Modelling a traffic network with missing data

Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending

“…For instance, a model uses data with 30-second resolution to predict traffic conditions 10 minutes ahead. In the literature of short-term traffic forecasting, the data resolution and forecasting horizon typically ranges from 20 seconds (Dia, 2001) to 60 minutes (Whitlock and Queen, 2000).…”

Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending

“…For instance, a model uses data with 30-second resolution to predict traffic conditions 10 minutes ahead. In the literature of short-term traffic forecasting, the data resolution and forecasting horizon typically ranges from 20 seconds (Dia, 2001) to 60 minutes (Whitlock and Queen, 2000).…”

Eliciting a Directed Acyclic Graph for a Multivariate Time Series of Vehicle Counts in a Traffic Network

“…Unfortunately due to faulty data collection equipment, no data were collected at some sites. The missing data could be estimated using Markov chain Monte Carlo techniques (see Whitlock & Queen, 2000). However, to allow evaluation of the model, only time series for which data are observed will be considered here.…”

Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks

“…Catriona and Casper in [10] presented a Linear Multiregression Dynamic Model (LMDM) which uses concept of graph for the traffic flow forecasting where the time series of flows at different sites are represented by the nodes and the structure between the flows at different sites as well as the independence is represented by the edges connecting the nodes. The idea of using graphical dynamic model approach in their work for traffic flow forecasting follows from that of [11,12], respectively, with focus on forecasting traffic flows in two separate motorway networks using UK as a case study. Based on the distinctive features of their the LMDM, their model can be used for testing real-time instances.…”

Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending

“…For instance, a model uses data with 30-second resolution to predict traffic conditions 10 minutes ahead. In the literature of short-term traffic forecasting, the data resolution and forecasting horizon typically ranges from 20 seconds (Dia, 2001) to 60 minutes (Whitlock and Queen, 2000).…”

Eliciting a Directed Acyclic Graph for a Multivariate Time Series of Vehicle Counts in a Traffic Network

“…Unfortunately due to faulty data collection equipment, no data were collected at some sites. The missing data could be estimated using Markov chain Monte Carlo techniques (see Whitlock & Queen, 2000). However, to allow evaluation of the model, only time series for which data are observed will be considered here.…”

Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks

“…Catriona and Casper in [10] presented a Linear Multiregression Dynamic Model (LMDM) which uses concept of graph for the traffic flow forecasting where the time series of flows at different sites are represented by the nodes and the structure between the flows at different sites as well as the independence is represented by the edges connecting the nodes. The idea of using graphical dynamic model approach in their work for traffic flow forecasting follows from that of [11,12], respectively, with focus on forecasting traffic flows in two separate motorway networks using UK as a case study. Based on the distinctive features of their the LMDM, their model can be used for testing real-time instances.…”

Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending

“…For instance, a model uses data with 30-second resolution to predict traffic conditions 10 minutes ahead. In the literature of short-term traffic forecasting, the data resolution and forecasting horizon typically ranges from 20 seconds (Dia, 2001) to 60 minutes (Whitlock and Queen, 2000).…”

Eliciting a Directed Acyclic Graph for a Multivariate Time Series of Vehicle Counts in a Traffic Network

“…Unfortunately due to faulty data collection equipment, no data were collected at some sites. The missing data could be estimated using Markov chain Monte Carlo techniques (see Whitlock & Queen, 2000). However, to allow evaluation of the model, only time series for which data are observed will be considered here.…”

Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks

“…Catriona and Casper in [10] presented a Linear Multiregression Dynamic Model (LMDM) which uses concept of graph for the traffic flow forecasting where the time series of flows at different sites are represented by the nodes and the structure between the flows at different sites as well as the independence is represented by the edges connecting the nodes. The idea of using graphical dynamic model approach in their work for traffic flow forecasting follows from that of [11,12], respectively, with focus on forecasting traffic flows in two separate motorway networks using UK as a case study. Based on the distinctive features of their the LMDM, their model can be used for testing real-time instances.…”