Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

El-Sayed, Hesham; Sankar, Sharmi; Daraghmi, Yousef‐Awwad; Tiwari, Prayag; Rattagan, Ekarat; Mohanty, Manoranjan; Puthal, Deepak; Prasad, Mukesh

doi:10.3390/s18061696

Cited by 20 publications

(7 citation statements)

References 19 publications

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“…Furthermore, each and every network node in VSN is assumed to store, carry, and precisely transfer the data with cooperative behavior. In recent years, following rapid diversification, navigation technologies and traffic information services enable a large amount of data to be collected from the different devices such as loop detectors, on-board equipment, speed sensors, remote microwave traffic sensors (RTMS), and road-side surveillance cameras etc., that have been proactively used for monitoring of traffic conditions in the ITS domain [5][6][7][8][9]. Sensor networks in the form of road side units (RSUs) offer numerous applications including broadcasting periodic informatory, warnings, and safety messages to road users.…”

Section: Introductionmentioning

confidence: 99%

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

Zahid

Chen

Jamal

et al. 2020

Sensors

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Short-term traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future traffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple if–then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.

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Section: Introductionmentioning

confidence: 99%

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

Zahid

Chen

Jamal

et al. 2020

Sensors

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“…El-Sayed et al studied the traffic flow characteristics under the heterogeneous vehicular networks environment and improved the support vector machine (SVM) method. The experiment results indicate that the improved-SVM forecasting accuracy is high, which is superior to other traffic flow forecasting methods [20]. Bratsas et al conducted multi-scenario experimental verification on the random forest model, support vector regression model, and multi-layer perceptron method to compare their prediction performance [21].…”

Section: Introductionmentioning

confidence: 99%

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

et al. 2020

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For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

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“…Moreover, the classification of traffic flow parameters was used for surrogate indicators of traffic status in few studies. Statistical techniques have been used to classify the status of traffic parameters and the ability to learn from traffic parameters without being explicitly programmed, which has provided powerful assistance to the classification of traffic flow parameters and has been gradually applied to the classification of traffic safety system [13,14]. Additionally, k-means and support vector machines have been used in some literature to explore the status of traffic safety.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Truck Proportion on Traffic Safety Using Surrogate Safety Measures in China

Chen

Zhang

Xing

et al. 2020

Journal of Advanced Transportation

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The purpose of this study is to investigate the impact of the truck proportion on surrogate safety measures to explore the relationship between truck proportion and traffic safety. The relationship between truck proportion and traffic flow parameters was analyzed by correlation and partial correlation analysis, and the value of the 85th percentile speed minus the 15th percentile speed (85%V–15%V) and the speed variation coefficient were selected as surrogate safety measures to explore the impact of truck proportion on traffic status. The k-means algorithm and the support vector machine were employed to evaluate traffic status on a freeway under different truck proportions in different periods. The major results are that the relationship between truck proportion and the value of 85%V–15%V and the speed variation coefficient is consistent in different aggregation periods. With increasing truck proportion, the value of 85%V–15%V, as well as the speed variation coefficient, increases initially and then decreases. In addition, the traffic flow status tends to be dangerous when the truck proportion ranges from 0.4 to 0.6 and when the value of 85%V–15%V and the speed variation coefficient are above 42 km/h and 0.223, respectively. While the truck proportion is from 0.1 to 0.3 and from 0.7 to 0.9, the traffic flow is relatively safe on the condition that the value of 85%V–15%V and the speed variation coefficient were under 42 km/h and 0.223, respectively. Therefore, the relationship between truck proportion and traffic safety could be well revealed by two surrogate safety measures, that is, the value of 85%V–15%V and the speed variation coefficient. In addition, the k-means algorithm and the support vector machine can well reveal the impact of truck proportion on traffic safety in different periods. The findings of this study indicate a need for decreasing the disturbance of mixed traffic and the impact of the truck proportion on traffic safety status.

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Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

Cited by 20 publications

References 19 publications

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

The Impact of Truck Proportion on Traffic Safety Using Surrogate Safety Measures in China

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