Route Travel Time Estimation on A Road Network Revisited: Heterogeneity, Proximity, Periodicity and Dynamicity

Haitao Yuan, Guoliang Li, Z. Bao
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引用次数: 2

Abstract

In this paper, we revisit the problem of route travel time estimation on a road network and aim to boost its accuracy by capturing and utilizing spatio-temporal features from four significant aspects: heterogeneity, proximity, periodicity and dynamicity. Spatial-wise, we consider two forms of heterogeneity at link level in a road network: the turning ways between different links are heterogeneous which can make the travel time of the same link various; different links contain heterogeneous attributes and thereby lead to different travel time. In addition, we take into account the proximity: neighboring links have similar traffic patterns and lead to similar travel speeds. To this end, we build a link-connection graph to capture such heterogeneity and proximity. Temporal-wise, the weekly/daily periodicity of temporal background information (e.g., rush hours) and dynamic traffic conditions have significant impact on the travel time, which result in static and dynamic spatio-temporal features respectively. To capture such impacts, we regard the travel time/speed as a combination of static and dynamic parts, and extract many spatio-temporal relevant features for the prediction task. Talking about the methodology, it remains an open problem to build a generic learning model to boost the estimation accuracy. Hence, we design a novel encoder-decoder framework - The encoder uses the sequence attention model to encode dynamic features from the temporal-wise perspective. The decoder first uses the heterogeneous graph attention model to decode the static part of travel speed based on static spatio-temporal features, and then leverages the sequence attention model to decode the estimated travel time from spatial-wise perspective. Extensive experiments on real datasets verify the superiority of our method as well as the importance of the four aspects outlined above.
道路网络的行程时间估计:异质性、邻近性、周期性和动态性
本文从异质性、接近性、周期性和动态性四个重要方面对路网的时间估计问题进行了研究,旨在通过捕获和利用路网的时空特征来提高其准确性。在空间层面上,我们考虑了道路网络中两种形式的异质性:不同路段之间的转弯方式是异构的,这使得同一路段的行驶时间不同;不同的链路包含不同的属性,从而导致不同的旅行时间。此外,我们还考虑了邻近性:相邻的链接具有相似的交通模式,并导致相似的旅行速度。为此,我们建立了一个链接连接图来捕捉这种异质性和接近性。在时间上,时间背景信息(如高峰时间)和动态交通状况的周/日周期性对出行时间有显著影响,分别形成静态和动态时空特征。为了捕捉这些影响,我们将旅行时间/速度视为静态和动态部分的组合,并提取了许多时空相关特征用于预测任务。在方法方面,如何建立通用的学习模型来提高估计精度仍然是一个有待解决的问题。因此,我们设计了一种新的编码器-解码器框架-编码器使用序列注意模型从时间的角度对动态特征进行编码。该解码器首先利用异构图注意模型对基于静态时空特征的旅行速度静态部分进行解码,然后利用序列注意模型从空间角度对估计的旅行时间进行解码。在真实数据集上的大量实验验证了我们方法的优越性以及上述四个方面的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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