Convex Quantile Regression For Traffic Congestion Modelling

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School of Business | Master's thesis
Degree programme
Information and Service Management (ISM)
62 + 3
Precise and reliable prediction of highway traffic and performance becomes ever more important as the global car fleet continues to grow. Although traffic big data is more abundant than ever, traffic management is struggling to keep up with the immense growth. In the center of the global effort to fight the congestions’ chilling economic effect is a century-old framework known as traffic flow theory. This thesis argues that the tools of this framework do not utilize the present levels of computing power to the maximum and that there is a gap in modelling approaches regarding some recently discovered methodologies such as convex quantile regression, the method that this thesis proposes. This thesis explores the history of traffic flow theory, explains the methodologies that arise from the framework, and then presents constructive criticism in form of a novel modelling approach that, as far as the author knows, is unlike anything that has been ever done in the framework so far. To test out the convex quantile regression method’s predictive capabilities, we use traffic flow data from Finnish highway sensors. The convex quantile regression method has two functions. Firstly, the method can be used to build statistical confidence intervals for a measure known as highway capacity. Secondly, the method can be used as a traffic breakdown detection method. The results show a notable difference in the performances of some famous Finnish ring roads and highways in Greater Helsinki between summers and winters in terms of traffic breakdown counts. The results also show a change in the general stochastic characteristics of highway traffic between the seasons.
Thesis advisor
Kuosmanen, Timo
Malo, Pekka
regression analysis, nonparametric regression, nonparametric statistics, traffic congestion
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