Analysing traffic fluency from bus data

Abstract

The use of stored public transportation data facilitates the identification of potential issues with urban traffic flow. Focusing on buses, the authors proceed from a city‐level delay distribution analysis to a detailed understanding of the factors that cause the delays on an example bus line. First, a database of bus data in Tampere was mined to detect any regular patterns in the distribution of delays in time, location or according to bus line throughout the city. The results allow the authors to focus on those areas and lines which are most prone to delays. In a case study, they illustrate that the most important reasons for tardy journeys are the long waiting times at traffic signals and bus stops, rather than slow driving speeds. The results are then further deepened to show spatially on a map which bus stops and intersections tend to be the ones where the time variances are high. The same methods can be applied to any other city for which the same kind of data are available.

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