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首页> 外文期刊>Procedia - Social and Behavioral Sciences >Improving short-term travel time prediction for on-line car navigation by linearly transforming historical traffic patterns to fit the current traffic conditions
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Improving short-term travel time prediction for on-line car navigation by linearly transforming historical traffic patterns to fit the current traffic conditions

机译：通过线性转换历史交通模式以适应当前交通状况，改善在线汽车导航的短期旅行时间预测

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This research is focused on the problem of travel time prediction for a personal on-line car navigation system. The aim of this study is to improve the short-term travel time prediction quality by creating a dynamic model that utilises the real-time GPS floating car data (from the users of a car navigation system), assuming a static black box model of historical traffic patterns is given as a base. A novel model is introduced for this task. It combines two methods: the first one (applied on the city level) is based on linearly transforming traffic patterns to fit the current traffic conditions by solving a weighted and regularised regression problem. The second method (applied on short road segments) is based on exponential smoothing. The models quality is evaluated through extensive experiments on real data, by measuring the squared prediction error on the chosen observations that has not previously influenced the examined model. The results show significant improvement over the static historical traffic patterns model.
机译：这项研究的重点是个人在线汽车导航系统的旅行时间预测问题。这项研究的目的是通过创建一个动态模型来提高短期旅行时间的预测质量，该模型利用实时GPS浮动汽车数据（来自汽车导航系统的用户），并假设一个历史上的静态黑匣子模型流量模式作为基础。为此任务引入了一种新颖的模型。它结合了两种方法：第一种方法（适用于城市级别）是基于线性变换交通模式以解决加权和正则化回归问题以适合当前交通状况的方法。第二种方法（适用于短路段）基于指数平滑。通过对真实数据进行广泛的实验，通过测量所选观测值的平方预测误差来评估模型质量，该误差先前未影响所检查的模型。结果表明，与静态历史流量模式模型相比，该方法有显着改进。

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