Terms & Conditions

Contents

Go to -> Company Details

Go to -> Licensing of Oasys software – Licensing Documentation PDF

Go to -> Help with ordering

Data Security

Website Terms and Conditions

Software Licensing Terms


Data Security

We are committed to ensuring that your information is secure. In order to prevent unauthorised access or disclosure we have put in place suitable physical, electronic and managerial procedures to safeguard and secure the information we collect.

All data is stored in secure electronic systems accessible only to Oasys staff with both valid network login credentials and specific authorisation to access the system.  Our systems further limit data access by role to ensure data is available only to those who have a specific need to see it.

If at any point you suspect or receive a suspicious communication from someone suggesting they work for Oasys or a website claiming to be affiliated with Oasys, please forward the communication to us or report the incident by email to oasys@arup.com or in writing to Oasys, 13 Fitzroy Street, London, UK, W1T 4BQ as soon as possible.

Data Security Notice Updated 27th February 2020

top ]


Website Terms and Conditions

The contents of this web site are protected by copyright and other intellectual property rights under international conventions. No copying of any words, images, graphic representations or other information contained in this web site is permitted without the prior written permission of the webmaster for this site.

Oasys accepts no responsibility for the content of any external site that links to or from this site.

top ]


Software Licensing Terms

Terms and Conditions of Purchase

The full conditions of purchase and maintenance for all Oasys software are set out in the Oasys Software Licence and Support Agreement. All prices are subject to TAX at the current rate.

Prices and specifications are subject to change without notice – please ask for a written quotation.

Although every care has been taken to ensure the accuracy of all information contained herein, the contents do not form or constitute a representation, warranty, or part of any contract.

Superseded Versions of Terms and Conditions

Oasys keeps copies of all superseded versions of its terms and conditions.

Maintenance & Support Services

Support and maintenance is included with all subscription licences for their full duration.

Annual maintenance contracts are available for software under a perpetual licence, prices are based on a percentage of the most recent list price.

This service includes:

  • telephone/email/web based support
  • free software updates available via internet download
  • personalised output header for many products

How – and when – will big data benefit pedestrian simulation?

Machine Learning is a current application of Artificial Intelligence based around the idea that we should just be able to give machines access to data and let them learn for themselves. What are the implications for pedestrian simulation now that there is a veritable flood of data becoming available to it?

The first release of release Oasys MassMotion pedestrian simulation software was a response to the specific needs of the engineering team working on what has now become the New York Fulton Centre and subway interchange. They had a huge infrastructure project on their hands, with no way of testing their design concept. The MassMotion team developed algorithms that could simulate people moving through 3D space. Impressive as it was, it was developed in what was, relatively speaking a data drought, dependent on manual sources and inputs.

Since then, Oasys MassMotion software has been under continuous development and is widely used in rail, air and sports hubs as well as complex public spaces. It is now generally accepted that crowd simulation has a key role to pay in any design process where there are large numbers of people and/or specific performance criteria to cater for. It is also accepted that MassMotion’s pedestrian simulation is about as near to real human behaviour as it is possible – currently – to get.

It uses relatively small amounts of input data to drive an iterative cause and effect model which then generates large amounts of predictive data. Now the data floodgates are set to open, we need to learn how to assimilate all the information.

Data that was typically collected manually is now becoming available from streaming sources:

The immediate benefit should be using these large amounts of data to generate models that can turn new input data into meaningful outputs using techniques including convolutional neural networks, clustering, and regression analyses. These models will be statistically reliable and verifiable. For example: In this situation will a person tend to choose the right or left door?

But there are limits… machine learning models only provide meaningful answers when new input data is similar to the data used to train the models. These models cannot generate datasets that describe novel conditions.

Ubiquitous and high-quality data collection isn’t here yet, but it will happen sooner rather than later. We are going to need new tools and techniques to handle the new data flood and now is the time to be resolving issues around ownership and interoperability.

For now, we need to look critically at workflows and identify and focus on areas of poor data integrity. Because simulations are so dependent on such a small amount of input data, it is critical that this data is a good as it can be.  In the short term, machine learning and sensor data analytics offer the promise of significant improvement to simulation input data.

Big Data Analytics will improve our understanding of the now and help us to simulate the future. Crowd movement and management will be improved: the buildings and cities we live in will be better suited to the needs of our growing population.

 

Require more information?

  • This field is for validation purposes and should be left unchanged.

Newsletter Sign up

Please fill out your details below to receive the latest oasys news.
  • This field is for validation purposes and should be left unchanged.