Figure 2 - uploaded by Martin Margreiter
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Source publication
This paper presents an approach that increases the resilience of a freeway network while
differentiating patterns of freeway congestion events and investigating hot spots of each
pattern both spatially and temporally. Based on an automated pattern recognition, an
emerging congestion event can be identified and classified into one of four predefi...
Contexts in source publication
Context 1
... detector data (SDD; i.e., loop or radar sensors) are provided from 43 sensors in Northbound (NB) and from 50 sensors in Southbound direction (SB). Data are available from several separate sections of the road (see Figure 2). SDD are available from eight months. ...
Citations
... In [11], the authors introduced four congestion types, which were also critically reviewed in [24]- [27]: Jam Wave, Stop and Go, Wide Jam, and Mega Jam. The single congestion wave is a thin stripe within the space-time diagram implying a temporarily low speed. ...
This paper investigates the detection rate of various freeway congestion patterns and compares them across different traffic sensor technologies. Congestion events can be categorized into multiple types, ranging from short traffic disruptions (referred to as Jam Wave) to Stop and Go patterns and severe congestion scenarios like Wide Jam. We analyze multiple traffic data sets, including speed data from loop detectors, travel time measurements from Bluetooth sensors, and floating car data (FCD) collected from probe vehicles. Each combination of congestion pattern and detection technology is thoroughly examined and evaluated in terms of its capability and suitability for identifying specific traffic congestion patterns. For our experimental site, we selected the freeway A9 in Germany, which spans a length of 157 km . Our findings reveal that Bluetooth sensors, which record travel times between two locations, are barely suited for detecting short traffic incidents such as Jam Waves due to their downstream detection direction, contrasting with the upstream congestion propagation. Segment-based speed calculations prove more effective in identifying significant congestion events. FCD tend to recognize Stop and Go patterns more frequently than loop detectors but often underestimate severe congestion due to their sensitivity to penetration rates and data availability.
