研究成果

Automatic calibration of fundamental diagram for first-order macroscopic freeway traffic models

期刊名称: Journal of Advanced Transportation,
全部作者: 钟任新, 陈昶佳, A.H. CHOW, 潘天鹭, Z.HE*
出版年份: 2016
卷       号: 50
期       号:
页       码:
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Despite its importance in macroscopic traffic flow modeling, comprehensive method for the calibration offundamental diagram is very limited. Conventional empir ical methods adopt a steady state analysis of theaggregate traffic data collected from measurement devices installed on a particular site without consideringthe traffic dynamics, which renders the simulation may not be adapti ve to the variability of data. Nonethe-less, determining the fundamental diagram for each detection site is often infeasible. To remedy these, thisstudy presents an automatic calibration method to estimate the parameters of a fundamental diagram througha dynamic approach. Simulated flow from the cell transmission model is compared against the measuredflow wherein an optimization merit is conducted to minimize the discrepancy between model-generated dataand real data. The empirical resu lts prove that the proposed automatic calibration algorithm can significantlyimprove the accuracy of traffic state estimation by adapting to the variability of traffic data when compare dwith several existing methods under both recurrent and abnormal traffic conditions. Results also highlightthe robustness of the proposed algorithm. The automatic calibrat ion algorithm provides a powerful toolfor model calibration when freeways are equipped with sparse detectors, new traffic surveillance systemslack of comprehensive traffic data, or the case that lots of detectors lose their effectiveness for agingsystems. Furthermore, the proposed method is useful for off-line model calibration under abnormal trafficconditions, for example, incident scenarios