Gaining new insights regarding traffic congestion, by explicitly considering the variability in traffic

Gaining new insights regarding traffic congestion, by explicitly considering the variability in traffic

Miete, O.M.

Hoogendoorn, S.P. (mentor)
Vrijling, J.K. (mentor)
Van Gelder, P.H.A.J.M. (mentor)
Van Lint, J.W.C. (mentor)
Taale, H. (mentor)
Wiggenraad, P.B.L. (mentor)

Civil Engineering and Geosciences

Hydraulic Engineering / Transport & Planning

2011-01-13

In hydraulic engineering it is known that for the evaluation of the performance of a system, a probabilistic approach is preferable to a deterministic one. The essence of such a probabilistic approach is that random variability/uncertainty is explicitly taken into account. In this graduation project, this probabilistic way of looking at a system is applied to the traffic system, in the context of analyzing (ways to alleviate) traffic congestion. Basically, the mechanism behind traffic congestion can be described as a process of interaction between the traffic demand and supply on a road network. Both this traffic demand and supply show a significant level of temporal variability, which makes the resulting traffic conditions variable as well. Traditionally, in evaluations of the effectiveness of proposed congestion relief measures this variability is taken into account only in a limited or simplified way, or even not at all. Often simply a kind of ‘representative’ situation is calculated. The main objective of this research project was to reveal what kind of new insights can be obtained if we actually do explicitly/systematically take into account the variable nature of daily motorway congestion. After a comprehensive study into the sources of the variability in the traffic conditions, and the selection of appropriate performance indicators, a quantification model was developed. The main principle of this model is that a large number of traffic simulations are performed for varying traffic demand and supply values. Subsequently, the desired performance indicators are computed from the combined set of simulation results. In order to explore the (potential) new insights obtained by explicitly considering the variability, the developed model was applied to a reasonably sized real-life motorway network. From the results it is clear that a ‘representative’ calculation (in which all demand and supply variables are taken at their ‘representative’ level, which for example could be the mean or median value) does not give a good impression of the performance of the traffic system. It underestimates the congestion in certain respects, and – obviously – does not provide information on the uncertainty in travel times (which is an important factor in the societal costs of traffic congestion). The research has shown that if the variability in traffic is explicitly considered, new insights can be obtained into the relative importance of different (variable) influence factors. This was demonstrated by ‘deactivating’ these influence factors in the model (one at a time). The results of this demonstration indicate that the capacity variations due to the intrinsic randomness in human driving behavior play a central role in (peak period-related) congestion. Such information yields important insights into how traffic congestion can be remedied most effectively. By considering the example of a rush-hour lane, the research has shown that new insights can also be obtained into the effectiveness of specific measures that are proposed to alleviate traffic congestion. It turned out that the ‘traditional’ way of evaluating may actually result in a significant underestimation of the benefits of a measure. The precise nature and extent of the additional/revised insights will be highly context and measure specific, however. Of course, these new insights are not necessarily all positive in nature. Some more negative aspects of a measure could be brought to light as well. The above implies that in practice more systematic attention should be given to the variability in traffic, when evaluating the effectiveness of measures that are proposed to alleviate congestion. Because of the complexity involved, this would have to be done by using a model in which the different sources of variability are explicitly accounted for, such as (a further developed version of) the model developed in this project.

traffic congestion
variability
probabilistic
measures
evaluation
performance
traffic demand
traffic supply
variable
representative
randomness
stochasticity
stochastic
effectiveness
alleviate
traffic conditions
motorway
variation
variations
evaluations
simulation

http://resolver.tudelft.nl/uuid:2673e7dd-af22-4d35-b3f7-e051d665bd80

Student theses

master thesis

(c) 2011 Miete, O.M.