Short-term travel-time prediction on highway: A review on model-based approach

Oh, Simon; Byon, Young-Ji; Jang, Kitae; Yeo, Hwasoo

doi:10.1007/s12205-017-0535-8

Cited by 48 publications

(26 citation statements)

References 35 publications

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“…Nonetheless, as the knowledge base becomes increasingly large, the time to obtain accurate predictions increases as well. Model-driven approaches can be divided into four levels: Macroscopic (e.g., TOPL [ 27 ]), mesoscopic (e.g., DynaMIT [ 28 ] and Dynasmart [ 29 ]), cellular automaton (CA) (e.g., OLSIM [ 30 ]) and microscopic methods (e.g., AIMSUM online [ 31 ]) [ 32 ]. In the past, most of the studies on travel time forecasting have focused on model-based methods.…”

Section: Related Workmentioning

confidence: 99%

Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys

Shen

2020

Sensors

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Travel time prediction is critical for advanced traveler information systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state-of-the-art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data-driven travel time prediction model for bus journeys based on open data. We explore a convolutional long short-term memory (ConvLSTM) model with a self-attention mechanism that accurately predicts the running time of each segment of the trips and the waiting time at each station. The model is more robust to capture long-range dependence in time series data as well.

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Section: Related Workmentioning

confidence: 99%

Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys

Shen

2020

Sensors

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“…Input: Historical traffic flow data set (0) Output: Short-term traffic flow forecasting result set (1) Initial state probability matrix , , , , (2) for ← 1 to do (3) Calculate forecasting result +1 and +1 (4)…”

Section: Short-term Flow Traffic Prediction Model Based On Grey Diffementioning

confidence: 99%

“…In recent years, the popularization of seamless links among heterogeneous traffic equipment brought about higher requirements on the real-time and reliability of short-term traffic flow prediction. With continuous improvement of traffic information processing, how to predict the short-term traffic flow accurately and effectively has aroused wide attention of scholars domestically and abroad [1][2][3], whereupon numerous of prominent research results have emerged. So far, relevant academic circles mainly focus on the construction and optimization of prediction models in terms of time series, linear regression, historical average model, Kalman filtering, grey theory, chaos theory, nonparametric regression, neural network, support vector machine, dynamic traffic assignment model, and so forth [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

Huang

Deng

2018

Journal of Control Science and Engineering

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Directing against the shortcoming of low accuracy in short-term traffic flow prediction caused by strong traffic flow fluctuation, a novel method for short-term traffic forecasting based on the combination of improved grey Verhulst prediction algorithm and first-order difference exponential smoothing is proposed. Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme.

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“…Considering that the current traffic states are relevant to the upstream and downstream roads, and are also similar to the same horizon of previous weekdays and weekends, various data-driven algorithms have been proposed to increase the prediction reliability and accuracy. In general, approaches can be categorized into three parts: parametric methods, non-parametric methods, and deep learning methods [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Traffic Speed Prediction: An Attention-Based Method

Duan-yang

Tang

Shen

et al. 2019

Sensors

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Short-term traffic speed prediction has become one of the most important parts of intelligent transportation systems (ITSs). In recent years, deep learning methods have demonstrated their superiority both in accuracy and efficiency. However, most of them only consider the temporal information, overlooking the spatial or some environmental factors, especially the different correlations between the target road and the surrounding roads. This paper proposes a traffic speed prediction approach based on temporal clustering and hierarchical attention (TCHA) to address the above issues. We apply temporal clustering to the target road to distinguish the traffic environment. Traffic data in each cluster have a similar distribution, which can help improve the prediction accuracy. A hierarchical attention-based mechanism is then used to extract the features at each time step. The encoder measures the importance of spatial features, and the decoder measures the temporal ones. The proposed method is evaluated over the data of a certain area in Hangzhou, and experiments have shown that this method can outperform the state of the art for traffic speed prediction.

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Short-term travel-time prediction on highway: A review on model-based approach

Cited by 48 publications

References 35 publications

Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys

Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys

A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

Traffic Speed Prediction: An Attention-Based Method

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