Accurate vehicle travel times are a prerequisite for many applications in the mobility domain as well as applications which are interested in eơects of reachability. This paper describes a novel approach to estimate travel times and their diurnal variation in urban street networks which uses only static map attributes and centrality measures extending the work presented in Leodolter et al.(2015). The method provides a low-cost alternative to expensive travel time measurement campaigns. By integrating closeness and betweenness centrality measures, the model is expanded to take advantage of previously neglected spatial information.

Centrality measures have been used, for example, to study city structure (Crucitti et al. 2006) or explain land use intensity (Wang et al. 2011), and retail and service activity (Porta et al. 2009). In the context of motorized traƥc, betweenness centrality has been used as an indicator to predict traƥc flows. For example, Jiang (2009) shows that street hierarchies derived from street length, connectivity, and betweenness are a good indicator for traƥc flow. Puzis et al.(2013) present a betweenness-driven traƥc assignment model which can take into account travel demand and model travel times. Similarly, Gao et al.(2013) combine betweenness with travel demand data and geographical constraints to predict traƥc flow. To the best of our knowledge, there is no work so far which uses centrality measures to model travel times and their diurnal variation. Our model predicts vehicle travel times for a given time of day in 15 minute intervals.