仅从相互探测时间估算探测车渗透率的贝叶斯推断法

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
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引用次数: 0

摘要

浮动车辆提供的数据与不完善的固定检测器提供的数据具有相同的局限性:它们提供的数据在总流量中所占比例未知。由于对这一比例的了解对某些应用至关重要,因此通常通过其他监测基础设施提供的数据进行补充来估算其价值。然而,在许多城市,尤其是发展中国家,这类基础设施并不常见。本文对之前提出的一种方法进行了扩展,可以仅通过一系列检测间时间来估算这种渗透率。改进后的方法以贝叶斯推理为基础,支持来自多车道基础设施的一系列通过时间,尽管仍然需要不间断的交通机制。该方法被应用于智利圣地亚哥一条四车道交织城市高速公路的实际 RFID 交叉检测时间。得出的估计比率与高速公路运营商报告的比率一致,显示了该方法的能力。因此,该方法有助于成功地充分利用许多城市已有的技术,而无需对监控基础设施进行新的投资。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian inference for estimating the penetration rate of probe vehicles from interdetection times only

Data provided by floating vehicles have the same limitation as that provided by an imperfect stationary detector that undercounts vehicles: they provide data from an unknown proportion of the total flow. Since the knowledge of this proportion is critical for some applications, its value is usually estimated by complementing with data provided by other monitoring infrastructure. However, this type of infrastructure is not the norm in many cities, particularly in developing countries. This paper extends a previously proposed method to estimate this penetration solely from a series of inter-detection times. The improved method is based on Bayesian inference and supports a series of passage times coming from multilane infrastructure, albeit still requiring an uninterrupted traffic regime. The method was applied to actual RFID interdetection times from a four-lane weaving urban freeway segment in Santiago, Chile. The resulting estimated rates are consistent with those reported by the freeway operator, showing the method’s capabilities. Thus, the method is instrumental in successfully taking full advantage of technology already in place in many cities without needing new investments in monitoring infrastructure.

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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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