Arterial travel time estimation method using SCATS traffic data based on KNN-LSSVR model

Bing, Qichun; Qu, Dayi; Chen, Xiufeng; Pan, Fuquan; Wei, Jinli

doi:10.1177/1687814019841926

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…The authors in [ 18 ] proposed SVR for freeway TTP. When compared to classical Support Vector Machine (SVM), an SVM model optimized using the artificial fish swarm approach [ 27 ] or least squared loss function and equality constraint [ 28 ] has been found to improve model precision. k-Nearest Neighbors (k-NN), an example-based or pattern matching-based model, has also been widely employed for similarity pattern matching in travel time problems on urban roads and highways [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

An attention-based recurrent learning model for short-term travel time prediction

2022

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With the advent of Big Data technology and the Internet of Things, Intelligent Transportation Systems (ITS) have become inevitable for future transportation networks. Travel time prediction (TTP) is an essential part of ITS and plays a pivotal role in congestion avoidance and route planning. The novel data sources such as smartphones and in-vehicle navigation applications allow traffic conditions in smart cities to be analyzed and forecast more reliably than ever. Such a massive amount of geospatial data provides a rich source of information for TTP. Gated Recurrent Unit (GRU) has been successfully applied to traffic prediction problems due to its ability to handle long-term traffic sequences. However, the existing GRU does not consider the relationship between various historical travel time positions in the sequences for traffic prediction. We propose an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical travel time sequences and update the weights of hidden states accordingly. We evaluated the proposed model using FCD data from Beijing. To demonstrate the generalization of our proposed model, we performed a robustness analysis by adding noise obeying Gaussian distribution. The experimental results on test data indicated that our proposed model performed better than the existing deep learning time-series models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).

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Section: Related Workmentioning

confidence: 99%

An attention-based recurrent learning model for short-term travel time prediction

2022

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“…Most of the recent predictions of travel time have adopted machine learning methods [13,14] including k-nearest neighbor (KNN) algorithms [15,16], support vector machines [17], and neural networks [18,19] model. Compared to earlier statistical prediction methods, machine learning models do not assume any specific model structure for the data, but treat it as unknown, which can handle complex problems and large amounts of data well.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection and Model Fusion Approach for Predicting Urban Macro Travel Time

et al. 2020

Mathematical Problems in Engineering

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With the rapid growth of car ownership, traffic congestion has become one of the most serious social problems. For us, accurate real-time travel time predictions are especially important for easing traffic congestion, enabling traffic control and management, and traffic guidance. In this paper, we propose a method to predict urban road travel time by combining XGBoost and LightGBM machine learning models. In order to obtain a relatively complete data set, we mine the GPS data of Beijing and combine them with the weather feature to consider the obtained 14 features as candidate features. By processing and analyzing the data set, we discussed in detail the correlation between each feature and the travel time and the importance of each feature in the model prediction results. Finally, the 10 important features screened by the LightGBM and XGBoost models were used as key features. We use the full feature set and the key feature set as input to the model to explore the effect of different feature combinations on the prediction accuracy of the model and then compare the prediction results of the proposed fusion model with a single model. The results show that the proposed fusion model has great advantages to urban travel time prediction.

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“…First, it is necessary to set out some basic definitions and principles in networks. A road network is a set of streets and avenues that intersect at intersections, which can occur at level or unevenness [23,24]. The concepts of road, trajectory and circuit that are recorded in an urban network are well known.…”

Section: Related Workmentioning

confidence: 99%

The effects of travel containment measures within COVID-19

Fernando

Wang

Zhang

et al. 2020

2020 24th International Conference Information Visualisation (IV)

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The outbreak of COVID-19 has brought the world to a standstill. More than half of the world's population was in lockdown at the beginning of April. People were urged not to go out and avoid public transport. A few figures to take stock of the situation: around 4 billion people subject to various movement restrictions worldwide, air traffic is reduced by about 90% and shared mobility platforms activity reduced to 2% to 5% of their regular business. The current crisis, which is impacting both the supply and demand for transport, appears to be a privileged moment to rethink mobility and place it on a more sustainable trajectory. The Covid-19 crisis has a significant repercussion on demand and supply of mobility in Victoria as the Victorian government has put citizens in lockdown to limit the spread of COVID-19. This research aims to investigate the travel containment measures (government restrictions) taken in response to the COVID-19 emergency have on Victorian's mobility as this insight is vital to estimate parameters appropriate for the different stages of the crisis, before and after the implementation of prevention policies.

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Arterial travel time estimation method using SCATS traffic data based on KNN-LSSVR model

Cited by 7 publications

References 19 publications

An attention-based recurrent learning model for short-term travel time prediction

An attention-based recurrent learning model for short-term travel time prediction

Feature Selection and Model Fusion Approach for Predicting Urban Macro Travel Time

The effects of travel containment measures within COVID-19

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