Prediction of Road Traffic Congestion Based on Random Forest

Liu, Yunxiang; Wu, Hao

doi:10.1109/iscid.2017.216

Cited by 107 publications

(49 citation statements)

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“…Using the performance evaluation metrics described in the preceding section, we compared the proposed model to the following baseline models. (1) Support Vector Regressor [32], (2) Extreme Gradient Boosting (xGBoost) [6], and RandomForest regressor [27]. For each of the baseline models, the identical training dataset was used to ensure fairness and objectivity in the model evaluation process.…”

Section: Baseline Modelsmentioning

confidence: 99%

A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

Petrounias²,

et al. 2020

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Short-term traffic parameter forecasting is critical to modern urban traffic management and control systems. Predictive accuracy in data-driven traffic models is reduced when exposed to non-recurring or non-routine traffic events, such as accidents, road closures, and extreme weather conditions. The analytical mining of data from social networksspecifically twittercan improve urban traffic parameter prediction by complementing traffic data with data representing events capable of disrupting regular traffic patterns reported in social media posts. This paper proposes a deep learning urban traffic prediction model that combines information extracted from tweet messages with traffic and weather information. The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom. The findings from extensive empirical analysis using real-world data demonstrate the effectiveness of the approach in improving prediction accuracy when compared to other classical/statistical and machine learning (ML) state-of-the-art models. The improvement in predictive accuracy can lead to reduced frustration for road users, cost savings for businesses, and less harm to the environment.

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Section: Baseline Modelsmentioning

confidence: 99%

A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

Petrounias²,

et al. 2020

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“…However, only received signal strength (RSS) RSS was considered for fingerprinting, a large area of wireless technologies was not applied, and many other factors were not considered (e.g., packet loss, information of channel state, queue size, throughput). Following this trend, research published by Liu and Wu [40] used the machine learning approach (random forest algorithm) to predict traffic congestion. Variables such as weather conditions, road quality, time period, and type of day were used to build the model.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

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“…Road traffic congestion in many cities around the world is very serious, especially in metropolitan cities [1]. There have been a lot of research on the prediction of urban road traffic congestion and traffic management [2][3][4][5]. Understanding the congestion patterns of an entire road network rather than a single road or several roads in an area is important.…”

Section: Introductionmentioning

confidence: 99%

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

Zhang

et al. 2020

Algorithms

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In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.

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Prediction of Road Traffic Congestion Based on Random Forest

Cited by 107 publications

References 0 publications

A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

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