Short-term traffic forecasting: Where we are and where we’re going

Vlahogianni, Eleni I.; Karlaftis, Matthew G.; Golias, John

doi:10.1016/j.trc.2014.01.005

Cited by 947 publications

(563 citation statements)

References 164 publications

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“…Grey system modeling finds the internal regularity of a given data series by accumulating operations. DGM (1,1), the discrete grey model with a first-order differential equation and one variable, has been shown to be equivalent to the GM(1,1) model under given conditions and to be simpler to use [37]. Xie and Liu discussed in detail the basic principles of DGM(1,1) [38], which has been widely used recently [32,[39][40][41].…”

Section: Fundamental Theoriesmentioning

confidence: 99%

“…Grey model with a first-order differential equation and one variable DGM (1,1) Discrete grey model with a first-order differential equation and one variable RDGM (1,1) Rolling DGM(1,1) model RSDGM (1,1) Rolling seasonal DGM(1,1) model…”

Section: Abbreviationsmentioning

confidence: 99%

“…To address the complex characteristics of traffic flow, many theories are used for traffic flow prediction, from classical mathematical physics models to support vector machines (SVMs) and other evolutionary algorithms. New methods and techniques for improving the prediction accuracy are continuously presented [1,2]. Currently, many methods such as the regression method [3,4], time-series analysis [5,6], Kalman filter [7], grey model [8,9], spectral analysis [10,11], chaos theory [12], time-space model [13,14], neural network [15] and SVM [16,17] are widely used in traffic flow prediction.…”

Section: Introductionmentioning

confidence: 99%

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Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

Entropy

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This paper studies the grey coupled prediction problem of traffic data with panel data characteristics. Traffic flow data collected continuously at the same site typically has panel data characteristics. The longitudinal data (daily flow) is time-series data, which show an obvious intra-day trend and can be predicted using the autoregressive integrated moving average (ARIMA) model. The cross-sectional data is composed of observations at the same time intervals on different days and shows weekly seasonality and limited data characteristics; this data can be predicted using the rolling seasonal grey model (RSDGM (1,1)). The length of the rolling sequence is determined using matrix perturbation analysis. Then, a coupled model is established based on the ARIMA and RSDGM(1,1) models; the coupled prediction is achieved at the intersection of the time-series data and cross-sectional data, and the weights are determined using grey relational analysis. Finally, numerical experiments on 16 groups of cross-sectional data show that the RSDGM(1,1) model has good adaptability and stability and can effectively predict changes in traffic flow. The performance of the coupled model is also better than that of the benchmark model, the coupled model with equal weights and the Bayesian combination model.

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Section: Fundamental Theoriesmentioning

confidence: 99%

Section: Abbreviationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

Entropy

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“…Alternative sensor technology has become a real option for traffic parameter estimation due to IT developments of the last few years (Vlahogianni et al, 2014). Nowadays, numerous historic or real-time databases exist that have relevant information for road traffic parameter estimation although they have not been directly generated for traffic estimation purposes.…”

Section: Alternative Road Traffic Measurement Technologiesmentioning

confidence: 99%

Road Traffic Measurement and Related Data Fusion Methodology for Traffic Estimation

Tettamanti

Horváth

Varga

2014

Transport and Telecommunication Journal

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The knowledge of road traffic parameters is of crucial importance to ensure state-of-the-art traffic services either in public or private transport. In our days, a plethora of road traffic data are continuously collected producing historical and real-time traffic information as well. The available information, however, arrive from inhomogeneous sensor systems. Therefore, a data fusion methodology is proposed based on Switching Kalman Filter. The concept enables efficient travel time estimation for urban road traffic network. On the other hand, the method may contribute to a better macroscopic traffic modelling.

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“…Due to the complexity of such systems, however, short term travel time estimation and prediction have been in the spotlight for a few decades; see, for example, Vlahogianni et al (2014) and references therein. We hereby categorise the available techniques according to (1) the regression technique applied (methodology ), (2) the type of data (urban or rural), and (3) the source of data collection.…”

Section: Introductionmentioning

confidence: 99%

Network traffic flow optimization under performance constraints

Csikós

Charalambous

Farhadi

et al. 2017

Transportation Research Part C: Emerging Technologies

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In this paper, a model-based perimeter control policy for large-scale urban vehicular networks is proposed. Assuming a homogeneously loaded vehicle network and the existence of a well-posed Network Fundamental Diagram (NFD), we describe a protected network throughout its aggregated dynamics including nonlinear exit ow characteristics. Within this framework of constrained optimal boundary ow gating, two main performance metrics are considered: (a) rst, connected to the NFD, the concept of average network travel time and delay as a performance metric is de ned;(b) second, at boundaries, we take into account additional external network queue dynamics governed by uncontrolled in ow demands. External queue capacities in terms of nite-link lengths are used as the second performance metric. Hence, the corresponding performance requirement is an upper bound of external queues. While external queues represent vehicles waiting to enter the protected network, internal queue describes the protected network's aggregated behaviour.By controlling the number of vehicles joining the internal queue from the external ones, herewith a network tra c ow maximization solution subject to the internal and external dynamics and their performance constraints is developed. The originally non-convex optimization problem is transformed to a numerically e ciently convex one by relaxing the performance constraints into time-dependent state boundaries. The control solution can be 1 interpreted as a mechanism which transforms the unknown arrival process governing the number of vehicles entering the network to a regulated process, such that prescribed performance requirements on travel time in the network and upper bound on the external queue are satis ed. Comparative numerical simulation studies on a microscopic tra c simulator are carried out to show the bene ts of the proposed method.

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Short-term traffic forecasting: Where we are and where we’re going

Cited by 947 publications

References 164 publications

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Road Traffic Measurement and Related Data Fusion Methodology for Traffic Estimation

Network traffic flow optimization under performance constraints

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