Forecasting road traffic conditions using a context-based random forest algorithm

Evans, Jonny; Waterson, Ben; Hamilton, Andrew

doi:10.1080/03081060.2019.1622250

Cited by 15 publications

(10 citation statements)

References 10 publications

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“…However, the four-month training period clearly improved LSTM predictions in most cases. As noted in [ 45 ], the longer the training is, the more context the models can incorporate, in turn resulting in reduced prediction errors.…”

Section: Resultsmentioning

confidence: 99%

“…As noted in [45], the longer the training is, the more context the models can incorporate, in turn resulting in reduced prediction errors.…”

Section: Resulting Predictions With Zero Nearby Cctvsmentioning

confidence: 99%

“…Among those, the review here is focused on RF regression. RF is a supervised ML algorithm based on decision tree models where data are sub-sampled, trained and predicted multiple times under individual well-structured trees [43,45]. A particular averaging method is applied to assess the variance and accuracy per prediction and a mean prediction is finally voted on while the algorithm converges [43].…”

Section: Traffic Prediction Approachesmentioning

confidence: 99%

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Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction

Peppa

Komar

Xiao

et al. 2021

Sensors

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Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision making in local traffic bureaus using largely available IoT sensors, especially CCTV, is yet to be developed. This study presents an end-to-end urban traffic volume detection and prediction framework using CCTV image series. The framework incorporates a novel Faster R-CNN to generate vehicle counts and quantify traffic conditions. Then it investigates the performance of a statistical-based model (SARIMAX), a machine learning (random forest; RF) and a deep learning (LSTM) model to predict traffic volume 30 min in the future. Tests at six locations with varying traffic conditions under different lengths of past time series are used to train the prediction models. RF and LSTM provided the most accurate predictions, with RF being faster than LSTM. The developed framework has been successfully applied to fill data gaps under adverse weather conditions when data are missing. It can be potentially implemented in near real time at any CCTV location and integrated into an online visualization platform.

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Section: Resultsmentioning

confidence: 99%

“…As noted in [45], the longer the training is, the more context the models can incorporate, in turn resulting in reduced prediction errors.…”

Section: Resulting Predictions With Zero Nearby Cctvsmentioning

confidence: 99%

Section: Traffic Prediction Approachesmentioning

confidence: 99%

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Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction

Peppa

Komar

Xiao

et al. 2021

Sensors

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“…In urban planning, Baumeister et al 93 rank the urban forest characteristics for cultural ecosystem services supply by typical RF. Forecasting road traffic conditions in done by [ 94 ]. The simulation of urban space development by RF is presented by [ 95 ].…”

Section: Related Workmentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

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“…Forecasters do not need to have a deep understanding of urban transportation systems to achieve better forecasting accuracy. Many well-known traditional Machine Learning (ML) methods have been applied to traffic flow prediction, such as Support Vector Machines (SVM) [20], K-Nearest Neighbours (KNN) [21,22], Random Forest [23,24], etc. Machine learning methods can not only effectively capture the spatial and temporal relationships in the transportation network, but also better process high-dimensional data and catch nonlinear relationships, so that the rich traffic data can be more fully utilised by ML methods [17].…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Autoregressive Model with Exogenous Variables for Traffic Flow Forecasting in Smaller Urban Regions

Guan³

2022

PROMTT

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Data-driven forecasting methods have the problems of complex calculations, poor portability and need a large amount of training data, which limits the application of data-driven methods in small cities. This paper proposes a traffic flow forecasting method using a Nonlinear AutoRegressive model with eXogenous variables (NARX model), which uses a dynamic neural network Focused Time-Delay Neural Network (FTDNN) with a Tapped Delay Line (TDL) structure as a nonlinear function. The TDL structure enables the FTDNN to have short-term memory capabilities. At the same time, before the data is input into the FTDNN, the use of trend decomposition or differential calculation on the traffic data sequence can make the NARX model maintain long-term predictive capabilities. Compared with common nonlinear models, the FTDNN has structural advantages. It uses a simple TDL structure without the memory mechanism and the gated structure, which can reduce the parameters of the model and reduce the scale of data. Through the four-day data of Guilin City, the traffic volume forecast for five minutes is verified, and the performance of the NARX model is better than that of the SARIMA model and the Holt-Winters model.

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Forecasting road traffic conditions using a context-based random forest algorithm

Cited by 15 publications

References 10 publications

Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction

Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction

A New Random Forest Algorithm Based on Learning Automata

A Nonlinear Autoregressive Model with Exogenous Variables for Traffic Flow Forecasting in Smaller Urban Regions

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