AGNP: Network-wide short-term probabilistic traffic speed prediction and imputation

IF 12.5 Q1 TRANSPORTATION
Meng Xu , Yining Di , Hongxing Ding , Zheng Zhu , Xiqun Chen , Hai Yang
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引用次数: 1

Abstract

The data-driven Intelligent Transportation System (ITS) provides great support to travel decisions and system management but inevitably encounters the issue of data missing in monitoring systems. Hence, network-wide traffic state prediction and imputation is critical to recognizing the system level state of a transportation network. Abundant research works have adopted various approaches for traffic prediction and imputation. However, previous methods ignore the reliability analysis of the predicted/imputed traffic information. Thus, this study originally proposes an attentive graph neural process (AGNP) method for network-level short-term traffic speed prediction and imputation, simultaneously considering reliability. Firstly, the Gaussian process (GP) is used to model the observed traffic speed state. Such a stochastic process is further learned by the proposed AGNP method, which is utilized for inferring the congestion state on the remaining unobserved road segments. Data from a transportation network in Anhui Province, China, is used to conduct three experiments with increasing missing data ratio for model testing. Based on comparisons against other machine learning models, the results show that the proposed AGNP model can impute traffic networks and predict traffic speed with high-level performance. With the probabilistic confidence provided by the AGNP, reliability analysis is conducted both numerically and visually to show that the predicted distributions are beneficial to guide traffic control strategies and travel plans.

AGNP:全网短期概率流量速度预测与估算
数据驱动的智能交通系统(ITS)为出行决策和系统管理提供了强大的支持,但不可避免地会遇到监控系统中数据丢失的问题。因此,全网交通状态预测和插补对于识别交通网络的系统级状态至关重要。大量的研究工作采用了各种方法进行交通预测和插补。然而,以前的方法忽略了预测/估算交通信息的可靠性分析。因此,本研究最初提出了一种关注图神经过程(AGNP)方法,用于网络级的短期交通速度预测和插补,同时考虑可靠性。首先,使用高斯过程(GP)对观测到的交通速度状态进行建模。通过所提出的AGNP方法进一步学习了这种随机过程,该方法用于推断剩余未观测路段的拥堵状态。使用来自中国安徽省交通网络的数据,进行了三个增加缺失数据率的实验,用于模型测试。通过与其他机器学习模型的比较,结果表明,所提出的AGNP模型能够以较高的性能估算交通网络并预测交通速度。利用AGNP提供的概率置信度,对可靠性进行了数值和可视化分析,表明预测的分布有利于指导交通控制策略和出行计划。
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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CiteScore
15.20
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