Prediction of Bus Travel Time Using Random Forests Based on Near Neighbors

Yu, Bin; Wang, Huaizhu; Shan, Weiwei; Yao, Baozhen

doi:10.1111/mice.12315

Cited by 132 publications

(62 citation statements)

References 43 publications

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“…Moreover, the machine learning technique is playing a crucial role in driving behavior recognition. A growing amount of studies on machine learning algorithms have been conducted in recent years [4][5][6][7]. This paper builds a driving style recognition model based on vehicle trajectory data.…”

Section: Introductionmentioning

confidence: 99%

Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data

Xue

Wang

et al. 2019

Journal of Advanced Transportation

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Rear-end collision crash is one of the most common accidents on the road. Accurate driving style recognition considering rear-end collision risk is crucial to design useful driver assistance systems and vehicle control systems. The purpose of this study is to develop a driving style recognition method based on vehicle trajectory data extracted from the surveillance video. First, three rear-end collision surrogates, Inversed Time to Collision (ITTC), Time-Headway (THW), and Modified Margin to Collision (MMTC), are selected to evaluate the collision risk level of vehicle trajectory for each driver. The driving style of each driver in training data is labelled based on their collision risk level using K-mean algorithm. Then, the driving style recognition model’s inputs are extracted from vehicle trajectory features, including acceleration, relative speed, and relative distance, using Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and statistical method to facilitate the driving style recognition. Finally, Supporting Vector Machine (SVM) is applied to recognize driving style based on the labelled data. The performance of Random Forest (RF), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) is also compared with SVM. The results show that SVM overperforms others with 91.7% accuracy with DWT feature extraction method.

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Section: Introductionmentioning

confidence: 99%

Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data

Xue

Wang

et al. 2019

Journal of Advanced Transportation

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“…ML and data science has shown great potential for predicting, designing, and discovering materials (Ley & Bordas, ). In civil engineering and construction, ML has been extensively used in a variety of applications such as structural heal monitoring (Gao & Mosalam, ; Rafiei & Adeli, , ; Xue & Li, ), reliability analysis (Dai & Cao, ; Grande, Castillo, Mora, & Lo, ; Nabian & Meidani, ), transportation (Dharia & Adeli, ; García‐Ródenas, López‐García, & Sánchez‐Rico, ; Yu, Wang, Shan, & Yao, ; Zhang & Ge, ), and prediction and estimation (Adeli & Wu, ; Chou & Pham, ; Rafiei, Khushefati, Demirboga, & Adeli, ; Zhao & Ren, ). In concrete‐related studies, DeRousseau, Kasprzyk, and Srubar () recently reviewed the application of ML to optimize mixture design of concrete.…”

Section: Introductionmentioning

confidence: 99%

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Nguyen

Kashani

Ngo

et al. 2018

Computer aided Civil Eng

175

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The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high‐order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross‐entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C‐ANN) and second‐order artificial neural network (SO‐ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high‐performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C‐ANN and SO‐ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water‐to‐cement and sand‐to‐cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.

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“…Bus travel time prediction is necessary for dynamic scheduling (Yu, Wang, Shan, & Yao, ). The objectives of the combined control strategy used in this study are to balance bus headways and to avoid bus bunching.…”

Section: Prediction Model Of Bus Operational Statesmentioning

confidence: 99%

Dynamic headway control for high‐frequency bus line based on speed guidance and intersection signal adjustment

Bie

Xiong

Yan

et al. 2019

Computer aided Civil Eng

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To prevent bus bunching, a dynamic headway control method in the V2I (vehicle to infrastructure) environment for a high‐frequency route with bus lane is developed. Bus operating speed guidance on the mid‐blocks and intersection signal adjustment are two main strategies in the proposed method. A forecasting model of bus travel time under the dynamic control method is developed. The objective function is set up by taking into account differences between actual bus headways and dispatching headways, and the scaling ratios of intersection cycle lengths. The optimization model is solved using genetic algorithm. The proposed method is applied to a real bus route in Meihekou city, China, and compared with the current control plan as well as holding strategy. Results show that the proposed method can reduce bus headway deviations in all investigating periods; negative impacts on cars can be limited by setting reasonable values for the parameters.

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Prediction of Bus Travel Time Using Random Forests Based on Near Neighbors

Cited by 132 publications

References 43 publications

Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data

Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Dynamic headway control for high‐frequency bus line based on speed guidance and intersection signal adjustment

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