Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network
نویسندگان
چکیده
منابع مشابه
Intervention and causality: forecasting traffic flows using a dynamic Bayesian network
Real-time traffic flow data across entire networks can be used in a traffic management system to monitor current traffic flows so that traffic can be directed and managed efficiently. Reliable short-term forecasting models of traffic flows are crucial for the success of any traffic management system. The model proposed in this paper for forecasting traffic flows is a multivariate Bayesian dynam...
متن کاملIntervention and causality in a dynamic Bayesian network
The use of intervention for time series modelling is a well established technique for on-line forecasting and decision-making in the context of Bayesian dynamic linear models. Intervention has also been recently used in (non-dynamic) Bayesian networks to investigate causal relationships between variables, and in dynamic Bayesian networks to investigate lagged causal relationships between time s...
متن کاملForecasting multivariate road traffic flows using Bayesian dynamic graphical models, splines and other traffic variables
Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simp...
متن کاملresearch publications and other research outputs Forecasting multivariate road traffic flows using Bayesian dynamic graphical models
Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simp...
متن کاملTraffic Flow Forecasting Using a Spatio-temporal Bayesian Network Predictor
A novel predictor for traffic flow forecasting, namely spatiotemporal Bayesian network predictor, is proposed. Unlike existing methods, our approach incorporates all the spatial and temporal information available in a transportation network to carry our traffic flow forecasting of the current site. The Pearson correlation coefficient is adopted to rank the input variables (traffic flows) for pr...
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2009
ISSN: 0162-1459,1537-274X
DOI: 10.1198/jasa.2009.0042