详情

原文传递 LEARNING TO ASSOCIATE OBSERVED DRIVER BEHAVIOR WITH TRAFFIC CONTROLS.

题名：	LEARNING TO ASSOCIATE OBSERVED DRIVER BEHAVIOR WITH TRAFFIC CONTROLS.
作者：	Pribe-CA; Rogers-SO
关键词：	BEHAVIOR-; DRIVERS-; INTERSECTIONS-; NEURAL-NETWORKS; TRAFFIC-CONTROL-DEVICES; TRAFFIC-ENGINEERING; TRAFFIC-FLOW
摘要：	Adaptive techniques support the development of new tools to help traffic engineers classify and evaluate traffic flow at intersections. A tool that learns to associate driver behavior with a subset of traffic controls (e.g., stoplights and stop signs) is described. In the case in which the traffic controls for an intersection are not readily available or are unknown, the tool automatically identifies the traffic controls present at an intersection from observed driver behavior. This capability may be used to augment digital maps with traffic control locations. In the case in which traffic controls are known or have previously been classified, the tool flags instances in which driver behavior is inconsistent with the traffic controls actually present. This capability might be used by various services for drivers such as dynamic routing and new safety systems. It might also be used by traffic engineers to evaluate control placement in real or simulated road networks by finding situations that elicit unusual driver behavior. The tool is calibrated with driving data for a set of segments with known controls. The tool first learns to identify the controls present on individual road segments and then uses handcrafted rules to verify control consistency across segments at intersections. The data set comprised real-world position data collected during normal daily driving. The tool accurately identified 100% of the data that passed verification. These results encourage belief that the system can provide traffic engineers with a reliable mapping between driver behavior and traffic controls.
总页数：	Transportation Research Record. 1999. (1679) pp95-100 (4 Fig., 1 Tab., 5 Ref.)
报告类型：	科技报告