Non-local first-order modelling of crowd dynamics: a multidimensional framework with applications

In this work a physical modelling framework is presented, describing the intelligent, non-local, and anisotropic behaviour of pedestrians. Its phenomenological basics and constitutive elements are detailed, and a qualitative analysis is provided. Within this common framework, two first-order mathematical models, along with related numerical solution techniques, are derived. The models are oriented to specific real world applications: a one-dimensional model of crowd-structure interaction in footbridges and a two-dimensional model of pedestrian flow in an underground station with several obstacles and exits. The noticeable heterogeneity of the applications demonstrates the significance of the physical framework and its versatility in addressing different engineering problems. The results of the simulations point out the key role played by the physiological and psychological features of human perception on the overall crowd dynamics.Comment: 26 pages, 17 figure

Probing streets and the built environment with ambient and community sensing

Data has become an important currency in todays world economy. Ephemeral and real-time data from Twitter, Facebook, Google, urban sensors, weather stations, and the Web contain hidden patterns of the city that are useful for informing architectural and urban design

FuturICT: Participatory computing to understand and manage our complex world in a more sustainable and resilient way

We have built particle accelerators to understand the forces that make up our physical world. Yet, we do not understand the princi-ples underlying our strongly connected, techno-socio-economic systems. We have enabled ubiquitous Internet connectivity and instant, global information access. Yet we do not understand how it impacts our be-havior and the evolution of society. To fill the knowledge gaps and keep up with the fast pace at which our world is changing, a Knowledge Accelerator must urgently be cre-ated. The financial crisis, international wars, global terror, the spread-ing of diseases and cyber-crime as well as demographic, technological and environmental change demonstrate that humanity is facing seri-ous challenges. These problems cannot be solved within the traditional paradigms. Moving our attention from a component-oriented view of the world to an interaction-oriented view will allow us to understand the com-plex systems we have created and the emergent collective phenomena characterising them. This paradigm shift will enable new solutions to long-standing problems, very much as the shift from a geocentric to a heliocentric worldview has facilitated modern physics and the ability to launch satellites. The FuturICT flagship project will develop new science and technology to manage our future in a complex, strongly connected world. For this, it will combine the power of information and communication technol-ogy (ICT) with knowledge from the social and complexity sciences. ICT will provide the data to boost the social sciences into a new era. Complexity science will shed new light on the emergent phenomena in socially interactive systems, and the social sciences will provide a better understanding of the opportunities and risks of strongly net-worked systems, in particular future ICT systems. Hence, the envisaged FuturICT flagship will create new methods and instruments to tackle the challenges of the 21 st century. FuturICT could indeed become one of the most important scientific endeavours ever, by revealing the principles that make socially inter-active systems work well, by inspiring the creation of new platforms to explore our possible futures, and by initiating an era of social and socio-inspired innovations

Internet of Things-based Traffic Management System for Maseru, Lesotho.

Published ThesisThe number of vehicles in Maseru has been steadily increasing, leading to heightened intensity of congestion and traffic occurrences. This is further exacerbated by ineffective solutions that are currently in place as well as the absence of tools that facilitate dispersal of information to motorists. Traffic lights have been put in place to manage flow of traffic but are becoming increasingly inefficient due to their design. The preset timing cycles between green, amber and red disregarding prevailing conditions leads, inter alia, to increased wait times, use of additional fuel and air pollution. In addition, lack of equipment that is able to provide motorists with information about prevailing road conditions further increases the possibility of one being stuck in traffic. To make traffic management more efficient at signaled junctions, the implementation of the Internet of Things (IoT) paradigm is used to create intelligent traffic management systems such as Wireless Sensor Networks (WSN) and fuzzy algorithms to intelligently decide the phases of traffic lights. Road density and vehicles’ speeds are collected from the road infrastructure using cameras and are passed to a fuzzy algorithm to determine how congested a road is. Dependent on these parameters, the algorithm will also determine which roads should be given highest priority while maintaining a degree of fairness, thus optimizing traffic flow. In addition, the ubiquitous provision of road condition information to motorists in various formats such as text and audio is also used. This feature allows for the acquisition of the latest road status, thus making it possible to find alternative routes. The unique feature in this project is the ability to collect road parameters from the road infrastructure itself, using WSN as well as crowd source data from road users using mobile devices. A study conducted in this research revealed a relationship between the number of cars on a road and concentration of Carbon Dioxide (CO2); the results showed that as the number of cars increases, so does the measure of CO2. Questionnaire-based surveys showed that Maseru citizens have noted an increase in congestion which they attributed to the increase in number of vehicles on the road that is not met by the increase or improvement in road infrastructure. The respondents in this survey also noted limited mechanisms that provide them with road conditions and highlighted that such tools may alleviate congestion. The performance of intelligent traffic lights was conducted via simulations compared with fixed cycle traffic lights. From the simulations it was observed that IoT- based traffic management systems reduced the wait times of vehicles at signaled junctions which would also result in reduction of the pollutant CO2. It is envisaged that the future implementation will include the ability to manage a network of junctions and ability to predict abnormal traffic flows