TomTom provides congestion data solution

Today we have more data than ever before, and one aspect of this data revolution is road congestion data. One powerful source of such data is from TomTom and in this article we aim to provide some insights into how this (and similar sources) can be used by transport professionals, as well as how we are looking to exploit it.

Overview

Tom Tom provides detailed data on road speeds for over 4 million road links in the UK and equivalent volumes of data across Europe and most of the world. Both live data and historic data is available, with historic data including both average speeds and variability in speeds.

Bearing this in mind, there are two broad categories of application:

1. journey planning, using real-time information for current journeys and historic data for planning future journeys and comparing travel options; and
2. transport planning and modelling. 

For both these broad categories a further distinction can be made between passenger and freight/logistics applications.

Defining congestion

While everyone has an intuitive idea of ‘congestion’ is, it is a very subjective term which is heavily influenced by personal experience. It also has a number of dimensions to it. Average road speed is one, though one that is somewhat divorced from the driving experience which might considered as a different aspect, while journey time predictability is another. There is also the distinction to be made concerning planned and unplanned congestion: that is, road works versus random events or sheer volume of traffic.

The consequence is that defining what we mean by ‘congestion’ is not straightforward. However, one simple but effective measure of congestion is the difference between road speeds during the peak when it is congested compared with during the night-time when it is not. Using TomTom data for each of the 4 million road links we have examined the profile of congestion across the UK and on this basis have  defined six congestion levels (see table 1). Having pre-defined levels is of course common practice and useful for enabling drivers to visualise conditions on the road, and for planners to provide visualisations of the effect of policies. It is therefore somewhat unfortunate there is no agreed standard, so no common appreciation of what  a term such as “heavily congested” means.

* based on the % difference between peak and night-time speeds

While more work is needed to establish what exactly the driver experience is for each of these levels and how they relate to standard “speed-flow” curves, we do know from work we did some years ago something about the pain (or disbenefit) of congestion and how this varies according to the degree of congestion.  For example, we know that aside from the pure journey time effect, there is also an issue of driver frustration and uncertainty which becomes increasingly prevalent as the level of traffic increases. In fact, when we looked to quantify this effect we established that at high levels of congestion it is equal in impact to the additional journey time: in other words, the disbenefit of serious congestion is around twice the extra journey time.    

Mapping congestion

What having defined levels of congestion enables is detailed mapping of congestion anywhere in the country, and at any level of geography. To illustrate, we have included four maps below, starting from the whole of the UK then zooming down to the Manchester region, then to Manchester city centre. The Final map shows aggregated data for entire urban areas, illustrating how different towns and cities are affected by congestion (in relative terms).