Quickest Paths in Simulations of Pedestrians

This contribution proposes a method to make agents in a microscopic simulation of pedestrian traffic walk approximately along a path of estimated minimal remaining travel time to their destination. Usually models of pedestrian dynamics are (implicitly) built on the assumption that pedestrians walk along the shortest path. Model elements formulated to make pedestrians locally avoid collisions and intrusion into personal space do not produce motion on quickest paths. Therefore a special model element is needed, if one wants to model and simulate pedestrians for whom travel time matters most (e.g. travelers in a station hall who are late for a train). Here such a model element is proposed, discussed and used within the Social Force Model.Comment: revised version submitte

LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW

Development of a microscopic crowd dynamic model: incorporating decision making capability Into the social force model.

Model daya sosial adalah salah satu model pejalan kaki mikroskopik yang paling berjaya yang mewakili fenomena dirancang dengan baik bagi aliran pejalan kaki. Bagaimanapun, keupayaan pejalan-pejalan kaki untuk membuat keputusan dalam situasi-situasi kecemasan dan normal tidak digabungkan dengan betul ke dalam model. The Social Force Model is one of the most successful microscopic pedestrian models that represent the well-organized phenomena of the pedestrian flow. However, the pedestrians‟ abilities to make decisions in normal and emergency situations have not been incorporated properly into the model

Vibration serviceability of footbridges under human-induced excitation : a literature review

Increasing strength of new structural materials and longer spans of new footbridges, accompanied with aesthetic requirements for greater slenderness, are resulting in more lively footbridge structures. In the past few years this issue attracted great public attention. The excessive lateral sway motion caused by crowd walking across the infamous Millennium Bridge in London is the prime example of the vibration serviceability problem of footbridges. In principle, consideration of footbridge vibration serviceability requires a characterisation of the vibration source, path and receiver. This paper is the most comprehensive review published to date of about 200 references which deal with these three key issues. The literature survey identified humans as the most important source of vibration for footbridges. However, modelling of the crowd-induced dynamic force is not clearly defined yet, despite some serious attempts to tackle this issue in the last few years. The vibration path is the mass, damping and stiffness of the footbridge. Of these, damping is the most uncertain but extremely important parameter as the resonant behaviour tends to govern vibration serviceability of footbridges. A typical receiver of footbridge vibrations is a pedestrian who is quite often the source of vibrations as well. Many scales for rating the human perception of vibrations have been found in the published literature. However, few are applicable to footbridges because a receiver is not stationary but is actually moving across the vibrating structure. During footbridge vibration, especially under crowd load, it seems that some form of human–structure interaction occurs. The problem of influence of walking people on footbridge vibration properties, such as the natural frequency and damping is not well understood, let alone quantified. Finally, there is not a single national or international design guidance which covers all aspects of the problem comprehensively and some form of their combination with other published information is prudent when designing major footbridge structures. The overdue update of the current codes to reflect the recent research achievements is a great challenge for the next 5–10 years

A hybrid model for simulating crowd evacuation

Macroscopic and microscopic models are typical approaches for simulating crowd behaviour and movement to simulate crowd and pedestrian movement, respectively. However, the two models are unlikely to address the issues beyond their modelling targets (i.e., pedestrian movement for microscopic models and crowd movement for macroscopic models). In order to solve such problem, we propose a hybrid model integrating macroscopic model into microscopic model, which is capable of taking into account issues both from crowd movement tendency and individual diversity to simulate crowd evacuation. In each simulation time step, the macroscopic model is executed first and generates a course-grain simulation result depicting the crowd movement, which directs microscopic model for goal selection and path planning to generate a fine-grain simulation result. In the mean time, different level-of-detail simulation results can also be obtained due to the proposed model containing two complete models. A synchronization mechanism is proposed to convey simulation results from one model to the other one. The simulation results via case study indicate the proposed model can simulate the crowd and agent behaviour in dynamic environments, and the simulation cost is proved to be efficient

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

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

Pedestrian Route Choice Of Vertical Facilities At Kuala Lumpur City Centre Underground Train Station

The increasing of ridership of Light Rail Transit (LRT) Kelana Jaya Line must be supported by efficient egress facilities in train station to optimise the pedestrian movement. However, limited route choices to egress platform level via stairways and escalators especially in underground train station are suspected to cause congestion and delay to egress the platform especially during the peak hours. In this dissertation, pedestrian route choice at Kuala Lumpur City Centre underground train station was studied by using Pedestrian Following Survey. The results showed that LRT users at the underground train station preferred to use the vertical facility that is nearer to their respective train doors due to the shortest path, except the passengers that alighted from the middle section of train and tended to egress with escalators over stairways, resulting in the percentage usage of escalators (52.3%) was higher than stairways (47.7%). LRT users at the underground platform walked faster towards stairways by at most 31.5% compared to the escalators. This is because pedestrians tended to use escalators thus reduced speed that caused by the overcrowded at the entrance of escalators. Hence, escalator users experienced higher delay in time by at least 30.1% compared to the stairway users. However, after the users boarded the vertical facilities, those who used stairways experienced higher delay time by at least 48.8% compared to those who used escalators that has uniform escalator operating speed. This dissertation can help the train station operators to have better understanding on pedestrian route choice to egress the underground train station and can be used as a reference for future facility design of underground train station

Modeling Trajectory-level Behaviors using Time Varying Pedestrian Movement Dynamics

We present a novel interactive multi-agent simulation algorithm to model pedestrian movement dynamics. We use statistical techniques to compute the movement patterns and motion dynamics from 2D trajectories extracted from crowd videos. Our formulation extracts the dynamic behavior features of real-world agents and uses them to learn movement characteristics on the fly. The learned behaviors are used to generate plausible trajectories of virtual agents as well as for long-term pedestrian trajectory prediction. Our approach can be integrated with any trajectory extraction method, including manual tracking, sensors, and online tracking methods. We highlight the benefits of our approach on many indoor and outdoor scenarios with noisy, sparsely sampled trajectory in terms of trajectory prediction and data-driven pedestrian simulation

MAKING THE GOOD EASY: THE SMART CODE ALTERNATIVE

This article advocates for a new, fundamentally different plan for how cities should be coded, the Smart Code. It links urbanism and environmentalism and is strongly aligned with smart growth and sustainability. The Smart Code is offered as an alternative to the current anti-urban, conventional codes which are rigid and focus on single-use zones that separate human living space from the natural environment, as illustrated by the sprawl

Velocity-Space Reasoning for Interactive Simulation of Dynamic Crowd Behaviors

The problem of simulating a large number of independent entities, interacting with each other and moving through a shared space, has received considerable attention in computer graphics, biomechanics, psychology, robotics, architectural design, and pedestrian dynamics. One of the major challenges is to simulate the dynamic nature, variety, and subtle aspects of real-world crowd motions. Furthermore, many applications require the capabilities to simulate these movements and behaviors at interactive rates. In this thesis, we present interactive methods for computing trajectory-level behaviors that capture various aspects of human crowds. At a microscopic level, we address the problem of modeling the local interactions. First, we simulate dynamic patterns of crowd behaviors using Attribution theory and General Adaptation Syndrome theory from psychology. Our model accounts for permanent, stable disposition and the dynamic nature of human behaviors that change in response to the situation. Second, we model physics-based interactions in dense crowds by combining velocity-based collision avoidance algorithms with external forces. Our approach is capable of modeling both physical forces and interactions between agents and obstacles, while also allowing the agents to anticipate and avoid upcoming collisions during local navigation. We also address the problem at macroscopic level by modeling high-level aspects of human crowd behaviors. We present an automated scheme for learning and predicting individual behaviors from real-world crowd trajectories. Our approach is based on Bayesian learning algorithms combined with a velocity-based local collision avoidance model. We further extend our method to learn time-varying trajectory behavior patterns from pedestrian trajectories. These behavior patterns can be combined with local navigation algorithms to generate crowd behaviors that are similar to those observed in real-world videos. We highlight their performance for pedestrian navigation, architectural design and generating dynamic behaviors for virtual environments.Doctor of Philosoph