Milad Haghani

Estimation and application of a multi-class multi-criteria mixed paired combinatorial logit model for transport networks analysis

Probabilistic approach of transport network modelling has received significant attention in recent years. Despite the recent progress in this area, the full advantage of the potential capability of random utility choice models has not yet been fully realised. This research is intended to introduce a new approach of combining the state-of-the-art paired combinatorial logit route choice modelling and random coefficient choice models in traffic assignment. While the former addresses the problem of correlation among path utilities, the latter can capture the random taste heterogeneity in route choice decision-making. Including an additional monetary cost explanatory variable, the model would be able to assess a broader range of planning policies such as road pricing. In addition, distinguishing multiple classes of decision-makers, the model has allowed the introduction of demographic aspects of travellers into a network analysis process. Results showed a considerable difference between the flow patterns predicted by the proposed model and the traditional models.

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

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.

Modeling Pedestrian Crowd Exit Choice Through Combining Sources of Stated Preference Data

One crucial aspect of pedestrian behavior when a facility is being evacuated is exit selection. This phenomenon, however, is difficult to capture. Recording revealed choices as the exact situations or moments in which individuals make or change their subconscious exit decisions when evacuating a place is highly ambiguous. The approach in which stated choice data are collected offers an appealing solution to tackle the problem. For the underlying factors that influence peoples exit decisions to be examined, two types of stated preference (SP) data were collected and pooled: traditional stated preference data and stated preference–off–revealed preference (RP) data. The latter is from the state-of-the-art class of stated choice methods that design experiments with reference to an alternative in an individuals actual choice set. The nested logit trick model and a customized version of the generalized mixed multinomial logit model were applied to estimate the difference in variance scale of the two sectors of data and to quantify the relative contribution of the factors of distance, density, visibility, and herding behavior to exit decisions. Results showed that the SP-off-RP method, compared with the classical SP method, led to lower variance for random noise by a small margin. Compared with the nested logit trick method, the generalized mixed multinomial logit approach allowed researchers to consider more behavioral dimensions of the problem as well as accommodate the difference in scale of variance, including heterogeneity in utility weights and utility scale of individuals, correlation between alternatives, correlation of unobserved utility factors over time, and correlation between utility coefficients.

Group and Single Pedestrian Behavior in Crowd Dynamics

An analytical study was proposed in this paper to explore various behavioral aspects of group and single pedestrians. The results were obtained from reviewing the literature and studying primary data. Data collected from field surveys, direct observations, and video recordings were analyzed to produce the following findings. For crowds with densities between 0.4 and 0.8 persons per square meter, individuals walked faster than groups, on average. There was also significant evidence to suggest that individuals were likely to have more trajectory changes than groups. As the crowd density got higher, the likelihood of pedestrians splitting from groups of two decreased. With higher crowd densities, the rate at which split members came back to the group decreased. More individual pedestrians chose spacious stairs; groups of two were more likely to go for escalators.

Quantifying benefits of traveler information systems to performance of transport networks prior to implementation: a double-class structured-parameter stochastic trip assignment approach

Predicting benefits of advanced traveler information systems before implementation is one of the challenges in the area of transport modeling. Taking into consideration the differences in commuting behavior of unequipped and would be equipped drivers, as well as their different level of perception error are the key factor. Accordingly, it seems that the multi-class approach of traffic assignment (TA) can be regarded as a possible solution to the problem. However, dealing with the challenge of lack of observed data before system installation is still a major challenge. To deal with this problem, a double-class stochastic TA approach is proposed in this work. The network loading procedure follows a paired combinatorial logit (PCL) model, which addresses the classical problem of path overlapping. In addition, the model is origin-destination (OD)-specific parameter, which enables the modeler to represent different levels of uncertainty and stochasticity involved in route decision-making between different OD pairs. A heuristic practical estimation method is also proposed, which exempts the modeler from resorting to route choice data and facilitates the challenges involved in estimation of route choice models to a considerable extent. Furthermore, in the approximate proposed method of estimation, the new perspective from which the estimation parameter is considered provides a more tangible interpretation than that of the classical approach. It allows manipulation of data to obtain some sort of synthesized information as to the route choice behavior of prospective equipped travelers. The estimation method is applied to an experimental data set and the TA method is tested on an illustrative network. Authors demonstrate that, given the market penetration of the system, how the analyst would be able to provide quantitative forecasts as to the expected improvements in the network performance as a result of being introduced to advanced travelers information systems.

Pedestrian Crowd Dynamics Observed at Merging Sections: Impact of Designs on Movement Efficiency

The need for reliable crowd simulation tools has necessitated an accurate understanding of human behavior and the rules that govern their movements under normal and emergency escapes. This paper investigates the dynamics of merging streams of pedestrians. In the merging sections, the interaction between pedestrians and geometric features of merging sections can significantly impede the collective motion and can increase the possibility of flow breakdown, particularly under emergency conditions. Therefore, to create safe and efficient designs, it is important to study human movement characteristics associated with these types of conflicting geometries. In this study, empirical data collected from large numbers of high-density experiments with people at different desired speed levels were used to explore the effect of different merging configurations (i.e., design and angle) on dynamics of merging crowds. For the first time, this study examined the impact of elevated speed regimes (as a behavioral proxy of emergency escapes) on the movement efficiency of crowds in merging sections with different geometric designs. In particular, this study investigated the impact of these conflicting geometric settings on the average waiting time in the system as a measure of movement efficiency. Results suggest that the experienced delay is dramatically greater in asymmetrical setups compared with the delay in symmetrical setups and that the difference is even more pronounced at elevated levels of pedestrians’ desired speed. These findings give significant insights into the implications of inefficient designs of merging sections for pedestrians’ safety, notably when quick movement of crowds is necessary (e.g., in emergencies).

Simulating Indoor Evacuation of Pedestrians: The Sensitivity of Predictions to Directional-Choice Calibration Parameters

The increasing occurrence of safety-related incidents like fire and terror attacks in crowded public facilities and mass gatherings has heightened the importance of planning for efficient evacuations through optimizing evacuation routes and architectural designs. This calls for the development of simulation and analytical tools that can replicate occupants’ responses and thereby their most likely movement patterns. Such models must be accurate to prevent inappropriate design and planning. One major factor connected to prediction accuracy is the sensitivity of modeling outputs to the value of their various parameters. We report on implementation of a calibrated model of directional choices in a microscopic simulation model of pedestrians’ evacuation. We show how estimates of the aggregate measures of prediction are sensitive to the parameters of this tactical level (i.e., directional choice) model. Results demonstrate that the prediction of the total evacuation time and average individual evacuation times are closely correlated with one another in terms of their variation, and are both very sensitive to the specification of each directional choice parameter. Simulated evacuation time could vary up to nearly 30% depending on parameter values. The observed sensitivity highlighted the significance of importing well-calibrated parameters into such simulation models and practicing consistent degrees of accuracy for all levels of decision modeling. We also inferred that the two aggregate measures (i.e., total evacuation time and average individual evacuation times) can be used interchangeably as the basis for evacuation optimization or sensitivity analysis practices.