A bottleneck automatic identification algorithm based on loop detector data is proposed. The proposed algorithm selects the critical flow rate as the trigger variable of the algorithm which is calculated by the road c...A bottleneck automatic identification algorithm based on loop detector data is proposed. The proposed algorithm selects the critical flow rate as the trigger variable of the algorithm which is calculated by the road conditions the level of service and the proportion of trucks.The process of identification includes two parts. One is to identify the upstream of the bottleneck by comparing the distance between the current occupancy rate and the mean value of the occupancy rate and the variance of the occupancy rate.The other process is to identify the downstream of the bottleneck by calculating the difference of the upstream occupancy rate with that of the downstream.In addition the algorithm evaluation standards which are based on the time interval of the data the detection rate and the false alarm rate are discussed.The proposed algorithm is applied to detect the bottleneck locations in the Shanghai Inner Ring Viaduct Dabaishu-Guangzhong road section.The proposed method has a good performance in improving the accuracy and efficiency of bottleneck identification.展开更多
Loop detectors are wide-spread and relatively cheap detecting devices. With today＇s traffic communication revolution, high resolution detector data is available on a central traffic management level. High resolution ...Loop detectors are wide-spread and relatively cheap detecting devices. With today＇s traffic communication revolution, high resolution detector data is available on a central traffic management level. High resolution detector data consist of detector slopes, also called pulse data. There is an initial and continuous need for checking the detectors for correct data as all kinds of disturbances may add erroneous information to the data. This paper proposes pulse data checking and interval data checking with optional data replacement in order to guarantee a continuous data flow even if detectors do not deliver the expected data quality： Raw detector data checking analyses rising and falling slopes of detector signals; Cumulative data checking compares interval values to reference curves. Cumulative data checking needs less computational effort, but needs more parameterization effort than raw detector data checking. Both checking principles are applied to different systems in Switzerland since about five years.展开更多
文摘A bottleneck automatic identification algorithm based on loop detector data is proposed. The proposed algorithm selects the critical flow rate as the trigger variable of the algorithm which is calculated by the road conditions the level of service and the proportion of trucks.The process of identification includes two parts. One is to identify the upstream of the bottleneck by comparing the distance between the current occupancy rate and the mean value of the occupancy rate and the variance of the occupancy rate.The other process is to identify the downstream of the bottleneck by calculating the difference of the upstream occupancy rate with that of the downstream.In addition the algorithm evaluation standards which are based on the time interval of the data the detection rate and the false alarm rate are discussed.The proposed algorithm is applied to detect the bottleneck locations in the Shanghai Inner Ring Viaduct Dabaishu-Guangzhong road section.The proposed method has a good performance in improving the accuracy and efficiency of bottleneck identification.
文摘Loop detectors are wide-spread and relatively cheap detecting devices. With today＇s traffic communication revolution, high resolution detector data is available on a central traffic management level. High resolution detector data consist of detector slopes, also called pulse data. There is an initial and continuous need for checking the detectors for correct data as all kinds of disturbances may add erroneous information to the data. This paper proposes pulse data checking and interval data checking with optional data replacement in order to guarantee a continuous data flow even if detectors do not deliver the expected data quality： Raw detector data checking analyses rising and falling slopes of detector signals; Cumulative data checking compares interval values to reference curves. Cumulative data checking needs less computational effort, but needs more parameterization effort than raw detector data checking. Both checking principles are applied to different systems in Switzerland since about five years.
