01128725

Component

It is essential for local traffic jurisdictions to systematically spot freeway bottlenecks and proactively deploy congestion mitigation strategies accordingly. However, the analysis results may be influenced by unreliable measurements, analysts' subjective knowledge and day-to-day traffic pattern variation. In order to suitably address these uncertainties and data imprecision, this study proposes a fuzzy-based system approach for bottleneck severity diagnosis in urban sensor networks. The proposed approach integrates a dynamic bottleneck identification model in a fuzzy logic framework to systematically diagnose the severities of recurring and nonrecurring bottlenecks by incorporation of expert's knowledge on local traffic conditions. The dynamic bottleneck identification model can be integrated into a real-time active traffic management control system to automatically trigger control strategies based on the estimated queue length. A sample of one month data on an urban freeway in Northern Virginia was considered as a case study for the analysis. The results suggest that the proposed approach can reasonably determine bottleneck severities and critical links in the sense of accounting for both spatial and temporal factors in a sensor network.

01120148

09-2231

English

34p

2009

Transportation Research Board 88th Annual Meeting

DVD

Figures; Maps; Photos; References; Tables (2)

Bottlenecks; Congestion management systems; Fuzzy systems; Highway traffic control; Incident management; Real time information; Sensors; Traffic congestion; Traffic control; Traffic control centers; Traffic incidents; Travel surveys

Northern Virginia

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2009 Paper #09-2231

TRIS, TRB

Jan 30 2009 6:35PM