Pedestrian Simulation

UAF, Dumb People Smart Space Informative Analytics

The UAF is a flexible tool built on solid analytics providing data that shows how to improve urban space; presentation and 3D graphics take second place to well defined modelling concepts and a tried and tested evaluation methodology which allows the user to clearly identify problem areas in the urban space.

Dumb People, Smart Space, Informative Analytics

In the UAF all simulated people or agents have a limited amount of configuration parameters which in real terms corresponds to a limited amount of intelligence. Agents learn about their environment as they move through it, each picking up new information about how they can navigate the available space. Like a traffic simulation model, pedestrian behavior is a function of their space and the information given to the agent by that space. This approach allows us to show reality, warts and all: clumsy, clustered, accident prone and confused people. If the agents in the UAF are too smart they will not make mistakes, get lost, confused, cause delay, be involved in vehicle collisions and therefore tell us very little about the quality of service they are receiving from the space they move through.

Identify Causality and Safety Issues

Identification of safety issues is the number one goal of the UAF. We allow agents to become frustrated at crosswalks, to ignore the “don’t walk” signal, to take opportunist gaps and to cross between traffic flows should they think it is safe to do so. In addition we model both the vehicle and agent’s field of view, vehicle stopping abilities and the difference between aggressive and courteous drivers interaction with agents. These quantitative measures allow us to define causality which in turn lets the UAF provide informed and accurate feedback on safety issues to urban space designers.

Discovery = Understanding = Control

The model build methodology employed by the UAF promotes the concepts of discovery, understanding and control. The network build process encourages discovery as the modeller learns how the space influences pedestrian behavior. Discovery promotes understanding of the problem space allowing the modeller to see relationships and identify areas where high probabilities of conflict (POC’s) exist or areas of space that are over/under utilised. Understanding allows the modeller to make informed decisions on how best to improve the urban space in ways that are efficient for both road traffic and pedestrians, safe and friendly for pedestrians to use, and environmentally sensitive.