Detection of Incidents and Events in Urban Networks

Although there is a large variation in traffic flow patterns, we can distinguish two main types: recurrent and non-recurrent patterns. A recurrent pattern repeats itself with a known period and is therefore predictable. An example is the rush hour peak, but also the peak in travel demand which is caused by a weekly event, like a professional football match. Non recurrent patterns are caused by single events and incidents. Although these are relatively rare, they often have a very negative impact on the traffic situation (e.g. Smith et al. 2003). An important issue for policy makers therefore has been to reduce these negative effects. Much effort has gone into the development of incident detection algorithms (e.g. Browne et al. 2005, Koppelman & Lin 1996) which form a crucial part of incident management. These algorithms use real-time measurements of volume, occupancy and/or speed to detect incidents. There are several types of detection algorithms. Some of them are based on neural networks (e.g. Liu et al. 2004, Ritchie & Cheu 1993). In other algorithms an incident is declared when the data fulfill certain pre-selected criteria. In time-serie algorithms for example an incident is detected when the measurements differ significantly from the predictions (e.g. Stephanedes et al. 1992, Dudek et al. 1974). With the McMaster algorithm different traffic situations (e.g. incidents) are distinguished based on the location of measurements in the occupancy – flow volume diagram (e.g. Hall et al. 1993). Finally, in decision structure algorithms incidents are detected when measurements exceed thresholds in a decision tree (e.g. Ash 1997, Payne & Tignor 1978). In The Netherlands a decision structure algorithm has been developed for the Dutch motorways which appears to be quite successful (Knibbe et al. 2005). Complementary, in this paper we introduce a prediction scheme for recurrent events and an incident detection algorithm based on data from a Dutch city. For our detection algorithm we only use volume data. In section 2 we describe the data and in section 3 we introduce our incident detection method