01025971

Component

A searching-free analytical procedure is mostly utilized with Adaptive Traffic Control Systems (ATCS) because of its short and stable computation time even though it suffers from feasibility limited to the designed conditions. Artificial Neural Network (ANN) is a perceptual model, which may overcome the shortcomings of an analytical model. However, ANN was concerned about its sensitivity that may cause safety problems in the field. This paper presents performance comparison between an analytical model, a degree-of-saturation based cycle length design model of Cycle-Offset-Split-Models-Of-Seoul (COSMOS) and an ANN model developed for this study. Cycle lengths from these models were compared against the ones suggested by TRANSYT-7F and SYNCHRO at various demand levels. It was found that the ANN model provides with the optimal cycle lengths stably adjusted by the minimum, the maximum, and a cycle increment, while the analytical model promotes congestion at certain operational conditions considered in the test.

01020180

06-2038

English

22p

2006

Transportation Research Board 85th Annual Meeting

CD-ROM

Figures (7) ; References (21) ; Tables (4)

Adaptive control; Area traffic signal control; Demand; Highway traffic control; Modal split; Neural networks; Traffic signal control systems; Traffic signal cycle; Travel demand

TRANSYT (Computer program)

Adaptive traffic control

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

Transportation Research Board Annual Meeting 2006 Paper #06-2038

TRIS, TRB

Mar 3 2006 10:51AM