Modeling traffic volatility dynamics in an urban network (original) (raw)
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Kamarianakis, Kanas and Prastacos 1 MODELING TRAFFIC VOLATILITY DYNAMICS IN AN URBAN NETWORK
2016
Abstract. This article discusses the application of Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) time series models for representing the dynamics of traffic flow volatility. The methods encountered in the literature so far, focus on the levels of traffic flows while regarding variance constant through time. The approach adopted in this paper concentrates mostly on the autoregressive properties of traffic variability aiming to provide better confidence intervals for traffic flow forecasts. The model building procedure is illustrated using 7.5 min average traffic flow data for a set of eleven loop detectors located at major arterials that direct to the center of the city of Athens, Greece. A sensitivity analysis for coefficient estimates is undertaken, with respect to both time and space. Kamarianakis, Kanas and Prastacos 3
Short-Term Prediction of Urban Traffic Variability: Stochastic Volatility Modeling Approach
Journal of Transportation Engineering, 2010
This paper addresses the problem of modeling and predicting urban traffic flow variability, which involves considerable implications for the deployment of dynamic transportation management systems. Traffic variability is described in terms of a volatility metric, i.e., the conditional variance of traffic flow level, as a latent stochastic ͑low-order Markov͒ process. A discrete-time parametric stochastic model, referred to as stochastic volatility ͑SV͒ model is employed to provide short-term adaptive forecasts of traffic ͑speed͒ variability by using real-time detector measurements of volumes and occupancies in an urban arterial. The predictive performance of the SV model is compared to that of the generalized autoregressive conditional heteroscedasticity ͑GARCH͒ model, which has been recently used for the traffic variability forecasting, with regard to different measurement locations, forms of data input, lengths of forecasting horizon and performance measures. The results indicate the potential of the SV model to produce out-of-sample forecasts of speed variability with significantly higher accuracy, in comparison to the GARCH model.
Real-time traffic volatility forecasting in urban arterial networks
2006
A methodology is presented for forecasting traffic volatility in urban arterial networks with real-time traffic flow information. This methodology provides a generalization of the standard modeling approach, in which both the mean, modeled by an autoregressive moving average process, and the variance, modeled by an autoregressive conditional heteroscedastic process, are time-varying.
Journal of the Royal Statistical Society: Series C (Applied Statistics), 2013
Linear multiregression dynamic models (LMDMs), which combine a graphical representation of a multivariate time series with a state space model, have been shown to be a promising class of models for forecasting of traffic flow data. Analysis of flows at a busy motorway intersection near Manchester, UK, highlights two important modelling issues: accommodating different levels of traffic variability depending on the time of day and accommodating measurement errors occurring due to data collection errors. This paper extends LMDMs to address these issues. Additionally, the paper investigates how close the approximate forecast limits usually used with the LMDM are to the true, but not so readily available, forecast limits.
Transportation Research Record: Journal of the Transportation Research Board, 2003
Several univariate and multivariate models have been proposed for performing short-term forecasting of traffic flow. Two different univariate [historical average and ARIMA (autoregressive integrated moving average)] and two multivariate [VARMA (vector autoregressive moving average) and STARIMA (space–time ARIMA)] models are presented and discussed. A comparison of the forecasting performance of these four models is undertaken with data sets from 25 loop detectors located in major arterials in the city of Athens, Greece. The variable under study is the relative velocity, which is the traffic volume divided by the road occupancy. Although the specification of the network’s neighborhood structure for the STARIMA model was relatively simple and can be further refined, the results obtained indicate a comparable forecasting performance for the ARIMA, VARMA, and STARIMA models. The historical average model could not cope with the variability of the data sets at hand.
A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction
International Journal of Communications, Network and System Sciences, 2015
This paper highlights the statistical procedure used in developing models that have the ability of capturing and forecasting the traffic of mobile communication network operating in Vietnam. To build such models, we follow Box-Jenkins method to construct a multiplicative seasonal ARIMA model to represent the mean component using the past values of traffic, then incorporate a GARCH model to represent its volatility. The traffic is collected from EVN Telecom mobile communication network. Diagnostic tests and examination of forecast accuracy measures indicate that the multiplicative seasonal ARIMA/GARCH model, i.e. ARIMA (1, 0, 1) × (0, 1, 1)24/GARCH (1, 1) shows a good estimation when dealing with volatility clustering in the data series. This model can be considered to be a flexible model to capture well the characteristics of EVN traffic series and give reasonable forecasting results. Moreover, in such situations that the volatility is not necessary to be taken into account, i.e. short-term prediction, the multiplicative seasonal ARIMA/GARCH model still acts well with the GARCH parameters adjusted to GARCH (0, 0).
IEEE Transactions on Intelligent Transportation Systems, 2017
Subway short-term ridership forecasting plays an important role in intelligent transportation systems. However, limited efforts have been made to forecast the subway shortterm ridership, accounting for dynamic volatility. The traditional forecasting methods can only provide point values that are unable to offer enough information on the volatility/uncertainty of the forecasting results. To fill this gap, the aim of this paper is to incorporate the dynamic volatility into the subway shortterm ridership forecasting process that not only generates the expected value of the short-term ridership but also obtains the prediction interval. Four kinds of the integrated ARIMA and GARCH models are constructed to model the mean part and volatility part of the short-term ridership. The performance of the proposed method is investigated with the real subway shortterm ridership data from three stations in Beijing. The model results show that the proposed model outperforms the traditional model for all three stations. The hybrid model can significantly improving the reliability of the predicted point value by reducing the mean prediction interval length of the ridership, and improve the prediction interval coverage probability. Considering the different traffic patterns between weekday and weekend, the shortterm ridership is also modeled, respectively. This paper can help management understand the dynamic volatility of the subway short-term ridership, and have the potential to disseminate more reliable subway information to travelers through the information systems.
Forecasting traffic flows in road networks: A graphical dynamic model approach
2008
Congestion on roads is a major problem worldwide. Many roads now have induction loops implanted into the road surface providing real-time traffic flow data. These data can be used in a traffic management system to monitor current traffic flows in a network so that traffic can be directed and managed efficiently. Reliable short-term forecasting and monitoring models of traffic flows are crucial for the success of any traffic management system.
A P ] 2 5 Fe b 20 16 On short-term traffic flow forecasting and its reliability
2018
Recent advances in time series, where deterministic and stochastic modelings as well as the storage and analysis of big data are useless, permit a new approach to short-term traffic flow forecasting and to its reliability, i.e., to the traffic volatility. Several convincing computer simulations, which utilize concrete data, are presented and discussed.