Abstract
We study the problem of making algorithmic statistical inferences about the dynamics of city traffic. Our data is based on loop detector counts of observed vehicles in various roads in the city of Tampere, Finland. We show that meaningful correlations can be found between traffic asymmetries at different measurement locations. The traffic asymmetry is the difference of the traffic counts of the opposite directions of a road. The correlations can be further quantified by estimating how much they effect on the average values of the traffic asymmetries at the neighbouring locations. Conditional expectations, both sample and binormal model-based versions are useful tools for quantifying this effect. The uncertainty bounds of conditional expectations of the binormal model distribution are extremely useful for outlier detection. Furthermore, conditional expectations of the multinormal distribution model can be used to recover missing data with bounds to uncertainty.
Highlights
People’s travel behavior is initiated by the need to travel and choosing the mode, route and time for trips
5.2 Inference about traffic dynamics We show an example that we expect to be directly helpful for a traffic management centers (TMC) operator
6 Conclusion and discussion We have described an algorithmic framework to extract relevant information about traffic dynamics from shortterm traffic count data in the case where the traffic counts in the opposite directions are available in two or more mutually relevant locations
Summary
People’s travel behavior is initiated by the need to travel and choosing the mode, route and time for trips. Travel behavior can be expressed in transport planning and management by origin-destination matrices (OD), [1]. OD-matrices have generally been estimated based on travel behavior surveys and interviews. For example, allow short-term estimation by using realtime data on the traffic situation. Information on traffic dynamic predictions is beneficial in traffic management centers (TMC) and operations. With the development of Cooperative Intelligent Transport Systems (C-ITS) and automated and connected vehicles, traffic could be further optimized and rerouted based on the current traffic situation. When a risk for a congestion arises, the vehicles could be rerouted either automatically or by providing real-time route information to the drivers and vehicles [2]
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