Characterizing regimes in daily cycles of urban traffic using smooth-transition regressions

Kamarianakis, Yiannis; Gao, H. Oliver; Prastacos, Poulicos

doi:10.1016/j.trc.2009.11.001

Cited by 62 publications

(45 citation statements)

References 43 publications

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“…Because the approximate daily-periodic trend of original passenger flow data may influence the prediction, a lot of researchers [35,74,75,19,76] have proposed various methods to remove this trend in order to improve the forecasting results, and make the prediction on the residual time series. In this subsection, the residual time series is used to predict the transfer passenger flow in Xidan station and the entrance passenger flow in Zhangzhizhong station.…”

Section: Experimental Results With Residual Time Seriesmentioning

confidence: 99%

A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system

2015

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Section: Experimental Results With Residual Time Seriesmentioning

confidence: 99%

A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system

2015

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“…Mentioning differential calculation, the most popular algorithms are ARIMA-like algorithms. Nevertheless, they can handle only single variable and the variable needs to be stationary [21,22]. Moreover, their related vectorised versions require more assumptions which are not practical [23].…”

Section: Introductionmentioning

confidence: 99%

Correcting and complementing freeway traffic accident data using Mahalanobis distance based outlier detection

et al. 2017

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Subject reviewA huge amount of traffic data is archived which can be used in data mining especially supervised learning. However, it is not being fully used due to lack of accurate accident information (labels). In this study, we improve a Mahalanobis distance based algorithm to be able to handle differential data to estimate flow fluctuations and detect accidents and use it to support correcting and complementing accident information. The outlier detection algorithm provides accurate suggestions for accident occurring time, duration and direction. We also develop a system with interactive user interface to realize this procedure. There are three contributions for data handling. Firstly, we propose to use multi-metric traffic data instead of single metric for traffic outlier detection. Secondly, we present a practical method to organise traffic data and to evaluate the organisation for Mahalanobis distance. Thirdly, we describe a general method to modify Mahalanobis distance algorithms to be updatable. Keywords: accident data; data labelling; differential distance; Mahalanobis distance; outlier detection; traffic data; updatable algorithm Ispravljanje i nadopunjavanje podataja o prometnim nesrećama na autocesti putem Mahalanobis udaljenosti na temelju otkrivanja netipičnih vrijednostiPregledni članak Arhivirana je ogromna količina podataka o prometu koji bi se mogli koristiti za dobivanje specifičnih podataka. Međutim, oni se u potpunosti ne koriste zbog nepostojanja točnih podataka o prometu (oznaka). U ovom radu poboljšavamo algoritam zasnovan na Mahalanobis udaljenosti za procjenu promjena toka prometa i otkrivanje nesreća i primjenjujemo ga kod ispravljanja i dopunjavanja informacija o nesreći. Algoritam za otkrivanje outliera (netipičnih vrijednosti) pruža točne podatke o vremenu događanja nesreće, trajanju i smjeru. Razvijamo i sustav s interaktivnim sučeljem korisnika u svrhu ostvarenja ovog postupka. Predlažu se tri načina za manipulaciju podacima. Najprije, za otkrivanje outliera u prometu predlažemo uporabu multi-metričkih podataka o prometu umjesto jedno metričkih. Nadalje, predlažemo praktičnu metodu za organizaciju prometnih podataka i evaluaciju organizacije Mahalanobis udaljenosti. Kao treće, dajemo opis opće metode za modifikaciju algoritama Mahalanobis udaljenosti kako bi se mogli ažurirati.

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“…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%

“…The traffic flow series observed from the same site over several consecutive days shows an intra-day trend following an M-shaped curve. Kamarianakis et al [3] used smooth-transition regressions to characterize the daily cycles of urban traffic flow. Chen et al [19] analyzed the retrieval of intra-day trends for traffic flow series to address missing data and to improve traffic predictions.…”

Section: Introductionmentioning

confidence: 99%

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

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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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Characterizing regimes in daily cycles of urban traffic using smooth-transition regressions

Cited by 62 publications

References 43 publications

A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system

A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system

Correcting and complementing freeway traffic accident data using Mahalanobis distance based outlier detection

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

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