Further lessons about road traffic

The last post introduced two fundamental approaches to road traffic modeling. It mainly focused on the choice-based approach, meaning that it discussed papers that studied the way in which people choose a particular route and how this affects the performance of the system as a whole. This post is to focus on the action-based approach which is centered around the question how driving behavior affects the efficiency of road traffic.

Sugiyama et al. argue that factors that are related to the density of cars on the road, such as route-choices or bottlenecks, are  "only a trigger and not the essential origin of a traffic jam" (Sugiyama et al: "Traffic jams without bottlenecks—experimental evidence for the physical mechanism of the formation of a jam", 2002).  Clearly, this fits well into our interpretation of action-based approaches to road traffic.

Sugiyama et al  argue  that the development of traffic flow, given that the average vehicle density surpasses a certain critical point, is crucially dependent on the interaction of vehicles. They back up this claim both theoretically and experimentally.   In their paper, road traffic is modeled  "as a non-equilibrium physical system consisting of moving particles with asymmetric interaction of exclusive effect".

Their model is based on the experience that there are always fluctuations in the movement of vehicles as vehicles  adjust their speed when they see other drivers in order to avoid collisions . If the vehicle density is sufficiently small, these fluctuations can disappear and the free flow of traffic is ensured. If on the other hand the vehicle density is beyond a certain critical value, the fluctuations can potentially  grow steadily and eventually cause a breakdown of the free flow that manifests itself in the formation of a jam. Hence once a critical vehicle density is surpassed, the system mathematically exhibits two solutions. A free flow solution where all vehicles move at roughly the same velocity and a jam flow solution where vehicles are stuck in a cluster. These solutions are essentially not stable over time and the system will alternate between both in irregular patterns.

The authors conducted an experiment on a circular road in order to verify these theoretical results. As predicted, traffic jam did occur once a certain density level was reached and the velocities of the cars were controlled by drivers (as opposed to automatic velocity control). The following video that captures the experiment illustrates, how traffic jam emerges as a result of steadily growing fluctuations in velocity. These in turn are caused by individual agents' reactions to other agents.

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Building on this result Manley and Cheng develop a model that describes the development of congestion as an emergent property of road traffic that is rooted in the microcosm of the behavior of individual drivers and try to identify circumstances under which a local congestion might lead to a network congestion. (Manley, Cheng: "Understanding Road Congestion as an Emergent Property of Traffic Networks", 2008).

In order to do so, they distinguish three different levels, namely the link, junction and network level. The link level refers to roads that are relatively unaffected by junctions or intersections (like the circular road in Yugisama's experiment). Here, congestion may start  through the mechanism that Yugisama et al have described, i.e. increasing fluctuations that cause delays which in turn spread backwards. But as soon as congestion reaches Junction level, this relative linearity fades as individuals make choices that may or may not contribute to the spread of the congestion to network level. Such choices include whether or not to cooperate or to change the route at the junction. These choices will determine whether or not a congestion at link (i.e. local) level will spread to affect a wider area of road network, i.e. whether or not a congestion will spread over many junctions and links to affect the macro network.

Using the Blackwall Tunnel in London as a case study, the authors find that it is especially illegal or uncooperative behavior that contributed to the spread of a congestion from the local to the network level. In other words it is especially the selfishness of individual drivers that could lead to temporary or even permanent delay.


Summarising what we have learned from these two papers, one can say that action-based approaches to road traffic provide us with another perspective on the emergence of inefficiencies in traffic flows. We went from developing an idea of the emergence of traffic jam on the individual level to trying to understand, how local congestions spreads to become a network congestion. It gave us an idea of how we could see traffic jam to be more dependent on the interaction and choices of drivers whilst on the road than on the actual vehicle density. Both discussed papers might prove themselves to be highly relevant for the purposes of this project as they provide a framework of discussing why some local congestions, caused by asymmetric actions and the resulting fluctuations, do actually lead to a congestion on network level, whilst others do not. Relating this to business cycles, this might be helpful in explaining how the sudden starts and stops of economic activity come about and why some of them do actually reverse the business cycle, whilst others do not.

The next post will bring together the results of this and the previous post. It appears to be essential to comprehensively identify factors that contribute to and affect the performance of the system road traffic in order to proceed with our analysis. The next post will also study, how the behavior of ants and birds does essentially differ from the behavior of road drivers and how this contributes to the efficiency of the respective system.