Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Potential Assessment of an Ellipsoidal Neural Fuzzy Time Series Model for Freeway Traffic Prediction
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Multivariate short-term traffic flow forecasting using time-series analysis
IEEE Transactions on Intelligent Transportation Systems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Continuous ant colony optimization in a SVR urban traffic forecasting model
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Multiscale wavelet support vector regression for traffic flow prediction
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Collective traffic forecasting
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO
Applied Stochastic Models in Business and Industry
A visual analytics framework for spatio-temporal analysis and modelling
Data Mining and Knowledge Discovery
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This paper discusses the application of space-time autoregressive integrated moving average (STARIMA) methodology for representing traffic flow patterns. Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Important spatial characteristics of the space-time process are incorporated in the STARIMA model through the use of weighting matrices estimated on the basis of the distances among the various locations where data are collected. These matrices distinguish the space-time approach from the vector autoregressive moving average (VARMA) methodology and enable the model builders to control the number of the parameters that have to be estimated. The proposed models can be used for short-term forecasting of space-time stationary traffic-flow processes and for assessing the impact of traffic-flow changes on other parts of the network. The three-stage iterative space-time model building procedure is illustrated using 7.5min average traffic flow data for a set of 25 loop-detectors located at roads that direct to the centre of the city of Athens, Greece. Data for two months with different traffic-flow characteristics are modelled in order to determine the stability of the parameter estimation.