Increasing Freeway Network Resilience by Advanced Congestion Pattern Analytics

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 predefined congestion patterns. Determining the spatial and temporal extensions of several congestion events, hot spots of each pattern can be localized. Additionally, possible traffic management and control measures are compiled and evaluated by expert statements to mitigate and dissolve the found congestion hot spots. This approach provides a helpful toolbox for freeway operators to classify occurring congestion into predefined categories and to select appropriate countermeasures based on the hot spot analysis to increase the resilience of the overall system. By applying the presented methodology, optimized traffic information is provided to the operator in time-critical situations, which enables an improved decision- making process in traffic management. The data base is three large-scale data sets from stationary detectors, vehicle re-identification sensors, and floating car data collected on a German freeway in 2019.     «

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 predefined congestion patterns. Determining the spatial and temporal extensions of several congestion events, hot spots of each pattern can be localized. Additi...     »