Urban Traffic Flow Prediction Using a Spatio-Temporal Random Effects Model

Wu, Yao-Jan; Chen, Feng; Lu, Chang; Yang, Shu

doi:10.1080/15472450.2015.1072050

Cited by 64 publications

(15 citation statements)

References 22 publications

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“…2 Convolutional and max-pooling operations of a convolutional neural network using a 3 3 filter (adapted from a deeplearning tutorial [47] ). State-space models, such as the spatiotemporal random effect model [57] , which use a set of inputs, outputs, and state variables to describe a system by a set of first-order differential equations.…”

Section: Deep Learning For Traffic Forecasting 221 Traditional Methodsmentioning

confidence: 99%

Geospatial data to images: A deep-learning framework for traffic forecasting

Jiang

Zhang

2019

Tinshhua Sci. Technol.

143

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Traffic forecasting has been an active research field in recent decades, and with the development of deeplearning technologies, researchers are trying to utilize deep learning to achieve tremendous improvements in traffic forecasting, as it has been seen in other research areas, such as speech recognition and image classification. In this study, we summarize recent works in which deep-learning methods were applied for geospatial data-based traffic forecasting problems. Based on the insights from previous works, we further propose a deep-learning framework, which transforms geospatial data to images, and then utilizes the state-of-the-art deep-learning methodologies such as Convolutional Neural Network (CNN) and residual networks. To demonstrate the simplicity and effectiveness of our framework, we present a formulation of the New York taxi pick-up/drop-off forecasting problem, and show that our framework significantly outperforms traditional methods, including Historical Average (HA) and AutoRegressive Integrated Moving Average (ARIMA).

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Section: Deep Learning For Traffic Forecasting 221 Traditional Methodsmentioning

confidence: 99%

Geospatial data to images: A deep-learning framework for traffic forecasting

Jiang

Zhang

2019

Tinshhua Sci. Technol.

143

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“…Finally, several recent studies discover spatiotemporal relationships on the basis of special methods or indicators, designed by the authors to apply deeper analysis of traffic similarities. Dong et al [7] constructed an indicator that simultaneously includes adjacency of spatial locationsthe shortest distance and number of intersections between them; Zhu et al [30] utilised similarity of traffic flows at different spatial locations; Cheng et al [31] weighted the similarity by the distance between links; Deng and Jiang [32] suggested empirical association rules; Pascale and Nicoli [33] utilised a mutual information indicator; Chan et al [34] applied the Taguchi method; Cai et al [35] constructed an indicator using the distance and a connective grade of spatial locations and correlations for traffic flows; Wu et al [36] suggested a custom bi-square function; Chen et al [37] applied weighted traffic flows as a similarity metric.…”

Section: Class 2: Endogenous Feature Filtering Methodsmentioning

confidence: 99%

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Pavlyuk

2019

Eur. Transp. Res. Rev.

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A spatiotemporal approach that simultaneously utilises both spatial and temporal relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate identification of the spatiotemporal structure (dependencies amongst traffic flows in space and time) plays a critical role in modern traffic forecasting methodologies, and recent developments of data-driven feature selection and extraction methods allow the identification of complex relationships. This paper systematically reviews studies that apply feature selection and extraction methods for spatiotemporal traffic forecasting. The reviewed bibliographic database includes 211 publications and covers the period from early 1984 to March 2018. A synthesis of bibliographic sources clarifies the advantages and disadvantages of different feature selection and extraction methods for learning the spatiotemporal structure and discovers trends in their applications. We conclude that there is a clear need for development of comprehensive guidelines for selecting appropriate spatiotemporal feature selection and extraction methods for urban traffic forecasting.

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“…According to Lewis' scale of interpretation of estimation accuracy [34], any forecast with a MAPE value of less than 10 % can be considered highly accurate, 11-20 % is good, 21-50 % is reasonable and 51 % or more is inaccurate. In most of the studies on flow prediction [23,24,29,30,35], a MAPE in the range of 10-20 % was reported. Since traffic flow observations vary from a few hundred vehicles per hour in off peak to several thousand vehicles during peak periods, MAPE in the range of 10-20 % is generally acceptable.…”

Section: Corroboration Of the Prediction Schemementioning

confidence: 99%

Short-term traffic flow prediction using seasonal ARIMA model with limited input data

Kumar

Vanajakshi

2015

Eur. Transp. Res. Rev.

593

207

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Background Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models reported in most studies demands sound database for model building. Hence, the applicability of these models remains a question in places where the data availability could be an issue. The present study tries to overcome the above issue by proposing a prediction scheme using Seasonal ARIMA (SARIMA) model for short term prediction of traffic flow using only limited input data. Method A 3-lane arterial roadway in Chennai, India was selected as the study stretch and limited flow data from only three consecutive days was used for the model development using SARIMA. After necessary differencing to make the input time series a stationary one, the autocorrelation function (ACF) and partial autocorrelation function (PACF) were plotted to identify the suitable order of the SARIMA model. The model parameters were found using maximum likelihood method in R. The developed model was validated by performing 24 hrs. ahead forecast and the predicted flows were compared with the actual flow values. A comparison of the proposed model with historic average and naive method was also attempted. The effect of increase in sample size of input data on prediction results was studied. Short term prediction of traffic flow during morning and evening peak periods was also attempted using both historic and real time data. Concluding remarks The mean absolute percentage error (MAPE) between actual and predicted flow was found to be in the range of 4-10, which is acceptable in most of the ITS applications. The prediction scheme proposed in this study for traffic flow prediction could be considered in situations where database is a major constraint during model development using ARIMA.

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Urban Traffic Flow Prediction Using a Spatio-Temporal Random Effects Model

Cited by 64 publications

References 22 publications

Geospatial data to images: A deep-learning framework for traffic forecasting

Geospatial data to images: A deep-learning framework for traffic forecasting

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Short-term traffic flow prediction using seasonal ARIMA model with limited input data

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