The aim of this post is to comprehensively summarize the key factors complexity science seems to identify when looking at road traffic inefficiencies. I will then be discussing how looking at alternative systems of collective motion, such as the behavior of ants and birds, might help us to further enhance our understanding of these inefficiencies. This post will also hint at possible ways in which one might want to apply these findings to the research question.
Having looked at both, choice and action-based approaches to road traffic, the following findings appear to be most relevant.
1) Over time, we might observe a decrease in system efficiency as individual agents optimize their behavior according to their respective key parameters
2) External efforts to reduce these inefficiencies, i.e. through the provision supplementary roads, might actually further increase the inefficiency of the system
3) Not the density of the vehicles on a particular stretch of a road, but the interaction between these vehicles represents the origin of endogenous breakdowns of the free flow of traffic (through backward spreading velocity fluctuations)
4) The spread of a congestion from a local level to the system level crucially depends on the level of cooperation that drivers choose at the junction level.
Looking at these factors, one might already be able to think of ways of how to apply those results to the research question. For now though, I would like to look at a group of other systems that exhibit both complex and traffic behavior as well. Contrasting the efficiency and the inherent incentive structure of both systems will hopefully further enhance our understanding by gaining yet another perspective on the conclusions so far.
Ants and birds are famous for exhibiting sophisticated forms of collective motion. Colonies of the New World army ant Eciton burchelli for example consist of up to a half million members. The ants form traffic systems made up by up to 200,000 virtually blind individuals that transport up to 30,000 items in one run (Franks et al, 1991) and display minimal congestions. Likewise birds, the interaction of ants hence can give rise to self-organized structures that seem to be vastly superior to road traffic in terms of efficiency. These structures display a swarm intelligence that vastly exceeds the intelligence of every individual member and that hence is beneficial to every participant. In fact, scholars were capable of simulating the behavior of ants without having to assume that the ants possess any form of memory at all (e.g. Millonas: Swarms, Phase Transitions, and Collective Intelligence").
For the purposes of this project it will be sufficient to look at the simplest forms of models that aim to simulate the collective behavior of ants and birds and at the assumptions with regards to the behavioral incentive-structures that they make. Craig Reynolds introduced an agent-based model for the aggregate motion of flocks, herds or swarms in his paper "Flocks, Herds and Schools: A Distributed Behavioral Model (1987)" that simulated birds as independent individual actors that navigate according to their local perceptions of the environment and a set of of behavioral patterns. The behaviors that lead to the simulated collective motion are as following:
1) Decision-makers seek to avoid collisions with other agents (Collision avoidance)
2) Decision-makers attempt to match their speed with other nearby agents (Velocity matching)
3) Decision-makers want to stay as close as possible to nearby agents (Flock centering)
Given a certain density of interacting agents, these behavioral patterns are sufficient to bring about realistic forms of swarming behavior and hence the transition from chaotic to ordered behavior. The graphical results of Reynold's simulations can be seen on the following page: http://www.red3d.com/cwr/boids/ . These principles also represent the cornerstones of other, more sophisticated self-propelled particle models that aim to simulate the complex behavior displayed by New World ants or birds.
How can this analysis help us to further enhance our understanding of factors that might contribute to the occurrence of endogenous congestions in road traffic? Comparing the behavioral patterns and conditions that allow for the occurrence of the respective macro-structures, one finds that it seems to be the flock centering behavior that makes the crucial difference.
Both collision avoidance and velocity matching are to an extent inherent to the behavioral structure of drivers on the road and have in fact been identified to cause the fluctuations in velocity that can potentially cause the breakdown of the free traffic flow. Flock centering on the other hand reflects the inherently different incentive-structures on the microscopic level of individual agents between both systems. Whilst an ant or a bird greatly benefits from staying close to other flockmates, a driver on the road when faced with a choice between two routes of equal length will always pick the less crowded one. For ants and birds , a sufficiently high density of interacting agents is necessary for collective motion to occur in the first place, whilst the density of vehicles on the road is a necessary condition for the endogenous occurrence of traffic jam.
Cooperation and mutual interests on the individuals' level hence seem to crucially affect the efficiency of a system. This can be seen to further underline and support the implied conclusion of our previous analysis that incentive-structures might to an extent turn out to be more important than macro factors, such as the number of available roads for the efficiency of road traffic. The introduction of a second class of reference models therefore allows us to further strengthen the results of our analysis.
The next post will make use of the results obtained so far by applying these results to the observation of sudden starts and stops in business cycles. In order to so it will, based on the previous discussion on how road traffic could represent a reference model for aggregate economic, behavior carefully construct analogies between road traffic and the economy that will help to reveal the relevance of this findings for the research question. The goal has to be to comprehensively formulate a hypothesis that can then be tested against the available empirical evidence.
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