Short‐term traffic forecasting: Overview of objectives and methods

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

doi:10.1080/0144164042000195072

Cited by 506 publications

(325 citation statements)

References 41 publications

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“…Using the extended model in (14), we predicted the occupancy rates of (n = 32) stations at 12 (noon) around Berkeley, selected from the PeMS dataset. We trained both the STATIC and HYBRID models with maximum order (k = 3) to obtain the hypergraphs G static and G hybrid , the difference being that in the HYBRID case, our inferred hypergraph is based on damped periodic kernel (as described in Section VI-B) and therefore, captures the neighbourhood which is relevant at 12 (noon).…”

Section: ) Resultsmentioning

confidence: 99%

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DLOREAN: Dynamic Location-Aware Reconstruction of Multiway Networks

Johansson

Jethava

Dubhashi

2013

2013 IEEE 13th International Conference on Data Mining Workshops

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Abstract-This paper presents a method for learning timevarying higher-order interactions based on node observations, with application to short-term traffic forecasting based on traffic flow sensor measurements. We incorporate domain knowledge into the design of a new damped periodic kernel which leverages traffic flow patterns towards better structure learning. We introduce location-based regularization for learning models with desirable geographical properties (short-range or long-range interactions). We show using experiments on synthetic and real data, that our approach performs better than static methods for reconstruction of multiway interactions, as well as time-varying methods which recover only pair-wise interactions. Further, we show on real traffic data that our model is useful for short-term traffic forecasting, improving over state-of-the-art.

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Section: ) Resultsmentioning

confidence: 99%

“…time-series methods such as ARIMA [2] and seasonal ARIMA [3], state-space models [4], [5], nonparametric methods [6], neural networks [7], simulation models [8], Bayesian methods [9], [10], random forests [11], [12] etc. See [13], [7] for survey of earlier methods, and [6], [14], [15], [16] for more recent advances.…”

Section: Introductionmentioning

confidence: 99%

DLOREAN: Dynamic Location-Aware Reconstruction of Multiway Networks

Johansson

Jethava

Dubhashi

2013

2013 IEEE 13th International Conference on Data Mining Workshops

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“…The predicted data is used for congestion evaluation and precaution. The short-term forecasting has developed quickly for nearly three decades [3]. The basic models can be divided into five types: linear models, nonlinear models, artificial intelligence models, composite models and simulation forecasting models.…”

Section: Passenger Data Monitoring and Forecastingmentioning

confidence: 99%

Congestion Risk Evaluation and Precaution of Passenger Flow in Metro Stations

Lu¹,

Han²

2016

TOCIEJ

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Abstract:Given the security problems of the urban rail transportation, this paper proposes a methodology for congestion risk evaluation and precaution in order to guarantee the safety of passengers and improve the operation efficiency in metro stations. To begin with, we selected the retention rate of the platform, the waiting rate of the ticket gate and the transfer efficiency as the indexes for the station congestion precaution system and created models to calculate the threshold respectively. By reasonably combining these three indexes, a congestion warning level function was set up to evaluate the congestion risk and determine the warning level. The characteristics of the proposed model are its quick decision results and ease of handling. With the determined warning level, some strategies are suggested to relieve the pressure. Finally, a practical application in Beijing's GuoMao metro station was performed to verify the feasibility and practicability of the proposed congestion precaution model.

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“…A smartphone based measurement system for road traffic monitoring and usage-based insurance was introduced by Handel et al [16].A parking guidance and information system (PGI) is designed to help the user in finding the parking lot more easily with the help of PARC system [17].E. I. Vlahogianni et al [18] designed logical flow based on data i.e. input or output and the quality of data for selecting the best forecasting approach.…”

Section: Related Workmentioning

confidence: 99%

“…In this literature, an extensive range of different prediction models has been studied and used for the transportation network to generate the prediction for traffic flow. There are some nonlinear model used for the forecasting short-term models like neural network [12], [13]and Auto-Regressive Integrated Moving Average Models (ARIMA) [18], [19]. Kalman et al [14], [15], [20]for linear models, and simulation-based methods.…”

Section: Related Workmentioning

confidence: 99%