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Evaluation of Speed-Based Travel Time Estimation Models

机译:基于速度的旅行时间估计模型的评估

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Travel time estimation models, which rely on speed data provided from point detectors (usually inductive loops), have application in travel time prediction and network performance monitoring. Unfortunately there are limited, and at times counterintuitive, results in the literature about their performance. This paper focuses on the field evaluation of four speed-based travel time estimation models, namely, the instantaneous model, the time slice model, the dynamic time slice model, and the linear model. Those models are evaluated using data from two operational motorways in Melbourne, Australia. Travel time estimation errors are quantified against actual travel times measured using a timed number plate survey and time-stamped toll tag data. There was little difference in the travel time estimation error across the models and they were all found to underestimate actual travel times. Errors ranged from about 7% in the off peak up to 15% in the peak. Marginal improvements in model performance were achieved through careful selection of which detectors provide input for each section (upstream, downstream, or the average of those values) and by conversion of the inputs from time mean speed to an estimate of space mean speed.
机译:行程时间估计模型依赖于从点检测器(通常是感应回路)提供的速度数据,已在行程时间预测和网络性能监控中应用。不幸的是,有关其性能的文献报道有限,有时违反直觉。本文着重于四种基于速度的行程时间估计模型的现场评估,即瞬时模型,时间切片模型,动态时间切片模型和线性模型。这些模型是使用来自澳大利亚墨尔本的两条运营高速公路的数据进行评估的。使用定时车牌调查和带时间戳的收费标签数据,可以根据实际旅行时间对旅行时间估计误差进行量化。各个模型的旅行时间估计误差几乎没有差别,并且发现它们都低估了实际旅行时间。误差范围从非峰值的约7%到峰值的15%。通过仔细选择哪些探测器为每个部分(上游,下游或这些值的平均值)提供输入,以及通过将输入从时间平均速度转换为空间平均速度的估计值,可以实现模型性能的显着改善。

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