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The modelling and prediction of traffic flow is one of the future challenges for science. We present a simulation tool for an urban road network based on real-time traffic data and a cellular automaton model for traffic flow. This tool has been applied to the inner city of Duisburg. The quality of the reproduced traffic states is investigated with regard to vehicle densities and typical features of urban traffic.

Schools are vulnerable to strong-magnitude earthquakes. The purpose of this study is to develop a seismic evacuation safety index (ESI) to assess school’s safety as a function of the following parameters: means of egress, disaster preparedness and disaster response. Moreover, the study aims to simulate and study an evacuation model to estimate evacuation time for a realistic understanding of the evacuation processes.

The paper used a semi-quantitative risk assessment method in developing the ESI. This was used to evaluate schools and classify them according to their level of evacuation safety. To estimate the evacuation time of each school, cellular automata theory and static floor field were used.

The paper provides primary school stakeholders important parameters that they should consider in preparing pre-disaster plans to ensure safe evacuation of school children.

ESI focuses only on the means of egress, disaster preparedness and disaster response as the contributing factors. The structural conditions of each school building and assessment of non-structural elements are not considered.

The ESI and the evacuation model can be used as a basis for evacuation planning and decision-making. This can help building owners and administrators in strengthening their disaster risk management plan by enforcing mitigating measures.

ESI is an original idea and fills the gap regarding the safe evacuation of school children especially during a major seismic event.

Up-to-date, the simulation of pedestrian behavior is used to support the design and analysis of urban infrastructure and public facilities. The purpose of this paper is to present a microscopic model that describes pedestrian behavior in a two-dimensional space. It is based on multi-agent systems and cellular automata theory. The concept of layered-intelligent terrain from the video game industry is reused and concepts such as tracing, evasion and rejection effects related to pedestrian interactive behavior are involved. In a simulation scenario, an agent represents a pedestrian with homogeneous physical characteristics such as walking speed and height. The agents are moved through a discrete space formed by a lattice of hexagonal cells, where each one can contain up to one agent at the same time. The model was validated by using a test that is composed of 17 real data sets of pedestrian unidirectional flow. Each data set has been extracted from laboratory-controlled scenarios carried out with up to 400 people walking through a corridor whose configuration changed in form of the amplitude of its entrance doors and the amplitude of its exit doors from one experiment to another. Moreover, each data set contained different groups of coordinates that compose pedestrian trajectories. The scenarios were replicated and simulated using the proposed model, obtaining 17 simulated data sets. In addition, a measurement methodology based on Voronoi diagrams was used to compute the velocity, density and specific flow of pedestrians to build a time-series graphic and a set of heat maps for each of the real and simulated data sets.

The approach consists of a multi-agent system and cellular automata theory. The obtained results were compared with other studies and a statistical analysis based on similarity measurement is presented.

A microscopic mobility model that describes pedestrian behavior in a two-dimensional space is presented. It is based on multi-agent systems and cellular automata theory. The concept of layered-intelligent terrain from the video game industry is reused and concepts such as tracing, evasion and rejection effects related to pedestrian interactive behavior are involved. On average, the simulated data sets are similar by 82 per cent in density and 62 per cent in velocity compared to the real data sets. It was observed that the relation between velocity and density from real scenarios could not be replicated.

The main limitations are presented in the speed simulations. Although the obtained results present a similar behavior to the reality, it is necessary to introduce more variables in the model to improve the precision and calibration. Other limitation is the dimension for simulating variables at this moment 2D is presented. So the resolution of cells, making that pedestrian to occupy many cells at the same time and the addition of three dimensions to the terrain will be a good challenge.

In total, 17 data sets were generated as a case study. They contain information related to speed, trajectories, initial and ending points. The data sets were used to calibrate the model and analyze the behavior of pedestrians. Geospatial data were used to simulate the public infrastructure in which pedestrians navigate, taking into account the initial and ending points.

The social impact is directly related to the behavior analysis of pedestrians to know tendencies, trajectories and other features that aid to improve the public facilities. The results could be used to generate policies oriented toward developing more consciousness in the public infrastructure development.

The general methodology is the main value of this work. Many approaches were used, designed and implemented for analyzing the pedestrians’ behavior. In addition, all the methods were implemented in plug-in for Quantum GIS. The analysis was described with heat maps and statistical approaches. In addition, the obtained results are focused on analyzing the density, speed and the relationship between these features.

It is of paramount importance to ensure safe and fast evacuation routes in cities in case of natural disasters, environmental accidents or acts of terrorism. The same applies to large-scale events such as concerts, sport events and religious pilgrimages as airports and to traffic hubs such as airports and train stations. The prediction of pedestrian is notoriously difficult because it varies depending on circumstances (age group, cultural characteristics, etc.). In this study, the Ensemble Kalman Filter (EnKF) data assimilation technique, which uses the updated observation data to improve the accuracy of the simulation, was applied to improve the accuracy of numerical simulations of pedestrian flow.

The EnKF, one of the data assimilation techniques, was applied to the in-house numerical simulation code for pedestrian flow. Two cases were studied in this study. One was the simplified one-directional experimental pedestrian flow. The other was the real pedestrian flow at the Kaaba in Mecca. First, numerical simulations were conducted using the empirical input parameter sets. Then, using the observation data, the EnKF estimated the appropriate input parameter sets. Finally, the numerical simulations using the estimated parameter sets were conducted.

The EnKF worked on the numerical simulations of pedestrian flow very effectively. In both cases: simplified experiment and real pedestrian flow, the EnKF estimated the proper input parameter sets which greatly improved the accuracy of the numerical simulation. The authors believe that the technique such as EnKF could also be used effectively in other fields of computational engineering where simulations and data have to be merged.

This technique can be used to improve both design and operational implementations of pedestrian and crowd dynamics predictions. It should be of high interest to command and control centers for large crowd events such as concerts, airports, train stations and pilgrimage centers.

To the authors’ knowledge, the data assimilation technique has not been applied to a numerical simulation of pedestrian flow, especially to the real pedestrian flow handling millions pedestrian such as the Mataf at the Kaaba. This study validated the capability and the usefulness of the data assimilation technique to numerical simulations for pedestrian flow.

The purpose of this paper is to propose a method that can calculate the transportation infrastructure network service capacity enhancement given by planned transportation infrastructure projects construction. Managers can sequence projects more rationally to maximize the construction effectiveness of infrastructure investments.

This paper designed a computational network simulation software to generate topological networks based on established rules. Based on the topological networks, the software simulated the movement path of users and calculated the average travel time. This software allows the adjustment of parameters to suit different research objectives. The average travel time is used as an evaluation index to determine the most appropriate construction sequence.

In this paper, the transportation infrastructure network of Sichuan Province in China was used to demonstrate this software. The average travel time of the existing transportation network in Sichuan Province was calculated as 211 min using this software. The high-speed railways from Leshan to Xichang and from Xichang to Yibin had the greatest influence on shortening the average travel time. This paper also measured the changes in the average travel time under two strategies: shortening the maximum and minimum priorities. All the transportation network optimisation plans for Sichuan Province will be somewhere between these two strategies.

The contribution of this research are three aspects: First, a complex network analysis method that can take into account the differences of node elements is proposed. Second, it provides an effective tool for decision makers to plan transportation infrastructure construction. Third, the construction sequence of transportation infrastructure development plan can effect the infrastructure investment effectiveness.

The purpose of this paper is to develop a first-principles model for the simulation of pedestrian flows and crowd dynamics capable of computing the movement of a million pedestrians in real-time in order to assess the potential safety hazards and operational performance at events where many individuals are gathered. Examples of such situations are sport and music events, cinemas and theatres, museums, conference centres, places of pilgrimage and worship, street demonstrations, emergency evacuation during natural disasters.

The model is based on a series of forces, such as: will forces (the desire to reach a place at a certain time), pedestrian collision avoidance forces, obstacle/wall avoidance forces; pedestrian contact forces, and obstacle/wall contact forces. In order to allow for general geometries a so-called background triangulation is used to carry all geographic information. At any given time the location of any given pedestrian is updated on this mesh. The model has been validated qualitatively and quantitavely on repeated occasions. The code has been ported to shared and distributed memory parallel machines.

The results obtained show that the stated aim of computing the movement of a million pedestrians in real-time has been achieved. This is an important milestone, as it enables faster-than-real-time simulations of large crowds (stadiums, airports, train and bus stations, concerts) as well as evacuation simulations for whole cities.

All models are wrong, but some are useful. The same applies to any modelling of pedestrians. Pedestrians are not machines, so stochastic runs will be required in the future in order to obtain statistically relevant ensembles.

This opens the way to link real-time data gathering of crowds (i.e. via cameras) with predictive calculations done faster than real-time, so that security personnel can be alerted to potential future problems during large-scale events.

This will allow much better predictions for large-scale events, improving security and comfort.

This is the first time such speeds have been achieved for a micro-modelling code for pedestrians.