Human exit choice in crowded built environments: Investigating underlying behavioural differences between normal egress and emergency evacuations

Haghani, Milad; Sarvi, Majid

doi:10.1016/j.firesaf.2016.07.003

Cited by 145 publications

(58 citation statements)

References 52 publications

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“…There are, however, particular characteristics of the crowd that pose challenges to the analyst to tackle the complexity of crowd modelling. We itemise those characteristics as (i) intrinsic complexity of human behaviour [3][4][5] and heterogeneity of individuals as the main constituent elements of a crowd [6]; (ii) continuity and the dynamic nature of the space of movement [7]; and (iii) scarcity of reliable explanatory data (especially at disaggregate level) for the purposes of phenomena understanding, model estimation, calibration and validation [8,9]. This paper addresses tactical level of pedestrian crowd decision making.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian crowd tactical‐level decision making during emergency evacuations

Haghani

Sarvi

2016

J of Advced Transportation

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This paper investigates pedestrian crowd tactical-level decision making during emergency evacuations. Of particular interest is crowd exit-choice behaviour. Two sources of stated choice data are collected and combined. One data set is derived from an experiment linked to a real-life exit choice experience of participants (in a non-evacuation setting). We examine aspects that have often been taken for granted in the literature in connection with egress behaviour of crowds during emergencies. We quantify evacuees' trade-off between "distance", "density", "exit visibility" and "directional density" as well as the interactive effect between exit visibility and tendency to follow others. A comprehensive random-utility analysis is conducted ranging from traditionally practiced models to the state-of-the-practice methods such as random-coefficient nested logit. Our findings suggest that (i) unless evacuees face certain levels of uncertainty in the escape environment; flows of crowd are unlikely to be followed. Otherwise, most evacuees perceive other individuals as potential sources of congestion and extra delay (generalisation to situations where crowd is completely unfamiliar with the egress geometry, however, may require careful scrutiny). (ii) Evacuees mostly prefer visible exits over the exits whose congestion level is unknown to them (i.e. the tendency to minimise ambiguity). (iii) The presence of attribute uncertainty (e.g. exit visibility) significantly changes the impact of observing decisions of others on each individual choice maker. We also found out that (iv) spatial distribution of exits has a significant influence on evacuees' decisions (presenting itself in the form of violating the IIA assumption). (v) The marginal weights that different individuals place upon attributes of exits are significantly heterogeneous. (vi) There is meaningful correlation between certain utility weights of individual evacuees. These behavioural findings can provide significant behavioural insight essential for safe evacuation planning and accurate forecast of evacuees' behaviour.All other model estimation results were obtained from NLOGIT 5.

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Section: Introductionmentioning

confidence: 99%

Pedestrian crowd tactical‐level decision making during emergency evacuations

Haghani

Sarvi

2016

J of Advced Transportation

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“…It essentially aims at developing reliable forecast tools that enable planners to reduce the likelihood of such disasters by conducting virtual (or simulated) evaluations on the design and performance of public crowded facilities. Despite major advances in this field, it is still thought of as an intractable problem many fundamental aspects of which are yet to be investigated [5]. The intrinsic complexity and variability of human behavior (as the constituent elements of crowds), the variety of circumstances and geometric features that need to be dealt with (ranging from large open areas, to complex confined spaces such as public transportation facilities or high-rise buildings), and the variety of contexts (such as entertainment events, sport events, political protests; or unanticipated emergency situations) that can give rise to different forms of behaviors in a crowd are among the primary challenges involved [6].…”

Section: Introductionmentioning

confidence: 99%

“…With regard to knowing how evacuees make their global navigation decisions, fundamental behavioral questions are yet to be addressed as to (1) the factor(s) that contribute to exit decisions [5], (2) the way tradeoffs are made between them [31]; and (3) whether those factors and their influences dramatically differ from person to person [32]. The insufficiency of the behavioral data has thus far hindered making solid inferences from the existing literature to answer these questions.…”

Section: Introductionmentioning

confidence: 99%

How Simple Hypothetical-Choice Experiments Can Be Utilized to Learn Humans’ Navigational Escape Decisions in Emergencies

et al. 2016

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How humans resolve non-trivial tradeoffs in their navigational choices between the social interactions (e.g., the presence and movements of others) and the physical factors (e.g., spatial distances, route visibility) when escaping from threats in crowded confined spaces? The answer to this question has major implications for the planning of evacuations and the safety of mass gatherings as well as the design of built environments. Due to the challenges of collecting behavioral data from naturally-occurring evacuation settings, laboratory-based virtual-evacuation experiments have been practiced in a number of studies. This class of experiments faces the traditional question of contextual bias and generalizability: How reliably can we infer humans’ behavior from decisions made in hypothetical settings? Here, we address these questions by making a novel link between two different forms of empirical observations. We conduct hypothetical emergency exit-choice experiments framed as simple pictures, and then mimic those hypothetical scenarios in more realistic fashions through staging mock evacuation trials with actual crowds. Econometric choice models are estimated based on the observations made in both experimental contexts. The models are contrasted with each other from a number of perspectives including their predictions as well as the sign, magnitude, statistical significance, person-to-person variations (reflecting individuals’ perception/preference differences) and the scale (reflecting context-dependent decision randomness) of their inferred parameters. Results reveal a surprising degree of resemblance between the models derived from the two contexts. Most strikingly, they produce fairly similar prediction probabilities whose differences average less than 10%. There is also unexpected consensus between the inferences derived from both experimental sources on many aspects of people’s behavior notably in terms of the perception of social interactions. Results show that we could have elicited peoples’ escape strategies with fair precision without observing them in action (i.e., simply by using only hypothetical-choice data as an inexpensive, practical and non-invasive experimental technique in this context). As a broader application, this offers promising evidence as to the potential applicability of the hypothetical-decision experiments to other decision contexts (at least for non-financial decisions) when field or real-world data is prohibitively unavailable. As a practical application, the behavioral insights inferred from our observations (reflected in the estimated parameters) can improve how accurately we predict the movement patterns of human crowds in emergency scenarios arisen in complex spaces. Fully-generic-in-parameters, our proposed models can even be directly introduced to a broad range of crowd simulation software to replicate navigation decision making of evacuees.

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“…We consider a number of different experiments of varying complexity. More specifically, we consider two experiments: the crossing flow experiment from Hoogendoorn and Daamen (2005), and the merging flow experiment described in Haghani and Sarvi (2016).…”

Section: Experimental Analysesmentioning

confidence: 99%

“…Each run of the experiments was repeated twice and in total, 20 runs of trials were performed. More information on these experiments can be found in Haghani and Sarvi (2016).…”

Section: Merging Flow Experimentsmentioning

confidence: 99%

Macroscopic Fundamental Diagram for pedestrian networks: Theory and applications

Hoogendoorn

Daamen

Knoop

et al. 2018

Transportation Research Part C: Emerging Technologies

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The Macroscopic Fundamental diagram (MFD) has proven to be a powerful concept in understanding and managing vehicular network dynamics, both from a theoretical angle and from a more application-oriented perspective. In this contribution, we explore the existence and the characteristics of the pedestrian Macroscopic Fundamental Diagram (p-MFD). From a theoretical perspective, the main contribution of this research shows how we can derive the p-MFD from assumed local fundamental diagrams (FDs). We show that we can relate the average (out-) flow from a pedestrian network as a function of the average spatial density and the density spatial variation . We show that the latter is essential to provide a reasonable description of the overall network conditions. For simple linear relations between density and speed, we derive analytical results; for more commonly used FDs in pedestrian flow theory we show the resulting relation using a straightforward simulation approach. As a secondary contribution of the paper, we show how the p-MFD can be constructed from pedestrian trajectory data stemming from either microsimulation or from experimental studies. The results found are in line with the theoretical result, providing further evidence for the validity of the p-MFD concept. We furthermore discuss concepts of hysteresis, due to the differences in the queue build up and recuperation phases. We end with applications of the presented concepts, e.g. in crowd management. AbstractThe Macroscopic Fundamental diagram (MFD) has proven to be a powerful concept in understanding and managing vehicular network dynamics, both from a theoretical angle and from a more application-oriented perspective. In this contribution, we explore the existence and the characteristics of the pedestrian Macroscopic Fundamental Diagram (p-MFD). From a theoretical perspective, the main contribution of this research shows how we can derive the p-MFD from assumed local fundamental diagrams (FDs). We show that we can relate the average (out-) flow from a pedestrian network as a function of the average spatial density and the density spatial variation . We show that the latter is essential to provide a reasonable description of the overall network conditions. For simple linear relations between density and speed, we derive analytical results; for more commonly used FDs in pedestrian flow theory we show the resulting relation using a straightforward simulation approach. As a secondary contribution of the paper, we show how the p-MFD can be constructed from pedestrian trajectory data stemming from either microsimulation or from experimental studies. The results found are in line with the theoretical result, providing further evidence for the validity of the p-MFD concept. We furthermore discuss concepts of hysteresis, due to the differences in the queue build up and recuperation phases. We end with applications of the presented concepts, e.g. in crowd management.

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Human exit choice in crowded built environments: Investigating underlying behavioural differences between normal egress and emergency evacuations

Cited by 145 publications

References 52 publications

Pedestrian crowd tactical‐level decision making during emergency evacuations

Pedestrian crowd tactical‐level decision making during emergency evacuations

How Simple Hypothetical-Choice Experiments Can Be Utilized to Learn Humans’ Navigational Escape Decisions in Emergencies

Macroscopic Fundamental Diagram for pedestrian networks: Theory and applications

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