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Vehicle Classification Model for Loop Detectors Using Neural Networks.
| 题名： | Vehicle Classification Model for Loop Detectors Using Neural Networks. |
| 作者： | Ki-Yong-Kul; Baik-Doo-Kwon |
| 关键词： | Evaluation-and-assessment; Freeways-; Loop-detectors; Microwave-detectors; Noise-; Performance-; Speed-measurement |
| 摘要： | Loop detectors have been the preeminent detection technology for several decades, but they require closing of the right-of-way during installation and potentially undermine the integrity of the pavement surface if they are not installed before paving. As a result, there is great interest in emerging technologies that promise traffic detection without the liabilities of loop detectors, many of which have already been deployed in large numbers. The remote traffic microwave sensor (RTMS) is among the most widely deployed noninvasive traffic detectors. This study evaluates the performance of the RTMS in side-fire mode relative to loop detectors in freeway applications. First, the aggregated data reported by the RTMS using its internal controller emulation are compared with data from nearby dual-loop detectors. It is shown that the RTMS measures are noisier than loop detectors for occupancy (percentage of time the detector is occupied by vehicles) and flow (number of vehicles per unit time), although the RTMS velocity estimates are almost as good as those from single-loop detectors (but inferior to direct measurement from dual-loop detectors). Second, the study considers aggregate measurements from contact closure data and compares the RTMS against the dual-loop detectors. For reference, the work also compares one loop against another in a dual-loop detector, with the spacing between loops being greater than the spacing between the reference loops and the RTMS detection zone. |
| 总页数： | Transportation Research Record: Journal of the Transportation Research Board. 2005. (1917) pp149-163 (1 Phot., 9 Fig., 3 Tab., 17 Ref.) |
| 报告类型： | 科技报告 |
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