in travel times and evacuation durations. These findings support the use of all lanes for contraflow during all evacuations and provide justification to examine a possible reconfiguration of the I-526/I-26 interchange for use during evacuations.     

An aggregate approach to model evacuee behavior for no-notice evacuation operations

This study proposes an aggregate approach to model evacuee behavior in the context of no-notice evacuation operations. It develops aggregate behavior models for evacuation decision and evacuation route choice to support information-based control for the real-time stage-based routing of individuals in the affected areas. The models employ the mixed logit structure to account for the heterogeneity across the evacuees. In addition, due to the subjectivity involved in the perception and interpretation of the ambient situation and the information received, relevant fuzzy logic variables are incorporated within the mixed logit structure to capture these characteristics. Evacuation can entail emergent behavioral processes as the problem is characterized by a potential threat from the extreme event, time pressure, and herding mentality. Simulation experiments are conducted for a hypothetical terror attack to analyze the models’ ability to capture the evacuation-related behavior at an aggregate level. The results illustrate the value of using a mixed logit structure when heterogeneity is pronounced. They further highlight the benefits of incorporating fuzzy logic to enhance the prediction accuracy in the presence of subjective and linguistic elements in the problem.     

Analysis of hurricane evacuee mode choice behavior

The purpose of this study is to explain the evacuee mode choice behavior of Miami Beach residents using survey data from a hypothetical category four hurricane to reveal different evacuees’ plans. Evacuation logistics should incorporate the needs of transit users and car-less populations with special attention and proper treatment. A nested logit model has been developed to explain the mode choice decisions for evacuees’ from Miami Beach who use non-household transportation modes, such as special evacuation bus, taxi, regular bus, riding with someone from another household and another type of mode denoted and aggregated as other. Specifically, the model explains that the mode choice decisions of evacuees’, who are likely to use different non-household transportation modes, are influenced by several determining factors related to evacuees’ socio-demographics, household characteristics, evacuation destination and previous experience. The findings of this study will help emergency planners and policy-makers to develop better evacuation plans and strategies for evacuees depending on others for their evacuation transportation.     

Effects of shadow evacuation on megaregion evacuations

Transportation systems serve important roles during emergencies, in particular for evacuations. However, efficient travel during these life-and-death scenarios can be adversely impacted by external conditions, such as unnecessary and unneeded travel. This research sought to enhance the understanding of the effects of these conditions by analyzing shadow evacuations, and their impact on regional traffic operations in megaregions, more broadly. The research was based on simulations of a range of hurricane evacuation threat scenarios in the Gulf of Mexico building upon prior study using TRANSIMS. These assessments are also targeted at what many assume could be worst case evacuation conditions and pushing the limits of current simulation modeling capability. Among the broader findings of this work was that shadow evacuation participation rates did not significantly impact the evacuation clearance times within mandatory evacuation areas of the megaregion as long as demand could be temporarily spread out. This finding does not, however, suggest that the shadow evacuations have no impact on evacuation processes. High rates of shadow evacuees can cause significant congestion if they are not able to exit critical routes before mandatory evacuees reach areas further away from the coast.     

Compromising system and user interests in shelter location and evacuation planning

Traffic management during an evacuation and the decision of where to locate the shelters are of critical importance to the performance of an evacuation plan. From the evacuation management authority’s point of view, the desirable goal is to minimize the total evacuation time by computing a system optimum (SO). However, evacuees may not be willing to take long routes enforced on them by a SO solution; but they may consent to taking routes with lengths not longer than the shortest path to the nearest shelter site by more than a tolerable factor. We develop a model that optimally locates shelters and assigns evacuees to the nearest shelter sites by assigning them to shortest paths, shortest and nearest with a given degree of tolerance, so that the total evacuation time is minimized. As the travel time on a road segment is often modeled as a nonlinear function of the flow on the segment, the resulting model is a nonlinear mixed integer programming model. We develop a solution method that can handle practical size problems using second order cone programming techniques. Using our model, we investigate the importance of the number and locations of shelter sites and the trade-off between efficiency and fairness.     

A practical method to estimate the benefits of improved road network reliability: an application to departing air passengers

This paper develops and applies a practical method to estimate the benefits of improved reliability of road networks. We present a general methodology to estimate the scheduling costs due to travel time variability for car travel. In contrast to existing practical methods, we explicitly consider the effect of travel time variability on departure time choices. We focus on situations when only mean delays are known, which is typically the case when standard transport models are used. We first show how travel time variability can be predicted from mean delays. We then estimate the scheduling costs of travellers, taking into account their optimal departure time choice given the estimated travel time variability. We illustrate the methodology for air passengers traveling by car to Amsterdam Schiphol Airport. We find that on average planned improvements in network reliability only lead to a small reduction in access costs per trip in absolute terms, mainly because most air passengers drive to the airport outside peak hours, when travel time variability tends to be low. However, in relative terms the reduction in access costs due to the improvements in network reliability is substantial. In our case we find that for every 1 Euro reduction in travel time costs, there is an additional cost reduction of 0.7 Euro due to lower travel time variability, and hence lower scheduling costs. Ignoring the benefits from improved reliability may therefore lead to a severe underestimation of the total benefits of infrastructure improvements.     

Optimization of bus routing strategies for evacuation

Efficient transportation of evacuees during an emergency has long been recognized as a challenging issue. This paper investigates emergency evacuation strategies that rely on public transit, where buses run continuously, rather than fixed route, based upon the spatial and temporal information of evacuee needs. We formulated an optimal bus operating strategy that minimizes the exposed casualty time rather than operational cost, as a deterministic mixed‐integer program, and investigated the solution algorithm. A Lagrangian‐relaxation‐based solution algorithm was developed for the proposed model. Numerical experiments with different problem sizes were conducted to evaluate the method. Copyright © 2013 John Wiley & Sons, Ltd.     

Strategic evacuation planning with pedestrian guidance and bus routing: a mixed integer programming model and heuristic solution

This paper presents a mathematical model to plan emergencies in a densely populated urban zone where a certain numbers of pedestrians depend on transit for evacuation. The proposed model features an integrated operational framework, which simultaneously guides evacuees through urban streets and crosswalks (referred to as “the pedestrian network”) to designated pickup points (e.g., bus stops), and routes a fleet of buses at different depots to those pick‐up points and transports evacuees to their destinations or safe places. In this level, the buses are routed through the so‐called “vehicular network.” An integrated mixed integer linear program that can effectively take into account the interactions between the aforementioned two networks is formulated to find the maximal evacuation efficiency in two networks. Because the large instances of the proposed model are mathematically difficult to solve to optimality, a two‐stage heuristic is developed to solve larger instances of the model. Results from hundreds of numerical examples analysis indicate that proposed heuristic works well in providing (near) optimal or feasibly good solutions for medium‐scale to large‐scale instances that may arise in real transit‐based evacuation situations in a much shorter amount of computational time compared with cplex (can find optimal/feasible solutions for only five instances within 3 hours of running). Copyright © 2016 John Wiley & Sons, Ltd.     

Expirable parking reservations for managing morning commute with parking space constraints

When total parking supply in an urban downtown area is insufficient, morning commuters would choose their departure times not only to trade off bottleneck congestion and schedule delays, but also to secure a parking space. Recent studies found that an appropriate combination of reserved and unreserved parking spaces can spread the departures of those morning commuters and hence reduce their total travel cost. To further mitigate both traffic congestion and social cost from competition for parking, this study considers a parking reservation scheme with expiration times, where commuters with a parking reservation have to arrive at parking spaces for the reservation before a predetermined expiration time. We first show that if all parking reservations have the same expiration time, it is socially preferable to set the reservations to be non-expirable, i.e., without expiration time. However, if differentiated expiration times are properly designed, the total travel cost can be further reduced as compared with the reservation scheme without expiration time, since the peak will be further smoothed out. We explore socially desirable equilibrium flow patterns under the parking reservation scheme with differentiated expiration times. Finally, efficiencies of the reservation schemes are examined.     

Effects of queue spillover in networks considering simultaneous departure time and route choices

This paper explores the effects of queue spillover in transportation networks, in the context of dynamic traffic assignment. A model of spatial queue is defined to characterize dynamic traffic flow and queuing formation in network links. Network users simultaneously choose departure time and travel route to minimize the travel cost including journey time and unpunctuality penalty. Using some necessary conditions of the dynamic user equilibrium, dynamic network flows are obtained exactly on some networks with typical structure. Various effects of queue spillover are discussed based on the results of these networks, and some new paradoxes of link capacity expansion have been found as a result of such effects. Analytical and exact results in these typical networks show that ignoring queuing length may generate biased solutions, and the link storage capacity is a very important factor concerning the performance of networks.     

Methodology for Validating Dynamic Origin–Destination Matrix Estimation Models with Implications for Advanced Traveler Information Systems

This paper describes a methodology for validating online dynamic O–D matrix estimation models using loop detector data in large-scale transportation networks. The simulation procedure focuses on travel aspects related to the collective trip structure of users, including the amount and duration of trips between O–D pairs, trip departure rates, average travel time from each origin and combinations of them. The analysis identifies emerging systematic patterns between these factors and issues related to the model performance, including network scale effects. This procedure aims to enhance the usage of prior O–D information based on, e.g. travel surveys, that are typically used in the estimation process. Moreover, it seeks to integrate the validation of dynamic O–D matrix estimation models with strategies for identifying target population groups for online planning and assessment of real-time travel information services within the context of Advanced Traveler Information Systems (ATIS).     

Risk-based spatial zone determination problem for stage-based evacuation operations

This study seeks to determine risk-based evacuation subzones for stage-based evacuation operations in a region threatened/affected by a disaster so that information-based evacuation strategies can be implemented in real-time for the subzone currently with highest evacuation risk to achieve some system-level performance objectives. Labeled the evacuation risk zone (ERZ), this subzone encompasses the spatial locations containing the population with highest evacuation risk which is a measure based on whether the population at a location can be safely evacuated before the disaster impacts it. The ERZ for a stage is calculated based on the evolving disaster characteristics, traffic demand pattern, and network supply conditions over the region in real-time subject to the resource limitations (personnel, equipment, etc.) of the disaster response operators related to implementing the evacuation strategies. Thereby, the estimated time-dependent lead time to disaster impact at a location and the estimated time-dependent clearance time based on evolving traffic conditions are used to compute evacuation risk. This time-unit measure of evacuation risk enables the ERZ concept to be seamlessly applied to different types of disasters, providing a generalized framework for mass evacuation operations in relation to disaster characteristics. Numerical experiments conducted to analyze the performance of the ERZ-based paradigm highlight its benefits in terms of better adapting to the dynamics of disaster impact and ensuring a certain level of operational performance effectiveness benchmarked against the idealized system optimal traffic pattern for the evacuation operation, while efficiently utilizing available disaster response resources.     

A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

Dynamic traffic simulation models are frequently used to support decisions when planning an evacuation. This contribution reviews the different (mathematical) model formulations underlying these traffic simulation models used in evacuation studies and the behavioural assumptions that are made. The appropriateness of these behavioural assumptions is elaborated on in light of the current consensus on evacuation travel behaviour, based on the view from the social sciences as well as empirical studies on evacuation behaviour. The focus lies on how travellers’ decisions are predicted through simulation regarding the choice to evacuate, departure time choice, destination choice, and route choice. For the evacuation participation and departure time choice we argue in favour of the simultaneous approach to dynamic evacuation demand prediction using the repeated binary logit model. For the destination choice we show how further research is needed to generalize the current preliminary findings on the location-type specific destination choice models. For the evacuation route choice we argue in favour of hybrid route choice models that enable both following instructed routes and en-route switches. Within each of these discussions, we point at current limitations and make corresponding suggestions on promising future research directions.     

Managing evacuation networks

This paper proposes a non-anticipative, adaptive, decentralized strategy for managing evacuation networks. The strategy is non-anticipative because it does not rely on demand forecasts, adaptive because it uses real-time traffic information, and decentralized because all the information is available locally. It can be used with a failed communication network.The strategy pertains to networks in which no links backtrack in the direction of increased risk. For these types of networks, no other strategy exists that can evacuate more people in any given time, or finish the evacuation in less time. The strategy is also shown to be socially fair, in the sense that the time needed to evacuate all the people exceeding any risk level is, both, the least possible, and the same as if less-at-risk individuals did not participate in the evacuation. The strategy can be proven optimal even when backflows happen due to driver gaming.