Development of Urban Road Network Traffic State Dynamic Estimation Method

Wang, Jiawen; Wang, Yinsong; Yun, Meiping; Yang, Xiaoguang

doi:10.1155/2015/714149

Cited by 6 publications

(3 citation statements)

References 26 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, Many traffic flow models [ 18 – 29 ] have been proposed to study complex traffic conditions. The present study employs the velocity of the traffic flow on a road segment, as in some previous studies [ 1 , 13 , 14 , 30 , 31 ]. The floating cars are part of the traffic flow; hence, it is reasonable to consider their speed as the speed of the traffic flow.…”

Section: Problem Descriptionmentioning

confidence: 99%

A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data

Peng

Chi

et al. 2015

PLoS ONE

View full text Add to dashboard Cite

Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a road segment is considered as a hidden state that can be estimated according to the conditions of road segments having similar traffic characteristics. An algorithm based on clustering and pattern mining rather than on adjacency relationships is proposed to find clusters with road segments having similar traffic characteristics. A multi-clustering strategy is adopted to achieve a trade-off between clustering accuracy and coverage. Finally, the proposed model is designed and implemented on the basis of a real-time algorithm. Results of experiments based on real FCD confirm the applicability, accuracy, and efficiency of the model. In addition, the results indicate that the model is practicable for traffic estimation on urban arterials and works well even when more than 70% of the probe data are missing.

show abstract

Section: Problem Descriptionmentioning

confidence: 99%

A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data

Peng

Chi

et al. 2015

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In order to improve the quality of the raw data to achieve more accurate prediction results, according to the missing characteristics, the original data are divided into accidental missing and multiple missing. Referring to related literature, it is found that the naive Bayesian method and dynamic time warping method can be used to repair these two types of missing data, respectively, with good performance [36][37][38]. Consequently, we choose the naive Bayesian method and dynamic time warping method to estimate the two types of missing data separately, obtaining a complete dataset without abnormal points, which lays a solid foundation for the subsequent prediction of short-term traffic speed.…”

Section: Data Quality Improvementmentioning

confidence: 99%

A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting

Huang

Pian

et al. 2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. However, the urban traffic speed has strong temporal, spatial correlation and the characteristic of complex nonlinearity and randomness, which makes it challenging to accurately and efficiently forecast short-term traffic speeds. We investigate the relevant literature and found that although most methods can achieve good prediction performance with the complete sample data, when there is a certain missing rate in the database, it is difficult to maintain accuracy with these methods. Recent studies have shown that deep learning methods, especially long short-term memory (LSTM) models, have good results in short-term traffic flow prediction. Furthermore, the attention mechanism can properly assign weights to distinguish the importance of traffic time sequences, thereby further improving the computational efficiency of the prediction model. Therefore, we propose a framework for short-term traffic speed prediction, including data preprocessing module and short-term traffic prediction module. In the data preprocessing module, the missing traffic data are repaired to provide a complete dataset for subsequent prediction. In the prediction module, a combined deep learning method that is an attention-based LSTM (ATT-LSTM) model for predicting short-term traffic speed on urban roads is proposed. The proposed framework was applied to the urban road network in Nanshan District, Shenzhen, Guangdong Province, China, with a 30-day traffic speed dataset (floating car data) used as the experimental sample. Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. The attention mechanism can significantly reduce the error of the LSTM model (up to 12.4%) and improves the prediction performance.

show abstract

“…In real-world conditions, traffic demand significantly fluctuates over time. Traffic flow may vary greatly even at the same time of the day or the same day of the week [5,6]. If only a fixed flow is used as the timing basis, the timing scheme will be susceptible to flow fluctuation, resulting in poor control reliability [7].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Control Method for Signalized Intersections Using the Flow Fluctuation Amplitude

Hang

Zhou

Wang

2020

Discrete Dynamics in Nature and Society

Self Cite

View full text Add to dashboard Cite

Intersections are a vital component of the urban road network. Their use is essential for traffic efficiency and operational stability. In this paper, a dynamic control method for two-phase signal control intersection is proposed by using the intercycle flow standard deviation. This technique can dynamically assign weights to different control goals of traffic efficiency and operational stability. For intersections with large flow fluctuation amplitudes, greater weight is given to the control goal of operational stability to reduce the sensitivity of the signal control on flow fluctuation. For intersections with a small flow fluctuation amplitude, greater weight is given to the control goal of traffic efficiency to maximize the intersection throughput. Compared with the plan selection and pretimed control method, the numerical results can validate that this method has better control performance under different flow fluctuations amplitude. The fluctuation range length is significantly optimized, especially in the cases of the low and medium flow saturation conditions.

show abstract

Development of Urban Road Network Traffic State Dynamic Estimation Method

Cited by 6 publications

References 26 publications

A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data

A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data

A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting

A Dynamic Control Method for Signalized Intersections Using the Flow Fluctuation Amplitude

Contact Info

Product

Resources

About