Not all mobile phone trip matrices are born the same (Part 6)
Mobile phone data provides new opportunities for further research into travel behaviour and transport models that may provide a more solid ground for practical applications and more reliable forecasting. This can help to reduce costs and delays of travel data collection, and also to explore new forms of modelling and monitoring trends.
Not all mobile phone trip matrices are born the same (Part 5)
An accurate specification of what is needed is key to obtain usable trip matrices, so here we analyse how to define them in the best possible way.
Not all mobile phone trip matrices are born the same (Part 4)
There are many studies comparing mobile phone based trip matrices with those obtained from other sources. Most of them appreciate the advantages of mobile phone data to generate mobility information, but there are different attributes to analyse.
Not all mobile phone trip matrices are born the same (Part 3)
Mobile phone data present some constraints for matrix building. However, some of these limitations can be overcome. In this article we present some of these challenges and how Nommon has found its way to work through them.
Not all mobile phone trip matrices are born the same (Part 2)
What exactly is mobile phone data? How is it processed to estimate trip matrices? Here we explain five important steps to process mobile phone data in order to generate good quality mobility information.
Not all mobile phone trip matrices are born the same (Part 1)
The traditional travel data collection methods have proven expensive, disruptive and limited, but new technologies have recently allowed to generate mobile phone trip matrices that could potentially overcome many of these constraints as they can provide door-to-door movements based on a device that most people carry with them at all times. So, how does this work?
