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MULTILAYER ARTIFICIAL NEURAL NETWORKS FOR LEVEL-OF-SERVICE ANALYSIS OF SIGNALIZED INTERSECTIONS.
| 题名： | MULTILAYER ARTIFICIAL NEURAL NETWORKS FOR LEVEL-OF-SERVICE ANALYSIS OF SIGNALIZED INTERSECTIONS. |
| 作者： | Saito-M; Fan-J |
| 关键词： | ACCURACY-; ADVANCED-TRAFFIC-MANAGEMENT-SYSTEMS; HIGHWAY-CAPACITY; LEVEL-OF-SERVICE; NEURAL-NETWORKS; SIGNALIZED-INTERSECTIONS; SOFTWARE-; TRAFFIC-DELAY |
| 摘要： | The effects of architecture, learning mode, and learning rate on the performance of a level-of-service (LOS) analysis model using an artificial neural network (ANN) are discussed. Multilayer LOSANN models demonstrated improved quality of learning and testing over single-layered models in evaluating level of service of signalized intersections given geometric, traffic, and traffic signal control data. At present, LOSANN takes delay data from Highway Capacity Software (HCS) outputs; hence its accuracy is constrained by the accuracy of the HCS analyses. However, if delays can be determined directly by field observation, the relationships (or patterns) between field-measured delays and the traffic, geometric, and signal control conditions can be fed to LOSANN. Then the neural network-based model can evaluate the level of service at a higher level of accuracy, and such models can be used as part of advanced traffic management systems to automate LOS analyses. |
| 总页数： | Transportation Research Record. 1999. (1678) pp216-224 (4 Fig., 8 Tab., 7 Ref.) |
| 报告类型： | 科技报告 |
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