Predicting Travel Times with Context-Dependent Random Forests by Modeling Local and Aggregate Traffic Flow

Hamner, Benjamin

doi:10.1109/icdmw.2010.128

Cited by 50 publications

(29 citation statements)

References 2 publications

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“…There are very limited studies discussed the RF model application in traffic prediction (Hamner, 2010;Leshem and Ritov, 2007). To the best knowledge of the authors, we did not find any studies on the application of the GBM model with freeway travel time data.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed algorithm is proved to be able to deal with missing data and is effective in predicting multiclass classification problems. Hamner (2010) applied random forest in travel time prediction and is one of the winners for the IEEE ICDM Contest: TomTom Traffic Prediction for Intelligent GPS Navigation. Their proposed method outperforms other models in terms of prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A gradient boosting method to improve travel time prediction

Zhang

Haghani

2015

Transportation Research Part C: Emerging Technologies

636

230

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A gradient boosting method to improve travel time prediction

Zhang

Haghani

2015

Transportation Research Part C: Emerging Technologies

636

230

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“…In particular, we used for benchmarking the following techniques: (non-parametric) kNN [25], RF [12], (parametric) STARIMA [3] and lag-STARIMA [6].…”

Section: Evaluation Resultsmentioning

confidence: 99%

Speed pattern recognition technique for short‐term traffic forecasting based on traffic dynamics

Kehagias

Salamanis

Tzovaras

2015

IET Intelligent Transport Systems

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This study introduces a new short-term traffic forecasting technique, based on the dynamic features of traffic data derived from vehicles moving in urban networks. The authors goal is to forecast the values of appropriate traffic status indicators such as average travel time or speed, for one or more time steps in the future until the next half hour. The proposed forecasting technique is based on road profiles generated from the application of data clustering techniques on real traffic data. Data clustering is applied after the original feature space is transformed to a new one of a significantly lower dimension. This transformation is based on the dynamic characteristics of current traffic, expressed in the form of the speed derivatives. To evaluate the proposed technique they used two-week historical data from the city of Berlin, Germany. Extensive evaluation results indicate improvement of the forecasting accuracy after comparison with a set of existing traffic forecasting techniques.

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“…This requirement limits the applicability of the regression model to the transportation areas as variables in transportation systems are highly inter-correlated [52]. However, the effect of multicollinearity can be reduced by two approaches -by calculating the value of Variance Inflation Factor (VIF), or use robust regression analysis instead of ordinary least squares regression, such as ridge regression [53], lasso regression [54], and principal component regression [55]. Statistical learning regression methods such as regression tree [56], bagging regression [57], and random forest regression [54] are also used.…”

Section: Literature Reviewmentioning

confidence: 99%

Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach

Bharathi

Kumar

Achar³

et al. 2020

Transportmetrica A: Transport Science

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Providing real-time arrival time information of the transit buses has become inevitable in urban areas to improve the efficiency of the public transportation system. However, accurate prediction of arrival time of buses is still a challenging problem in dynamically varying traffic conditions especially under heterogeneous traffic condition without lane discipline. One broad approach researchers have adopted over the years is to divide the entire bus route into sections and model the correlations of section travel times either spatially or temporally. The proposed study adopts this approach of working with section travel times and developed two predictive modelling methodologies namely (a) classical time-series approach employing a seasonal AR model with possible integrating non-stationary effects and (b) linear non-stationary AR approach, a novel technique to exploit the notion of partial correlation for learning from data to exploit the temporal correlations in the bus travel time data. Many of the reported studies did not explore the distribution of travel time data and incorporated their effects into the modelling process while implementing time series approach. The present study conducted a detailed analysis of the marginal distributions of the data from Indian conditions (that we use for testing in this paper). This revealed a predominantly log-normal behaviour which was incorporated into the above proposed predictive models. Towards a complete solution, the study also proposes a multi-section ahead travel time prediction algorithm based on the above proposed classes of temporal models learnt at each section to facilitate real time implementation. Finally, the predicted travel time values were corroborated with the actual travel time values. From the results, it was found that the proposed method was able to perform better than historical average, exponential smoothing, ARIMA, and ANN methods and the methods that considered either temporal or spatial variations alone.

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Predicting Travel Times with Context-Dependent Random Forests by Modeling Local and Aggregate Traffic Flow

Cited by 50 publications

References 2 publications

A gradient boosting method to improve travel time prediction

A gradient boosting method to improve travel time prediction

Speed pattern recognition technique for short‐term traffic forecasting based on traffic dynamics

Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach

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