Evaluating and Diagnosing Road Intersection Operation Performance Using Floating Car Data

Chen, Deqi; Yan, Xuedong; Liu, Feng; Liu, Xiaobing; Wang, Liwei; Zhang, Jiechao

doi:10.3390/s19102256

Cited by 16 publications

(10 citation statements)

References 34 publications

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“…The data used in this case study include about 110 million floating cars' trajectories, which the geographic coordinate ranges from 116.399450 to 116.431700 and the 39.947934 to 39.966344. The data are the same as those used in the previous study [9,29]. Figure 2a shows the trajectory points, and according to trajectories, we can obtain the topology of the intersection (see Figure 2b).…”

Section: Data Preprocessingmentioning

confidence: 99%

“…It is necessary to preprocess the FCD to reduce the impact of error data and improve the prediction results accuracy. A more detailed data preprocessing process has been described in previous studies [9,16,29]. This paper introduces two main steps as follows:…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Especially for the intersection, it is sometimes difficult to obtain a high-precision digital map to correspond to the more complex structures. Recently, we proposed an efficient grid-based method to visualize the signal intersections and evaluate the intersection operation state [9]. The grid-based method can discretize intersections into grids to improve the efficiency of extracting the traffic parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

et al. 2021

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Urban road intersection bottleneck has become an important factor in causing traffic delay and restricting traffic efficiency. It is essential to explore the prediction of the operating performance at intersections in real-time and formulate corresponding strategies to alleviate intersection delay. However, because of the sophisticated intersection traffic condition, it is difficult to capture the intersection traffic Spatio-temporal features by the traditional data and prediction methods. The development of big data technology and the deep learning model provides us a good chance to address this challenge. Therefore, this paper proposes a multi-task fusion deep learning (MFDL) model based on massive floating car data to effectively predict the passing time and speed at intersections over different estimation time granularity. Moreover, the grid model and the fuzzy C-means (FCM) clustering method are developed to identify the intersection area and derive a set of key Spatio-temporal traffic parameters from floating car data. In order to validate the effectiveness of the proposed model, the floating car data from ten intersections of Beijing with a sampling rate of 3s are adopted for the training and test process. The experiment result shows that the MFDL model enables us to capture the Spatio-temporal and topology feature of the traffic state efficiently. Compared with the traditional prediction method, the proposed model has the best prediction performance. The interplay between these two targeted prediction variables can significantly improve prediction accuracy and efficiency. Thereby, this method predicts the intersection operation performance in real-time and can provide valuable insights for traffic managers to improve the intersection’s operation efficiency.

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Section: Data Preprocessingmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

et al. 2021

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“…Probe vehicles resulted useful in estimating traffic flow characteristics and, thus, identifying congestion. Some researchers used FCD to evaluate traffic flow at intersections [329][330][331] and turbo-roundabouts [332]. Traffic speed can also be estimated by using FCD [333,334].…”

Section: Advanced Traffic Management System (Atms)mentioning

confidence: 99%

Sustainable Mobility: A Review of Possible Actions and Policies

Gallo

Marinelli

2020

Sustainability

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In this paper, a review of the main actions and policies that can be implemented to promote sustainable mobility is proposed. The work aims to provide a broad, albeit necessarily not exhaustive, analysis of the main studies and research that from different points of view have focused on sustainable mobility. The structure of the paper enables the reader to easily identify the topics covered and the studies related to them, so as to guide him/her to the related in-depth studies. In the first part of the paper, there is a preliminary analysis of the concept of sustainable mobility, the main transport policies implemented by the European Union and the USA, and the main statistical data useful to analyze the problem. Next, the main policies that can promote sustainable mobility are examined, classifying them into three topics: Environmental, socio-economic, and technological. Many of the policies and actions examined could be classified into more than one of the three categories used; for each of them, there is a description and the main literature work on which the topic can be analyzed in more detail. The paper concludes with a discussion on the results obtained and the prospects for research.

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“…The mobile detective data from the vehicle have a wide coverage and continuous space, and the huge daily traffic data make the prediction of the traffic state more accurate and reliable. Emerging technologies based on Global Positioning System (GPS) enable us to track vehicle trajectories and collect real-time traffic data across entire road networks [7], and have been introduced to predict the AS of road segments. Queen and Albers [8] proposed a dynamic Bayesian model to identify lagged causal relationships between time series, and predict traffic speed at multiple road link locations.…”

Section: Introductionmentioning

confidence: 99%

FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data

Zhu

Shen

Duan-yang

et al. 2019

Sensors

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The average speed (AS) of a road segment is an important factor for predicting traffic congestion, because the accuracy of AS can directly affect the implementation of traffic management. The traffic environment, spatiotemporal information, and the dynamic interaction between these two factors impact the predictive accuracy of AS in the existing literature, and floating car data comprehensively reflect the operation of urban road vehicles. In this paper, we proposed a novel road segment AS predictive model, which is based on floating car data. First, the impact of historical AS, weather, and date attributes on AS prediction has been analyzed. Then, through spatiotemporal correlations calculation based on the data from Global Positioning System (GPS), the predictive method utilizes the recursive least squares method to fuse the historical AS with other factors (such as weather, date attributes, etc.) and adopts an extended Kalman filter algorithm to accurately predict the AS of the target segment. Finally, we applied our approach on the traffic congestion prediction on four road segments in Chengdu, China. The results showed that the proposed predictive model is highly feasible and accurate.

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Evaluating and Diagnosing Road Intersection Operation Performance Using Floating Car Data

Cited by 16 publications

References 34 publications

Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

Sustainable Mobility: A Review of Possible Actions and Policies

FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data

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