Space Syntax and London’s ‘Main’ Roads

At the very broadest scope, Space Syntax can be said to investigate the relationship between movement and the configuration and connectivity of space.  In the past, while much favour has been found in the approach, critics have been distrustful of the axial line concept and of the representation of road segments as nodes in a network.  The construction of the network too, the process of drawing a network of longest lines of sight, has been seen to be unscientific.  Although I personally feel this to be a weak argument against Space Syntax in general, it’s acceptance into the wider research community may be hampered by this fact.

By way of a response to this argument, either intentionally or otherwise, there has been a movement towards segment-to-segment angularity (known as Angular Choice) as a predictor of movement.  The method is described by Turner in this paper, but in summary it is a calculation of betweenness on each network segment using the angular deviation between segments as the weight on which to calculate a shortest path.  The higher scoring segments, therefore, are those which are on a larger number of shortest angular paths passing over them.

One implication of this approach is that it a better fit for through-movement, that is an indicator of the routes we’re likely to use when moving from A to B.  This fits with what has been identified in other literature (particularly spatial cognition) where least angular change is identified as a driver of choice, notably in favour of pure metric distance.

So with a view to better understanding this relationship between the reality and angular choice, I wanted to compare the networks we find in the city and those indicated by this measure.  The first step was to draw out what traffic planners view as the most important roads on the network.  These are the roads identified in network as ‘Motorways’ and ‘A Roads’ (e.g. the ‘main’ roads), and as defined by the Department for Transport.  These were extracted and are as shown below:

The top 2% of these measures immediately draw out many of the most used and most well-known roads in London.  The M25 is prominent, as is the North Circular and various corridor roads into the city.  At 5% there is more definition of some of the other key roads, and by 10% we have a network that is quite similar to the map of ‘main’ roads in London.

By way of a statistically breakdown, the top 2% of values of the Choice measure predicts 76.3% of all ‘Motorway’ segments and 28.4% of all ‘A Roads’.  By 10%, these values have risen to 87.4% and 75.4% of all segments, respectively.  It is therefore clear that there is a correlation between this network measure and the definitions applied to the network.

I realise that this is a somewhat unrefined piece of work but I’d welcome any comments and am happy to share more on my method and results for those who are interested.

Taxi Driver Brains

Going through some old links I found this, UCL’s Hugo Spiers talking about taxi drivers’ brain activity during their movement around the city.  Demonstrates the use of landmarks and salient features in movement around the city, as well as providing some quantitative evidence for route-choice patterns.

For those interested there a BBC article on this work here, and the full paper (for those with access) here.

From Road Closure to Road Congestion

Much of my work attempts to recreate the macro from the micro.  That is the explanation of large-scale effects through the examination of small-scale behaviours.  I look at how these develop over space and time.

So, more specifically, I look at how road congestion forms in cities and how we, as travellers, all contribute towards it.

As part of my early work on this stuff, I developed a simulation looking at how traveller decisions impact on the flow of traffic in adverse situations.  This consisted of the development of an Agent-based Model (ABM) using the Java-based Repast Simphony framework.  After a fair bit of faffing with Repast (which, I should add, is great although has a considerable learning curve in comparison to some ABM software), I have a model that demonstrates the impact of road closures across a population of driving agents.

The video below shows how the population of individually-cognating agents move from an area of origins (in green) to an area of destinations (in red) through London.  All of the agents move through geographic space, specifically an area around UCL in Euston.  So, this first video shows the normal situation, the next video will show how that changes once we mess things up a bit. (By the way, the video takes a few seconds to get moving, just allowing me a few seconds of in-lecture explanation).

Although the model is relatively simple in traffic simulation terms (with no traffic lights and regulations etc), I think it does show where concentrations of traffic form.  Particularly through the Euston Road/Tottenham Court Road junction.  So, what would happen if we closed this junction?  This…

I think it’s interesting to see the redistribution in traffic around the network.  Knowing that this junction is closed, you get a lot more movement along other roads suggesting that traffic would be considerably slower in these areas.  Clearly, the exact where’s and when’s in this scenario are some way of what reality might show.  Not only do we not have the impact of road regulations, but each individual holds a perfect knowledge of the network, proceeds towards their target along the shortest path and has prior knowledge of the closure ahead.  These are three important aspects I address in other pieces of work that I’ll put up later.  I also realise a bit of flow data would be quite useful here, but considering the pure conjecture of this scenario I’m not sure it’ll add much!