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Day-to-day origin-destination tuple estimation and prediction with hierarchical bayesian networks using multiple data sources

机译:使用多个数据源的分层贝叶斯网络进行的每日原始目的地元组估计和预测

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摘要

Prediction of traffic demand is essential, either for an understanding of the future traffic state or so necessary measures can be taken to alleviate congestion. Usually, an origin-destination (O-D) matrix is used to represent traffic demand between two zones in transportation planning. Vehicles are assumed to be homogeneous; the trips of each vehicle are examined separately. This traditional O-D matrix lacks a behavioral basis and trip-based model structure. Another research stream of travel activity-based research addresses individual travel behaviors. This stream addresses the trip chain for travelers, but the research scope is attributes of trips, which ignores the road network. The concept of the O-D tuple, a sequence of dependent O-D pairs, is proposed for linking these two fields and for predicting traffic demand better. Through advanced monitoring systems that identify and track vehicles in the road network, the additional uncertainties of O-D tuples can be mitigated and thus reduce the underspecification more specifically. The hierarchical Bayesian networks mechanism in Gaussian space with multiprocesses is proposed for gaining the posterior of uncertain parameters. The model includes level and trend components for predicting future traffic volumes. A case study demonstrates that the proposed method can predict demand, and the path flow from cameras can reduce uncertainty in the estimation and prediction process, especially for O-D tuples.
机译:交通需求的预测对于了解未来的交通状况至关重要,因此可以采取必要的措施来缓解交通拥堵。通常,在运输规划中,使用起点(O-D)矩阵表示两个区域之间的交通需求。假设车辆是同质的;每辆车的行程要分别检查。这种传统的O-D矩阵缺乏行为基础和基于行程的模型结构。基于旅行活动的研究的另一种研究流针对个人旅行行为。该信息流为旅行者解决了旅行链,但研究范围是旅行的属性,而忽略了路网。提出了O-D元组的概念,即一系列相关的O-D对,以链接这两个字段并更好地预测流量需求。通过识别和跟踪道路网络中车辆的高级监视系统,可以减轻O-D元组的附加不确定性,从而更具体地减少规格不足。为了获得不确定参数的后验,提出了具有多个过程的高斯空间中的分层贝叶斯网络机制。该模型包括用于预测未来流量的级别和趋势组件。案例研究表明,该方法可以预测需求,并且摄像机的路径流可以减少估计和预测过程中的不确定性,尤其是对于O-D元组。

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