A network optimization-based approach for crowd management in large public gatherings

Effective crowd management during large public gatherings is necessary to enable pedestrians' access to and from the venue and to ensure their safety. This paper proposes a network optimization-based methodology to support such efficient crowd movement during large events. Specifically, a bi-level integer program is presented that, at the upper-level, seeks a reconfiguration of the physical layout that will minimize total travel time incurred by system users (e.g. evacuees) given utility maximizing route decisions that are taken by individuals in response to physical offerings in terms of infrastructure at the lower-level. The lower-level formulation seeks a pure-strategy Nash equilibrium that respects collective behavior in crowds. A Multi-start Tabu Search with Sequential Quadratic Programming procedure is proposed for its solution. Numerical experiments on a hypothetical network were conducted to illustrate the proposed solution methodology and the insights it provides.     

Pedestrian crowd tactical‐level decision making during emergency evacuations

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. Copyright © 2016 John Wiley & Sons, Ltd.     

A hybrid simulation-assignment modeling framework for crowd dynamics in large-scale pedestrian facilities

This paper presents a hybrid simulation-assignment modeling framework for studying crowd dynamics in large-scale pedestrian facilities. The proposed modeling framework judiciously manages the trade-off between ability to accurately capture congestion phenomena resulting from the pedestrians’ collective behavior and scalability to model large facilities. We present a novel modeling framework that integrates a dynamic simulation-assignment logic with a hybrid (two-layer or bi-resolution) representation of the facility. The top layer consists of a network representation of the facility, which enables modeling the pedestrians’ route planning decisions while performing their activities. The bottom layer consists of a high resolution Cellular Automata (CA) system for all open spaces, which enables modeling the pedestrians’ local maneuvers and movement decisions at a high level of detail. The model is applied to simulate the crowd dynamics in the ground floor of Al-Haram Al-Sharif Mosque in the City of Mecca, Saudi Arabia during the pilgrimage season. The analysis illustrates the model’s capability in accurately representing the observed congestion phenomena in the facility.     

Animal dynamics based approach for modeling pedestrian crowd egress under panic conditions

Collective movement is important during emergencies such as natural disasters or terrorist attacks, when rapid egress is essential for escape. The development of quantitative theories and models to explain and predict the collective dynamics of pedestrians has been hindered by the lack of complementary data under emergency conditions. Collective patterns are not restricted to humans, but have been observed in other non-human biological systems. In this study, a mathematical model for crowd panic is derived from collective animal dynamics. The development and validation of the model is supported by data from experiments with panicking Argentine ants (Linepithema humile). A first attempt is also made to scale the model parameters for collective pedestrian traffic from those for ant traffic, by employing a scaling concept approach commonly used in biology.     

Potential-based dynamic pedestrian flow assignment

This study proposes a potential-based dynamic pedestrian flow assignment model to optimize the evacuation time needed for all pedestrians to leave an indoor or outdoor area with internal obstacles and multiple exits, e.g., railway station, air terminal, plaza, and park. In the model, the dynamic loading of pedestrian flows on a two-dimensional space is formulated by a cell transmission model, the movement of crowds is driven by space potential, and the optimization of evacuation time is solved by a proportional swapping process. In this way, the proposed model can be applied to not only efficiently optimize the evacuation process of a crowd with large scale but also recognize local congestion dynamics during crowd evacuation. Finally, a set of numerical examples are presented to show the proposed model’s effectiveness for optimizing crowd evacuation process and its application to design a class of variable guide sign systems.     

Performance analysis of crowd-shipping in urban and suburban areas

Transportation - Crowd logistics is a novel shipping concept where delivery operations are carried out by using existing resources, namely vehicle capacity and drivers from the crowd, thereby...     

On a mean field game approach modeling congestion and aversion in pedestrian crowds

In this paper we present a new class of pedestrian crowd models based on the mean field games theory introduced by Lasry and Lions in 2006. This macroscopic approach is based on a microscopic model, that considers smart pedestrians who rationally interact and anticipate the future. This leads to a forward-backward structure in time. We focus on two-population interactions and validate the modeling with simple examples. Two complementary classes of problems are addressed, namely the case of crowd aversion and the one of congestion. In both cases we describe the model and present numerical solvers (based on the optimization formulation and the partial differential equations respectively). Finally we present numerical tests involving anticipation phenomena and complex group behaviors such as lane formation.     

Analyzing time sensitivity to discomfort in the Paris subway: an interval data model approach

We analyze individual travel discomfort-time tradeoffs in the Paris subway using stated choice experiments. The survey design allows to set up in a willingness-to-pay space to estimate the distributions of elasticities of values of travel time to crowd density and time multipliers. Several formulations of a generalized travel cost function are tested. Accounting for heterogeneity in preferences, the econometric models all take the form of an ordered probit with known bounds. We derive several estimates that could be used for fine-tuning of traffic simulation systems and more general transportation policy analysis.     

Modelling negative interactions among pedestrians in high density situations

Situations characterised by the presence of a high density of pedestrians involved in negative interactions (e.g. flows in opposite directions) often represent a problematic scenario for simulation models, especially those taking a discrete approach to the representation and management of spatial aspects of the environment. While these situations can be relatively infrequent, and even if architects, event organisers and crowd managers actually try to prevent them as much as possible, they simply cannot be neglected and they actually represent interesting situations to be analysed by means of simulation. The paper presents specific extensions to a floor-field Cellular Automata pedestrian model that are specifically aimed at supporting the simulation of high density situations comprising negative interactions among pedestrians without incurring in the traditional limits of discrete approaches. The models are formally described and experimented in experimental and real world situations.     

The effect of crowding on public transit user travel behavior in a large-scale public transportation system through modeling daily variations

In this paper, the crowding effect in a transit vehicle is modeled in a time-expanded network that considers the daily variation in passenger flows. The study models the daily variation of in-vehicle crowding in a real large-scale transit system. A transit assignment for this real network is modeled and implemented by constructing a crowding cost function that follows the valuation of crowding and by using the reliable shortest path finding method. The direct application of the crowding model to a real network for the Utah Transit Authority indicates that crowd modeling with multi-user classes could influence public transportation system planning and affect the revenues of transit agencies. Moreover, the addition of the disutility factor, crowding, does not always appear to cause an increase in disutility for transit users.     

Modeling real-time human mobility based on mobile phone and transportation data fusion

Even though a variety of human mobility models have been recently developed, models that can capture real-time human mobility of urban populations in a sustainable and economical manner are still lacking. Here, we propose a novel human mobility model that combines the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and urban transportation data (i.e., continuous collection and high accuracy). Using the proposed human mobility model, travel demands during each 1-h time window were estimated for the city of Shenzhen, China. Significantly, the estimated travel demands not only preserved the distribution of travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed human mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams.     

Assessing the usage and level-of-service of pedestrian facilities in train stations: A Swiss case study

A framework for assessing the usage and level-of-service of rail access facilities is presented. It consists of two parts. A dynamic demand estimator allows to obtain time-dependent pedestrian origin–destination demand within walking facilities. Using that demand, a traffic assignment model describes the propagation of pedestrians through the station, providing an estimate of prevalent traffic conditions in terms of flow, walking times, speed and density. The corresponding level-of-service of the facilities can be directly obtained. The framework is discussed at the example of Lausanne railway station. For this train station, a rich set of data sources including travel surveys, pedestrian counts and trajectories has been collected in collaboration with the Swiss Federal Railways. Results show a good performance of the framework. To underline its practical applicability, a six-step planning guideline is presented that can be used to design and optimize rail access facilities for new or existing train stations. In the long term, the framework may also be used for crowd management, involving real-time monitoring and control of pedestrian flows.     

Predicting electronic toll collection service adoption: An integration of the technology acceptance model and the theory of planned behavior

In order to reduce the number of vehicles stuck in congestion, especially for stop-and-go traffic at toll plazas, the establishment of electronic toll collection (ETC) systems has been a hot issue and dominant trend in many countries. Taiwan has joined the crowd, adding an ETC system to its toll roads in early 2006. However, despite the potential benefits for motorists, the utilization rate has been lower than expected during the introductory stage. The objective of this study is to advance our understanding on the critical antecedents of motorists’ intention of ETC service adoption by integrating both technology acceptance model (TAM) and theory of planned behavior (TPB) perspectives. Through empirical data collection and analysis from highway motorists who had not installed on-board units (OBU) for ETC service in Taiwan, we found that system attributes, perceived usefulness and perceived ease of use, indeed, positively engender motorists’ attitudes towards ETC service adoption. Moreover, results also reveal that attitude, subjective norm and perceived behavioral control positively influence the intention of ETC system adoption. Implications for practitioners and researchers, and suggestions for future research are also addressed in this study.     

Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks

Short-term passenger flow forecasting is a vital component of transportation systems. The forecasting results can be applied to support transportation system management such as operation planning, and station passenger crowd regulation planning. In this paper, a hybrid EMD-BPN forecasting approach which combines empirical mode decomposition (EMD) and back-propagation neural networks (BPN) is developed to predict the short-term passenger flow in metro systems. There are three stages in the EMD-BPN forecasting approach. The first stage (EMD Stage) decomposes the short-term passenger flow series data into a number of intrinsic mode function (IMF) components. The second stage (Component Identification Stage) identifies the meaningful IMFs as inputs for BPN. The third stage (BPN Stage) applies BPN to perform the passenger flow forecasting. The historical passenger flow data, the extracted EMD components and temporal factors (i.e., the day of the week, the time period of the day, and weekday or weekend) are taken as inputs in the third stage. The experimental results indicate that the proposed hybrid EMD-BPN approach performs well and stably in forecasting the short-term metro passenger flow.     

Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks

Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts.     

Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses

Monitoring pedestrian and cyclists movement is an important area of research in transport, crowd safety, urban design and human behaviour assessment areas. Media Access Control (MAC) address data has been recently used as potential information for extracting features from people’s movement. MAC addresses are unique identifiers of WiFi and Bluetooth wireless technologies in smart electronics devices such as mobile phones, laptops and tablets. The unique number of each WiFi and Bluetooth MAC address can be captured and stored by MAC address scanners. MAC addresses data in fact allows for unannounced, non-participatory, and tracking of people. The use of MAC data for tracking people has been focused recently for applying in mass events, shopping centres, airports, train stations, etc. In terms of travel time estimation, setting up a scanner with a big value of antenna’s gain is usually recommended for highways and main roads to track vehicle’s movements, whereas big gains can have some drawbacks in case of pedestrian and cyclists. Pedestrian and cyclists mainly move in built distinctions and city pathways where there is significant noises from other fixed WiFi and Bluetooth. Big antenna’s gains will cover wide areas that results in scanning more samples from pedestrians and cyclists’ MAC device. However, anomalies (such fixed devices) may be captured that increase the complexity and processing time of data analysis. On the other hand, small gain antennas will have lesser anomalies in the data but at the cost of lower overall sample size of pedestrian and cyclist’s data. This paper studies the effect of antenna characteristics on MAC address data in terms of travel-time estimation for pedestrians and cyclists. The results of the empirical case study compare the effects of small and big antenna gains in order to suggest optimal set up for increasing the accuracy of pedestrians and cyclists’ travel-time estimation.     

The use of transferable permits in transport policy

This paper considers potential use of domestic transferable, or tradable, permit systems for the purposes of travel management, especially reducing environmental nuisances. The main arguments for and against the use of permits are analyzed. Secondly two case studies of existing permit systems are examined. The main conclusions are that tradable permits can address greenhouse gas and regional atmospheric pollutant emissions, and are suitable for congestion on a restricted time–space basis. Permits applied to mobile sources are technically feasible at acceptable financial cost for protecting sensitive geographical areas, and schemes applied to automakers for unitary vehicle emissions are also viable.     

Restructuring land transport in New Zealand

Restructuring of governmental activities in New Zealand calls for public enterprises to operate in competitive environments. This has created problems for highways and urban passenger transport. Whereas the national airline has been privatized and railways corporatized, legislation for land transport has created a Crown agency that is expected to operate commercially within funding and policy constraints. The problems are assessed as Transit New Zealand attempts to allocate state funds between competing projects using commercial criteria. New roles have been legislated for regional agencies and local governments: they are expected to either privatize service delivery or create public corporations to maintain highways and operate passenger transport. Principles underlying restructuring are examined, and suggestions are made on how agencies might take advantage of opportunities.