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.     

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.

     

A random-parameter hazard-based model to understand household evacuation timing behavior

The goal of this paper is to develop a random-parameter hazard-based model to understand hurricane evacuation timing by individual households. The choice of departure time during disasters is a complex dynamic process and depends on the risk that the hazard represents, the characteristics of the household and the built environment features. However, the risk responses are heterogeneous across the households; this unobserved heterogeneity is captured through random parameters in the model. The model is estimated with data from Hurricane Ivan including households from Alabama, Louisiana, Florida and Mississippi. It is found that the variables related to household location, destination characteristics, socio-economic characteristics, evacuation notice and household decision making are key determinants of the departure time. As such the developed model provides some fundamental inferences about hurricane evacuation timing behavior.     

Gender,Residential Location,and Household Travel: Empirical Findings from Low‐income Urban Settlements in Durban,South Africa

Abstract

Urban development and transport policies designed to improve the livelihoods of poor communities need to consider the particular needs of women to be effective. Gender roles are played out in a spatial world, and can thus be expected to vary across the urban landscape. The paper examines empirical relationships between spatial factors—in particular residential location within the city—and travel behaviour for men and women in a cross‐section of low‐income communities in a large metropolitan area in South Africa. Data from a comprehensive household survey show that locality has a significant influence on gender experiences of mobility. Gender differences are greatest in more distant, rural localities, although site‐specific characteristics such as walking access to social services, informal work, and small‐scale agriculture help alleviate women’s inequitable travel burdens. Central localities display the smallest differences between men and women’s travel habits, supporting the notion that the high access afforded by centrally located housing helps to promote the satisfaction of women’s daily needs as well as their strategic empowerment. Households in peri‐urban and peripheral localities suffer the highest travel burdens, having neither the high access of a central location nor the livelihoods‐enhancing amenities of a rural environment. Women bear a large part of this burden. Urban development strategies to benefit the urban poor while promoting gender equity are highlighted, including an added emphasis on the provision of social and educational infrastructure within closer proximity to peripheral residential areas, coupled with better pedestrian access.     

Determinants of full and partial household evacuation decision making in hurricane matthew

This paper adds partial household evacuation to the traditional binary evacuate/stay decision. Based on data from a survey of Jacksonville, FL residents after Hurricane Matthew, multinomial (MNL) and random parameter MNL models were developed to determine the influential factors and whether some variables’ effects are more nuanced than prior literature suggests. The random parameter model was preferred to the fixed parameters model. Variables significant in this model included injury concern, certainty about hurricane impact location, age, marital status, family cohesion, and living in mobile or detached homes. Greater injury concern results in lower likelihood of none of the household evacuating and greater likelihood of partial evacuation, but lower likelihood of full household evacuation. Similarly, greater certainty about hurricane impact increased the probability of partial household evacuation but decreased the probability of full evacuation. Respondent age had heterogenous effects; for 85.54% of respondents, additional years of age increased the likelihood of the household staying. Married households had a higher likelihood of staying or evacuating together. Similarly, greater family cohesion was associated with the household remaining together. Living in mobile homes decreased the likelihood that all of the household stays or evacuates and increased the probability of partial household evacuation. Living in a single-family detached home was associated with lower likelihood of all of the household staying or evacuating and a greater likelihood of a partial household evacuation. These findings can inform strategies that influence full or partial household evacuations, material requirements based on these decisions, and ways to reduce family risk.     

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.     

Rural mobility and mode choice: Evidence from the 2001 National Household Travel Survey

This article uses data from the 2001 National Household Travel Survey (NHTS) to compare travel behavior in rural and urban areas of the U.S. As expected, the car is the overwhelmingly dominant mode of travel. Over 97% of rural households own at least one car vs. 92% of urban households; 91% of trips are made by car in rural areas vs. 86% in urban areas. Regardless of age, income, and race, almost everyone in rural areas relies on the private car for most travel needs. Mobility levels in rural areas are generally higher than in urban areas. That results from the more dispersed residences and activity sites in rural areas, which increase trip distances and force reliance on the car. Somewhat surprisingly, the rural elderly and poor are considerably more mobile than their urban counterparts, and their mobility deficit compared to the rural population average is strikingly less than for the urban elderly and poor compared to the urban average. Data limitations prevented a measurement of accessibility, however, and it seems likely that rural areas, by their very nature, are less accessible than urban areas, especially for the small percentage of car-less poor and elderly households.     

Trip generation of vulnerable populations in three Canadian cities: a spatial ordered probit approach

This paper provides an analysis of trip generation of three vulnerable groups: single-parent families, low income households, and the elderly. It compares the mobility of these groups to that of the general population in three Canadian urban areas of Hamilton, Montreal and Toronto, based on data from large-sample metropolitan transport surveys. An ordered probit model with spatially expanded coefficients is used for the analysis. Spatial expansion shows that there are spatial mobility trends for elderly populations and low-income populations even after socio-economic attributes are accounted for. Such spatial differences are not generally found for single parent families. This novel spatial analysis provides clues as to where vulnerable populations may experience greater degrees of social exclusion. It provides information to help prioritize transportation infrastructure projects or other social programs to take into account the needs of vulnerable populations with the lowest levels of mobility.     

Analysis of child pick-up during daily routines and for daytime no-notice evacuations

In a no-notice disaster (e.g., nuclear explosion, terrorist attack, or hazardous materials release), an evacuation may start immediately after the disaster strikes. When a no-notice evacuation occurs during the daytime, household members are scattered throughout the regional network, and some family members (e.g., children) may need to be picked up. This household pick-up and gathering behavior was seldom investigated in previous work due to insufficient data; this gap in our understanding about who within families handles child-gathering is addressed here. Three hundred fifteen interviews were conducted in the Chicago metropolitan area to ascertain how respondents planned their response to hypothetical no-notice emergency evacuation orders. This paper presents the influencing factors that affect household pick-up and gathering behavior/expectations and the logistic regression models developed to predict the probability that parents pick up a child in three situations: a normal weekday and two hypothetical emergency scenarios. The results showed that both mothers and fathers were more likely to pick up a child under emergency conditions than they were on a normal weekday. For a normal weekday, increasing the distance between parents and children decreased the probability of parents picking up children; in other words, the farther parents are from their children, the less likely they will pick them up. In an emergency, effects of distance on pick-up behavior were significant for women, but not significant for men; that is, increasing the distance between parents and children decreased the probability that mothers pick up a child, but had a less significant effect on the fathers’ probability. Another significant factor affecting child pick-up behavior/expectations was household income when controlling for distance. The results of this study confirm that parents expect to gather children under emergency conditions, which needs to be accounted for in evacuation planning; failure to do so could cause difficulties in executing the pick-ups, lead to considerable queuing and rerouting, and extend the time citizens are exposed to high levels of risk.     

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.     

The effects of ozone action day public advisories on train ridership in Chicago

Ground level ozone is a criteria pollutant that is significantly affected by transportation patterns. Ozone action day advisories represent one type of voluntary ozone-abating program operating in urban areas where ozone pollution is concentrated. When forecasts predict that ground level ozone will exceed healthy levels, public advisories urge citizens to voluntarily choose public transportation as a means of eliminating automobile trips and reducing mobile emissions. To obtain credit for emission reductions spurred by voluntary programs, states must provide verifiable reduction estimates. This paper applies a fixed effects regression model to a panel of hourly Chicago Transit Authority train ridership data to evaluate the potential effects of Ozone Action Day advisories in Chicago from 2002 to 2003. Findings show that while the overall effect of ozone action days on ridership is not significant, there are statistically significant changes in hourly ridership patterns that indicate a more complex relationship between the public advisories and travel behavior.     

Measuring post-disaster transportation system performance: the 1995 Kobe earthquake in comparative perspective

Recent earthquake disasters have caused major damage to transportation networks, leading to significant economic disruption. While this suggests the need to evaluate total system performance in transportation risk assessment, in addition to examining the vulnerability of individual components such as bridges, no appropriate measures currently exist. This paper develops post-disaster system performance measures and applies them to the urban rail and highway transportation systems in the Kobe, Japan, region devastated by the 1995 Hyogoken–Nanbu earthquake. Performance is evaluated in terms of network coverage and transport accessibility. Performance degradation was much more severe for highways and railways than for other lifeline infrastructure systems. Both transportation systems fared poorly in the disaster but service restoration proceeded much more rapidly for rail. The restoration of highway system performance correlated closely with the recovery of highway traffic volumes. The paper further develops a measure of subarea transport accessibility and applies this to Kobe’s constituent city wards. Results indicate substantial spatial disparity that is maintained throughout the restoration period. Comparisons with the 1989 Loma Prieta and 1994 Northridge earthquakes in the US show that although these disasters caused notable damage to highway bridges, system performance degradation was small in comparison with the Kobe experience. The paper argues that explicitly measuring transportation system performance can greatly facilitate both understanding the effects of historic disasters and preparing for future hazard events.     

Spatial structure and mobility

The aim of this paper is to contribute to a clearer understanding of the extent to which the spatial structure and planning of the residential environment can explain mobility, in general, and the choice of mode of transport, in particular, and what spatial planning and traffic management aspects play a significant role in this. The research showed that certain aspects of the planned environment do indeed have a clear impact on mobility. These effects are particularly apparent in trips made for shopping and social or recreational purposes. It is mainly personal characteristics that largely or almost entirely determine commuter traffic. An integral approach to the planning of residential areas is required to achieve any great changes in mobility. Only then may we expect spatial planning to have any significant impact on car use.     

Hurricane evacuation demand models with a focus on use for prediction in future events

Although substantial literature exists on understanding hurricane evacuation behavior, few studies have developed models that can be used for predicting evacuation rates in future events. For this paper, we develop new ordered probit models for evacuation using survey data collected in the hurricane-prone state of North Carolina in 2011 and 2012. Since all covariates in the models are available from the census or based on location, the new models can be applied to predict evacuation rates for any future hurricane. The out-of-sample predictive power of the new models are evaluated at the individual household level using cross validation, and the aggregated level using available data from Hurricane Irene (2011), Hurricane Isabel (2003) and Hurricane Floyd (1999). Model results are also compared with an existing participation rate model, and a logistic regression model available from the literature. Results at the individual household level suggests approximately 70% of households’ evacuation behavior will be predicted correctly. Errors are evenly divided between false positives and false negatives, and with accuracy increasing to 100% as the percentage of people who actually evacuate goes to zero or all and decreasing to about 50% when the population is divided and about half of all households actually evacuate. Aggregate results suggest the new models compare favorably to the available ones, with average aggregate evacuation rate errors of five percentage points.     

Location privacy preferences: A survey-based analysis of consumer awareness,trade-off and decision-making

With the advent and rapid dissemination of location-sensing information technology, the issue of location information privacy is receiving growing attention. Perhaps of greatest concern is ensuring that potential users of mobile Information and Communications Technologies (e.g., Location-Based Services and Intelligent Transportation Systems) are comfortable with the levels of privacy protection afforded them, as well as with the benefits they will receive in return for providing private location information. This paper explores the concepts of privacy risks, benefits, willingness to trade, and compensation in relationship to mobile and locational technologies using a stated preference survey to ascertain areas of interest in determining the trade-offs that consumers will be willing to make in return for mobility enhancements. Analysis of the survey leads to findings that while respondents believe that sharing data in the mobile environment may pose privacy risks, they do not generally take steps necessary to address these risks; that privacy preferences are impacted by a range of factors, including both personal and contextual considerations (such as factors arising from their specific situation at the time of information seeking); and that willingness to trade private location data is dependent upon a number of factors related to context, personal characteristics, expected benefits and degree of trust in the collecting organization.     

Travel,location and race/ethnicity

This paper examines the relationship between residential location and travel among ethnic and racial population segments using the 1995 US Nationwide Personal Transportation Survey (NPTS) data. Using measures of total daily travel, I explore the effects of metropolitan location and local neighborhood characteristics. I estimate models which test for both independent and interaction effects of race/ethnicity. I find that there are significant differences in travel characteristics across race/ethnic groups. There are independent effects associated with race/ethnicity. Individual characteristics such as sex, age and household income, have different effects on daily travel. Metropolitan location is not related to total daily travel. However, residential neighborhood characteristics are related and have different effects across race/ethnic groups. I conclude that our understanding of travel behavior is largely an understanding of the white majority population, which dominates analysis when race/ethnicity is not explicitly considered.     

Transportation and income inequality in China: 1978–2007

This paper mainly studies how various types of transportation modes are associated with income inequality in China for the years between 1978 and 2007. Gini coefficients are used to measure the income inequality nationwide, and within urban and rural areas of China. Factors other than transportation are also considered in the regression model, including a few demographic variables and major economic indicators. We contribute to the literature by examining the income redistributive effects of transport infrastructure and services, and providing transport planning and policy guidance which may alter the orientation of public policy and help alleviate the growing social and economic imbalance in China.     

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.     

Trade-offs between mobility and equity maximization under environmental capacity constraints: A case study of an integrated multi-objective model

This paper investigates the performance of a policy decision tool proposed for multi-objective decision under different policy interventions. This tool deals with the trade-off between mobility and equity maximization under environmental capacity constraints. Two system objectives, maximization of mobility and equity, are formulated in terms of the sum of total car ownership and number of trips, and the differences in accessibility between zones. Environmental capacities are based on production efficiency theory in which the frontier emission under maximum system efficiency is taken as environmental capacity. To examine the performance of the proposed model, three types of hypothetical policies (network improvement, population increase and urban sprawl) are formulated. Effects are simulated using data pertaining to Dalian City, China. Results show that the proposed model is capable of representing the trade-offs between mobility and equity based on different policy interventions. Compared with two extreme cases with the single objective of mobility maximization or equity maximization, the Pareto-optimal solutions provide more interesting practical options for decision makers. Taking the solution based on the maximum equity as an example, the policy of urban sprawl yields the most significant improvement in both emission and accessibility of the three scenarios.     

Poverty,policy, and public transportation

By all appearances, the circumstances surrounding employment and income distribution in the United States have remained notably the same over the past 30–40 years. At the same time, policies for improving the conditions of low-income persons have remained relatively unchanged. Relevant published accounts continue to cite poorly integrated residential and employment location patterns and poor public transportation service as critical obstacles to improving the economic and social conditions of low-income persons. The relationship between poverty and public transportation was researched extensively during the late 1960s and the early 1970s; however, little recognition has been given to these efforts by more recent research efforts. To learn from the past we should review public transportation policies from 1960 to 2000 to highlight federal policies that affected urban areas during this time period, especially in relation to low-income transportation mobility.