245 research outputs found

    Passenger Flows in Underground Railway Stations and Platforms, MTI Report 12-43

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    Urban rail systems are designed to carry large volumes of people into and out of major activity centers. As a result, the stations at these major activity centers are often crowded with boarding and alighting passengers, resulting in passenger inconvenience, delays, and at times danger. This study examines the planning and analysis of station passenger queuing and flows to offer rail transit station designers and transit system operators guidance on how to best accommodate and manage their rail passengers. The objectives of the study are to: 1) Understand the particular infrastructural, operational, behavioral, and spatial factors that affect and may constrain passenger queuing and flows in different types of rail transit stations; 2) Identify, compare, and evaluate practices for efficient, expedient, and safe passenger flows in different types of station environments and during typical (rush hour) and atypical (evacuations, station maintenance/ refurbishment) situations; and 3) Compile short-, medium-, and long-term recommendations for optimizing passenger flows in different station environments

    Adaptive cell-based evacuation systems for leader-follower crowd evacuation

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    The challenge of controlling crowd movement at large events expands not only to the realm of emergency evacuations but also to improving non-critical conditions related to operational efficiency and comfort. In both cases, it becomes necessary to develop adaptive crowd motion control systems. In particular, adaptive cell-based crowd evacuation systems dynamically generate exit-choice recommendations favoring a coordinated group dynamic that improves safety and evacuation time. We investigate the viability of using this mechanism to develop a ‘‘leader-follower’’ evacuation system in which a trained evacuation staff guides evacuees safely to the exit gates. To validate the proposal, we use a simulation–optimization framework integrating microscopic simulation. Evacuees’ behavior has been modeled using a three-layered architecture that includes eligibility, exit-choice changing, and exit-choice models, calibrated with hypothetical-choice experiments. As a significant contribution of this work, the proposed behavior models capture the influence of leaders on evacuees, which is translated into exitchoice decisions and the adaptation of speed. This influence can be easily modulated to evaluate the evacuation efficiency under different evacuation scenarios and evacuees’ behavior profiles. When measuring the efficiency of the evacuation processes, particular attention has been paid to safety by using pedestrian Macroscopic Fundamental Diagrams (p-MFD), which model the crowd movement dynamics from a macroscopic perspective. The spatiotemporal view of the evacuation performance in the form of crowd-pressure vs. density values allowed us to evaluate and compare safety in different evacuation scenarios reasonably and consistently. Experimental results confirm the viability of using adaptive cell-based crowd evacuation systems as a guidance tool to be used by evacuation staff to guide evacuees. Interestingly, we found that evacuation staff motion speed plays a crucial role in balancing egress time and safety. Thus, it is expected that by instructing evacuation staff to move at a predefined speed, we can reach the desired balance between evacuation time, accident probability, and comfort

    Elementary modelling and behavioural analysis for emergency evacuations using social media

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    YesSocial media usage in evacuations and emergency management represents a rapidly expanding field of study. Our paper thus provides quantitative insight into a serious practical problem. Within this context a behavioural approach is key. We discuss when facilitators should consider model-based interventions amid further implications for disaster communication and emergency management. We model the behaviour of individual people by deriving optimal contrarian strategies. We formulate a Bayesian algorithm which enables the optimal evacuation to be conducted sequentially under worsening conditions.Supported by EPSRC (IDEAS Factory - Game theory and adaptive networks for smart evacuations, EP/I005765/1

    Mobile eye tracking applied as a tool for customer experience research in a crowded train station

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    Train stations have increasingly become crowded, necessitating stringent requirements in the design of stations and commuter navigation through these stations. In this study, we explored the use of mobile eye tracking in combination with observation and a survey to gain knowledge on customer experience in a crowded train station. We investigated the utilization of mobile eye tracking in ascertaining customers’ perception of the train station environment and analyzed the effect of a signalization prototype (visual pedestrian flow cues), which was intended for regulating pedestrian flow in a crowded underground passage. Gaze behavior, estimated crowd density, and comfort levels (an individual’s comfort level in a certain situation), were measured before and after the implementation of the prototype. The results revealed that the prototype was visible in conditions of low crowd density. However, in conditions of high crowd density, the prototype was less visible, and the path choice was influenced by other commuters. Hence, herd behavior appeared to have a stronger effect than the implemented signalization prototype in conditions of high crowd density. Thus, mobile eye tracking in combination with observation and the survey successfully aided in understanding customers’ perception of the train station environment on a qualitative level and supported the evaluation of the signalization prototype the crowded underground passage. However, the analysis process was laborious, which could be an obstacle for its practical use in gaining customer insights

    Optimizing Stadium Evacuation by Integrating Geo-Computation and Affordance Theory

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    The purpose of this project was to optimize football stadium evacuation time by integrating geo-computation with affordance theory from perceptual psychology to account for evacuee characteristics: age, gender, physical fitness, alcohol consumption, and prior experience attending football games at The University of Southern Mississippi (USM), evacuating from large, outdoor public places, and with hazard events. According to the Uniting and Strengthening America by Providing Appropriate Tools Required to Intercept and Obstruct Terrorism (USA PATRIOT) Act, football stadiums are part of the country’s critical infrastructure warranting special government protection. Evacuation modeling was identified as an important component of game day emergency preparation. Research shows that: (1) the age, gender, and physical fitness of an individual impact his/her locomotion speed; (2) evacuation route choice is influenced by the perception of its safety and effectiveness; and (3) prior evacuation experience affects evacuation decision-making processes. By including these factors, this research, conducted at USM’s M.M. Roberts Stadium, represents the reality of evacuee movement and behaviors that influence stadium evacuation time. A questionnaire-based survey was administered to game attendees prior to a USM home game to gather evacuee attribute data that influenced locomotion speed. This data, plus secondary spatial data, were used in an agent-based model to model individual evacuee movement. The time required for all evacuees to exit the stadium and campus was 165.16 minutes. This time was significantly shorter than evacuation times from the same location using non-location-specific evacuee locomotion speeds, suggesting that use of local data is vital to accurately depicting evacuation time. The findings also indicated that age and gender were the two main factors that impacted locomotion speeds. The main contributions of this study were: (1) optimizing evacuation time by using location-specific locomotion speeds and (2) providing insights into how evacuees’ physical and mental health influence their evacuation decision-making processes. The U.S. government and sports management industry could use these findings to increase game day safety and security. Due to the spatio-temporal nature of evacuation modeling and perceptions of evacuees that impact evacuation time, this research contributed to the fields of geography, computer science, sport management, psychology, and emergency management

    Multi-scale Models for Transportation Systems Under Emergency Conditions

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    The purpose of this study is to investigate human behavior in emergencies. More specifically, agent-based simulation and social force models were developed to examine the impact of various human and environmental factors on the efficiency of the evacuation process, through a series of case studies. The independent variables of the case studies include the number of exits, the number of passengers, the evacuation policies, and instructions, as well as the queue configuration and wall separators. The results revealed the location of the exits, number of exits, evacuation strategies, and group behaviors all significantly impact the total time of the evacuation. For the queue configuration, short aisles lower infection spread when rope separators were used. The findings provide new insights in designing layout, planning, practice, and training strategies for improving the effectiveness of the pedestrian evacuation process under emergency

    Optimizing Simulated Crowd Behaviour

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    In the context of crowd simulation, there is a diverse set of algorithms that model steering, the ability of an agent to navigate between spatial locations, while avoiding static and dynamic obstacles. The performance of steering approaches, both in terms of quality of results and computational efficiency, depends on internal parameters that are manually tuned to satisfy application-specific requirements. This work investigates the effect that these parameters have on an algorithm's performance. Using three representative steering algorithms and a set of established performance criteria, we perform a number of large scale optimization experiments that optimize an algorithm's parameters for a range of objectives. For example, our method automatically finds optimal parameters to minimize turbulence at bottlenecks, reduce building evacuation times, produce emergent patterns, and increase the computational efficiency of an algorithm. Our study includes a statistical analysis of the correlations between algorithmic parameters, and performance criteria. We also propose using the Pareto Optimal Front as an efficient way of modelling optimal relationships between multiple objectives, and demonstrate its effectiveness by estimating optimal parameters for interactively defined combinations of the associated objectives. The proposed methodologies are general and can be applied to any steering algorithm using any set of performance criteria

    The Use of Human Behaviour to Inform Egress Modeling in Stadiums

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    With growing concerns of public safety in infrastructure where large crowds gather, designing for egress under normal and emergency conditions is pertinent to ensuring efficient and safe conditions in stadia. There is a need for a large database of publicly available pedestrian movement profiles through experiments and the evaluation of relevant case studies. This thesis outlines novel human behaviour data collection at two stadia. Subsequent egress model validation using the MassMotion Advanced Crowd Simulation Software (MassMotion) was performed and measured total egress times. Although demographics and anthropometry in the stands slightly influenced the egress times, the stadium architecture was the governing factor which impeded pedestrian flow under non-emergency conditions. Analysis of a stadium fire case study allowed for evaluation of this conclusion during an evacuation which revealed that behavioural aspects of both occupant and staff may begin to dominate the egress simulation in an emergency context

    A review of traffic models for wildland-urban interface wildfire evacuation

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    Recent years have seen an increased prevalence of wildfires, some of which has spread into the wildland-urban interface and lead to large-scale evacuations. Large-scale evacuations gives rise to both logistical and traffic related issues. To aid in the planning and execution of such evacuations reliable modelling tools to simulate evacuation traffic are needed. Today no traffic model exists which is dedicated only to simulate wildfire evacuation in the wildland/urban interface. The aim of this thesis is to identify benchmark characteristics needed in such a model and review 12 existing models, both traffic models and evacuation models, and their potential usefulness in WUI wildfire scenarios. The thesis concludes that some models can be tuned to represent aspects of a WUI fire evacuation and that future research should focus on integrating traffic modelling with modelling of fire/smoke spread and pedestrian movement
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