Leveraging social networks for efficient hurricane evacuation

One of the important factors affecting evacuation performance is the departure time choices made by evacuees. Simultaneous departures of evacuees can lead to overloading of road networks causing congestion. We are especially interested in cases when evacuees subject to little or no risk of exposure evacuate along with evacuees subject to higher risk of threat (also known as shadow evacuation). One of the reasons for correlated evacuee departures is higher perceived risk of threat spread through social contacts. In this work, we study an evacuation scenario consisting of a high risk region and a surrounding low risk area. We propose a probabilistic evacuee departure time model incorporating both evacuee individual characteristics and the underlying evacuee social network. We find that the performance of an evacuation process can be improved by forcing a small subset of evacuees (inhibitors) in the low risk area to delay their departure. The performance of an evacuation is measured by both average travel time of the population and total evacuation time of the high risk evacuees. We derive closed form expressions for average travel time for ER random network. A detailed experimental analysis of various inhibitor selection strategies and their effectiveness on different social network topologies and risk distribution is performed. Results indicate that significant improvement in evacuation performance can be achieved in scenarios where evacuee social networks have short average path lengths and topologically influential evacuees do not belong to the high risk regions. Additionally, communities with stronger ties improve evacuation performance.     

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.     

Online calibration of an integrated framework for information‐based evacuation operations

This study seeks to online calibrate the parameters of aggregate evacuee behavior models used in a behavior‐consistent information‐based control module for determining information strategies for real‐time evacuation operations. It enables the deployment of an operational framework for mass evacuation that integrates three aspects underlying an evacuation operation: demand (evacuee behavior), supply (network management), and disaster characteristics. To attain behavior‐consistency, the control module factors evacuees' likely responses to the disseminated information in determining information‐based control strategies. Hence, the ability of the behavior models to predict evacuees' likely responses is critical to the effectiveness of traffic routing by information strategies. The mixed logit structure is used for the aggregate behavior models to accommodate the behavioral heterogeneity across the population. An online calibration problem is proposed to calibrate the random parameters in the behavior models by using the least square estimator to minimize the gap between the predicted network flows and unfolding traffic dynamics. Background traffic, an important but rarely studied issue for modeling evacuation traffic, is also accounted for in the proposed problem. Numerical experiments are conducted to illustrate the importance of the calibration problem for addressing the system consistency issues and integrating the demand, supply, and disaster characteristics for more efficient evacuation operations. Copyright © 2016 John Wiley & Sons, Ltd.     

Modelling car trip generations for UK residential developments using data from TRICS

Abstract

This paper models trip generation for a cross-section of residential developments around the UK. Consistent with recent literature, the empirical model tests whether trip making patterns for residential developments are independent of car ownership and finds that trip generation is dependent upon car ownership socio-economic factors and site-specific characteristics, in particular land-zone type (e.g. town centre, out of town, etc.). However, public transport services are not found to have a significant relationship with trip generation; consequently, a policy implication of the results is that increasing bus services to residential developments is not associated with a reduction in generated trips.     

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.     

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.     

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.     

Reserve capacity model based on variable demand for land-use development control

In this paper, the concept of reserve capacity has been extended to zone level to measure the land-use development potentiality of each trip generation zone. Bi-level programing models are proposed to determine the signal setting of individual intersections for maximizing possible increase in total travel demand and the corresponding reserve capacity for each zone. The change of the origin–destination pattern with the variation of upper level decision variables is presented through the combined distribution/assignment model under user equilibrium conditions. Both singly constrained and doubly constrained combined models are considered for different trip purposes and data information. Furthermore, we have introduced the continuous network design problem by increasing road capacity and examined its effect on the land-use development potentiality of trip generation zone. A numerical example is presented to illustrate the application of the models and how a genetic algorithm is applied to solve the problem.     

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.     

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.     

A structural equations analysis of commuters' activity and travel patterns

An exploratory analysis of commuters' activity and travel patterns was carried out using activity-based travel survey data collected in the Washington, DC metropolitan area to investigate and estimate relationships among socio-demographics, activity participation, and travel behavior. Structural equations modeling methodology was adopted to determine the structural relationships among commuters' demographics, activity patterns, trip generation, and trip chaining information. Three types of structural equations model systems were estimated: one that models relationships between travel and activity participation, another that captures trade-offs between in-home and out-of-home activity durations, and a third that models the generation of complex work trip chains. The model estimation results show that strong relationships do exist among commuters' socio-demographic characteristics, activity engagement information, and travel behavior. The finding that significant trade-offs exist between in-home and out-of-home activity participation is noteworthy in the context of in-home vs. out-of-home substitution effects. Virtually all of the results obtained in this paper corroborate earlier findings reported in the literature regarding relationships among time use, activity participation, and travel. This revised version was published online in June 2006 with corrections to the Cover Date.     

Structural equation approach to investigate trip-chaining and mode choice relationships in the context of developing countries

This paper investigates empirical relationships between trip chain type and mode class choice for developing countries. To formulate these two sets of decisions, four empirical models are developed using structural equation modeling (SEM). Those models are calibrated using one-month travel diary data collected in Dhaka city. SEM correlates the observed variables and identifies their relationship with trip-chaining type utility and mode class choice utility. The fitted models are selected based on statistical results and similarity with the real-life situation. Direct relationships between trip-chaining and mode choice utilities are found insignificant. However, several socio-demographic factors influence both simultaneously. Consequently, it is essential to consider mode class choice concurrently for modeling trip chains. This study also investigates the influencing factors for work-based and non-work-based trip chains separately and effects of road users’ heterogeneity. The research results can be utilized to perceive trip chain-mode choice patterns for developing countries.     

The Dublin transportation study

This article deals with the Transportation Study currently nearing completion in Dublin. A feature of this Study was the use of simplified data collection and modelling techniques. Beginning with a brief outline of the background to the transportation problem in Dublin, the article goes on to outline the objectives of the Study and the methods by which these objectives were fulfilled. These methods involved the taking of detailed inventories of Dublin's travel patterns, of its land uses, population and employment, and of its road and public transport systems. Mathematical models were then developed and modified until they could simulate the existing travel patterns to an acceptable degree of accuracy. These models covered the Study's trip generation, modal split, trip distribution and trip assignment stages, and the forms taken by the models are dealt with in the article. The article ends with a summary of the main recommendations of the Dublin Transportation Study.     

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.     

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.     

Exploring public transport usage trends in an ageing population

An ageing population remains one of the most significant challenges for Western society in the 21st century. Whilst public transport use has attractive sustainability features for older generations there is mixed evidence with regard to trends in travel and public transport use in ageing societies. This paper explores public transport trip rates amongst older age groups using travel survey evidence collected from a household travel survey in Melbourne, Australia for the period 1994 to 1999. A particular aim of the research was to establish trends in trip rates so as to explore the impact of the ageing Baby Boomer generation on travel by public transport. The results suggested that compared to those aged below 60, those aged over 60 years demonstrated 30% lower trip making overall and 16% lower public transport trip rates. Longitudinal trends in trip rates showed those aged over 60 had a very small decline in trip rates by public transport (−0.004 average daily trips per annum) but increasing rates for car trips. A further analysis showed a small but significant increase in longitudinal trip rates of public transport use amongst Baby Boomers (0.004 daily trips p.a., p < .05) while car usage for Baby Boomers was steady. The implication of these findings is that trends in the existing over 60s population are not necessarily going to flow through to behaviour patterns in the Baby Boomer generations. The Baby Boomer age group showed longitudinal trends in travel behaviour which contrasted with those of the existing over 60s generation notably with a trend towards increased public transport usage.     

An empirical comparison of disaggregate category and regression trip generation analysis techniques

Category and regression household trip generation analysis techniques were compared and contrasted. The comparative research was facilitated through a discussion that revealed the interchangeability of two methods of calibrating a category model. While the cell mean method is simple to implement, it does not readily yield statistical indexes for comparison with regression models. The general linear model analysis of variance (GLANOVA) readily provides statistical indexes for the comparison of category and regression trip generation models, and it produces identical empirical results to the simpler cell mean approach of calibrating a category model.The empirical comparison supports the widespread use of category models for trip generation analysis in transportation planning studies. It was found that regression and category models yielded equivalent results for typical planning applications at the district level of aggregation. In addition, both techniques estimated overall trip rate with equal accuracy in the calibration phase, and the two approaches were indistinguishable with respect to sample size sensitivity. However, households with extremely large trip rates were underestimated to a greater degree by category models than regression models. This tendency, in turn, resulted in larger calibration coefficients of determination for regression models. Since the cell mean method of calibrating a model is simpler and easier to understand than a regression model representation, category models can be recommended over regression models for planning studies.     

Is accessibility relevant in trip generation? Modelling the interaction between trip generation and accessibility taking into account spatial effects

The influence of accessibility to opportunities in trip generation continues to be debated in the specialised literature given its relevance to simulate phenomena such as induced demand. This article estimates multiple linear regression models (MLR), spatial autoregressive models (SAR), spatial autoregressive models in the error term (SEM) and spatially filtered Poisson regression models (SPO) to discover whether or not accessibility is a significant factor in trip generation using data from the urban area of Santander (Spain). The results obtained provide evidence which shows that, on an intraurban scale, more accessibility to opportunities decreases trip production in private vehicle for work purpose, whereas it increases trip production in other transport modes for non—mandatory purposes. For the correct interpretation of the estimated parameters it was important to consider the direct and indirect effects of the independent variables in the SAR production models. Finally, the validation of the models showed that the SAR and SEM models had a mean squared error slightly lower than the MLR models in predicting overall trip production. This was because the spatial models reduced the correlation of the residuals present in the MLR models. Furthermore, the SPO models performed better in validation mode than all the continuous models.     

Evacuation traffic dynamics

Historically, evacuation models have relied on values of road capacity that are estimated based on Highway Capacity Manual methods or those observed during routine non-emergency conditions. The critical assumption in these models is that capacity values and traffic dynamics do not differ between emergency and non-emergency conditions. This study utilized data collected during Hurricanes Ivan (2004), Katrina (2005) and Gustav (2008) to compare traffic characteristics during mass evacuations with those observed during routine non-emergency operations. From these comparisons it was found that there exists a consistent and fundamental difference between traffic dynamics under evacuation conditions and those under routine non-emergency periods. Based on the analysis, two quantities are introduced: “maximum evacuation flow rates” (MEFR) and “maximum sustainable evacuation flow rates” (MSEFR). Based on observation, the flow rates during evacuations were found to reach a maximum value of MEFR followed by a drop in flow rate to a MSEFR that was able to be sustained over several hours, or until demand dropped below that necessary to completely saturate the section. It is suggested that MEFR represents the true measure of the “capacity”. These findings are important to a number of key policy-shaping factors that are critical to evacuation planning. Most important among these is the strong suggestion of policy changes that would shift away from the use of traditional capacity estimation techniques and toward values based on direct observation of traffic under evacuation conditions.     

Truck trip generation in small- and medium-sized urban areas

This paper describes procedures to develop truck trip generation (TTG) rates for small- and medium-sized urban areas and its implications. Ordinary least squares models are used to develop separate truck production and attraction equations with the number of employees as the independent variable for three industrial groups – retail, transportation and warehousing, and manufacturing. Results from this research indicate that number of employees is a statistically significant predictor, and has significant explanatory power in predicting the number of truck trips produced and attracted. The rates developed in this study are also found to be significantly different from rates developed in other studies with the implication that caution needs to be taken when transferring TTG rates. The rates are applied in a travel demand model as the initial step of incorporating truck traffic into the modeling process.