STARIMA-based traffic prediction with time-varying lags

Duan, Peibo; Mao, Guoqiang; Wang, Shangbo

doi:10.1109/itsc.2016.7795773

Cited by 20 publications

(6 citation statements)

References 14 publications

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“…On the other hand, time series models have also been utilized in transportation-related problems as well as to model the subway ridership. For example, time series ARIMA models have been applied to many areas of transportation including traffic arrival demand modeling, seasonal variation of freeway traffic conditions, prediction of actuated signal cycle length, traffic speed modeling on a downstream link, and so forth (12)(13)(14)(15). Also, time series models have been applied to predict transit ridership: for instance, Ding et al presented an ARIMA-generalized autoregressive conditional heteroscedasticity (GARCH) time series model to predict short-term metro ridership, which is an ARIMA model that takes care of the deterministic part and the nonlinear GARCH model for the stochastic part (16).…”

Section: Literature Reviewmentioning

confidence: 99%

Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City

Moghimi

Kamga

Safikhani

et al. 2022

Transportation Research Record: Journal of the Transportation R

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The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City (NYC), U.S. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic, since some of the modeling assumptions might be violated during this time. In this paper, utilizing change point detection procedures, a piecewise stationary time series model is proposed to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in NYC for randomly selected stations. Fitting the proposed model to these data sets enhances understanding of ridership changes during external shocks, both in relation to mean (average) changes and the temporal correlations.

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Section: Literature Reviewmentioning

confidence: 99%

Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City

Moghimi

Kamga

Safikhani

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…Traditional methods predominantly employ machine learning methods such as Autoregressive Integrated Moving Average (ARIMA) [3], Space-Time ARIMA (STARIMA) [4], Vector Autoregression (VAR) [14], Hidden Markov Models [15], and Gaussian Processes [16]. ARIMA [3] is a classic time-series forecasting method that relies on autocorrelation within historical data to predict future trends.…”

Section: Related Workmentioning

confidence: 99%

“…ARIMA [3] is a classic time-series forecasting method that relies on autocorrelation within historical data to predict future trends. STARIMA [4] extends ARIMA by incorporating the influence of neighboring areas, adapting it for spatio-temporal data. VAR [14] extends univariate regression models to multivariate time-series autoregression but requires a substantial number of parameters, leading to high computational costs.…”

Section: Related Workmentioning

confidence: 99%

“…Over the years, numerous approaches have been proposed to tackle this problem. Traditional time-series prediction methods, such as ARIMA [3] and SARIMA [4], mainly focus on analyzing temporal dimensions while neglecting spatial correlations. With the recent advancements in deep learning techniques, deep neural networks have introduced novel perspectives and methodologies for urban crowd flow prediction.…”

Section: Introductionmentioning

confidence: 99%

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ARFGCN: Adaptive Receptive Field Graph Convolutional Network for Urban Crowd Flow Prediction

Dai,

Huang,

Peng

et al. 2024

Mathematics

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Urban crowd flow prediction is an important task for transportation systems and public safety. While graph convolutional networks (GCNs) have been widely adopted for this task, existing GCN-based methods still face challenges. Firstly, they employ fixed receptive fields, failing to account for urban region heterogeneity where different functional zones interact distinctly with their surroundings. Secondly, they lack mechanisms to adaptively adjust spatial receptive fields based on temporal dynamics, which limits prediction performance. To address these limitations, we propose an Adaptive Receptive Field Graph Convolutional Network (ARFGCN) for urban crowd flow prediction. ARFGCN allows each region to independently determine its receptive field size, adaptively adjusted and learned in an end-to-end manner during training, enhancing model prediction performance. It comprises a time-aware adaptive receptive field (TARF) gating mechanism, a stacked 3DGCN, and a prediction layer. The TARF aims to leverage gating in neural networks to adapt receptive fields based on temporal dynamics, enabling the predictive network to adapt to urban regional heterogeneity. The TARF can be easily integrated into the stacked 3DGCN, enhancing the prediction. Experimental results demonstrate ARFGCN’s effectiveness compared to other methods.

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“…These approaches assume a priori structure of prediction model [22]. Typical models are autoregressive integrated moving average (ARIMA) forecast models [23,24] and other variants focused on specific seasonal conditions, e.g., [25][26][27]. Other approaches focus on statistics, including Kalman filters or Markov models.…”

Section: Introductionmentioning

confidence: 99%

Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints

Šimůnek

Smutný

2020

Applied Sciences

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Traffic speed prediction for a selected road segment from a short-term and long-term perspective is among the fundamental issues of intelligent transportation systems (ITS). During the course of the past two decades, many artefacts (e.g., models) have been designed dealing with traffic speed prediction. However, no satisfactory solution has been found for the issue of a long-term prediction for days and weeks using the vast spatial and temporal data. This article aims to introduce a long-term traffic speed prediction ensemble model using country-scale historic traffic data from 37,002 km of roads, which constitutes 66% of all roads in the Czech Republic. The designed model comprises three submodels and combines parametric and nonparametric approaches in order to acquire a good-quality prediction that can enrich available real-time traffic information. Furthermore, the model is set into a conceptual design which expects its usage for the improvement of navigation through waypoints (e.g., delivery service, goods distribution, police patrol) and the estimated arrival time. The model validation is carried out using the same network of roads, and the model predicts traffic speed in the period of 1 week. According to the performed validation of average speed prediction at a given hour, it can be stated that the designed model achieves good results, with mean absolute error of 4.67 km/h. The achieved results indicate that the designed solution can effectively predict the long-term speed information using large-scale spatial and temporal data, and that this solution is suitable for use in ITS.

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STARIMA-based traffic prediction with time-varying lags

Cited by 20 publications

References 14 publications

Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City

Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City

ARFGCN: Adaptive Receptive Field Graph Convolutional Network for Urban Crowd Flow Prediction

Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints

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