An Improved Evidential Fusion Approach for Real-time Urban Link Speed Estimation

Kong, Qing; Chen, Y.; Liu, Yuncai

doi:10.1109/itsc.2007.4357757

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…The D-S evidence theory has been proven useful on GNSS trajectory data [32,[39][40][41][42]. For example, Zhao et al [32] improved a map-matching algorithm of GNSS trajectories based on the D-S evidence theory for the high-density road network.…”

Section: Related Workmentioning

confidence: 99%

“…Talavera et al [39] proposed a method to quantify the uncertainty inherent in ship trajectories based on the D-S evidence theory from Automatic Identification System (AIS) data. Kong et al [40] added an estimating process to dynamic reliability and thus proposed an improved evidence theory to provide real-time traffic state estimation with data from loop detector and GNSS probe vehicles. Zhang et al [41] also used evidence theory for fusing multi-source data to estimate real-time traffic state.…”

Section: Related Workmentioning

confidence: 99%

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Detecting Anomalous Trajectories Using the Dempster-Shafer Evidence Theory Considering Trajectory Features from Taxi GNSS Data

2018

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In road networks, an ‘optimal’ trajectory is a geometrically optimal drive from the source point to the destination point. In reality, the driver’s driving experience or road traffic conditions will lead to differences between the ‘actual’ trajectory and the ‘optimal’ trajectory. When the differences are excessive, these trajectories are considered as anomalous trajectories. In addition, these differences can be observed in various trajectory features, such as velocity, distance, turns, and intersections. In this paper, our aim is to fuse these trajectory features and to quantitatively describe this difference to infer anomalous trajectories. The Dempster-Shafer (D-S) evidence theory is a theory and method that uses different features as evidence to infer uncertainty. The theory does not require prior knowledge or conditional probabilities. Therefore, we propose an automatic, anomalous trajectory inference method based on the D-S evidence theory that considers driving behavior and road network constraints. To achieve this objective, we first obtain all of the ‘actual’ trajectories of drivers for different source-destination pairs in taxi Global Navigation Satellite System (GNSS) trajectories. Second, we define and extract five trajectory features: route selection ( R S ), intersection rate ( I R ), heading change rate ( HCR ) , slow point rate ( SPR ), and velocity change rate ( VCR ) . Then, different features of each trajectory are combined as evidence according to Dempster’s combinational rule. The precise probability interval of each trajectory is calculated based on the D-S evidence theory. Finally, we obtain the anomalous possibility of all real trajectories and infer anomalous trajectories whose trajectory features are significantly different from normal ones. The experimental results show that the proposed method can infer anomalous trajectories effectively and that it can be used to monitor driver behavior automatically and to discover adverse urban traffic events.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Detecting Anomalous Trajectories Using the Dempster-Shafer Evidence Theory Considering Trajectory Features from Taxi GNSS Data

2018

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“…For instance, El Faouzi et al investigated the application of DS evidence theory to address the travel time estimation, using data from loop detectors and toll collection stations [18]. Based on evidence theory, Kong et al added an estimating process to dynamic reliability and thus proposed an improved evidence theory to provide real-time traffic state estimation with data from loop detector and taxi GPS [11]. Mengying et al proposed a recursive-Bayesian inference data fusion model for signalized arterials, which is based on simulated loop detector data, probe data, and historical data [19].…”

Section: A Brief Overview Of Data Fusion Techniques Varieties Of Tramentioning

confidence: 99%

“…And it is significant to figure out what the influential factor is and how it will affect the fusion results. For fixed detectors, high failure ratio and inaccurate traffic state conversion arithmetic are two types of principal problems [11]. In addition, the ignorance of traffic variance (i.e., shockwave) due to the limited coverage of fixed detectors is also an issue.…”

Section: Introductionmentioning

confidence: 99%

Freeway Multisensor Data Fusion Approach Integrating Data from Cellphone Probes and Fixed Sensors

Zhang

Cheng

et al. 2016

Journal of Sensors

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Freeway traffic state information from multiple sources provides sufficient support to the traffic surveillance but also brings challenges. This paper made an investigation into the fusion of a new data combination from cellular handoff probe system and microwave sensors. And a fusion method based on the neural network technique was proposed. To identify the factors influencing the accuracy of fusion results, we analyzed the sensitivity of those factors by changing the inputs of neural-network-based fusion model. The results showed that handoff link length and sample size were identified as the most influential parameters to the precision of fusion. Then, the effectiveness and capability of proposed fusion method under various traffic conditions were evaluated. And a comparative analysis between the proposed method and other fusion approaches was conducted. The results of simulation test and evaluation showed that the fusion method could complement the drawback of each collection method, improve the overall estimation accuracy, adapt to the variable traffic condition (free flow or incident state), suit the fusion of data from cellphone probes and fixed sensors, and outperform other fusion methods.

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“…In this paper, we first improve the model in [18] by adding an estimating process to the dynamic reliability of evidence [19] and then further validate the new model using real-world traffic data in four aspects: 1) accuracy; 2) conflict resistance; 3) robustness; and 4) operation speed.…”

mentioning

confidence: 99%

An Approach to Urban Traffic State Estimation by Fusing Multisource Information

Kong

Chen

et al. 2009

IEEE Trans. Intell. Transport. Syst.

135

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This paper presents an information-fusion-based approach to the estimation of urban traffic states. The approach can fuse online data from underground loop detectors and global positioning system (GPS)-equipped probe vehicles to more accurately and completely obtain traffic state estimation than using either of them alone. In this approach, three parts of the algorithms are developed for fusion computing and the data processing of loop detectors and GPS probe vehicles. First, a fusion algorithm, which integrates the federated Kalman filter and evidence theory (ET), is proposed to prepare a robust, credible, and extensible fusion platform for the fusion of multisensor data. After that, a novel algorithm based on the traffic wave theory is employed to estimate the link mean speed using single-loop detectors buried at the end of links. With the GPS data, a series of technologies are combined with the Geographic Information Systems for Transportation (GIS-T) map to compute another link mean speed. These two speeds are taken as the inputs of the proposed fusion platform. Finally, tests on the accuracy, conflict resistance, robustness, and operation speed by real-world traffic data illustrate that the proposed approach can well be used in urban traffic applications on a large scale.Index Terms-Evidence theory (ET), GIS-T map, information fusion, Kalman filter, loop detector, map matching, probe vehicle, traffic state estimation, traffic wave theory.

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An Improved Evidential Fusion Approach for Real-time Urban Link Speed Estimation

Cited by 9 publications

References 21 publications

Detecting Anomalous Trajectories Using the Dempster-Shafer Evidence Theory Considering Trajectory Features from Taxi GNSS Data

Detecting Anomalous Trajectories Using the Dempster-Shafer Evidence Theory Considering Trajectory Features from Taxi GNSS Data

Freeway Multisensor Data Fusion Approach Integrating Data from Cellphone Probes and Fixed Sensors

An Approach to Urban Traffic State Estimation by Fusing Multisource Information

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