Modified Bayesian data fusion model for travel time estimation considering spurious data and traffic conditions

Mil, Soknath; Piantanakulchai, Mongkut

doi:10.1016/j.asoc.2018.06.046

Cited by 29 publications

(11 citation statements)

References 26 publications

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“…For motorized traffic, the needs for accurate travel times (e.g., for traffic information and traffic management) have led to extensive literature on the analysis, estimation, and prediction of travel times in different conditions (e.g., motorways vs. arterials), based on (fusion of) different data sources and applying a variety of techniques that have been extensively reviewed in [17]. Applied methods include Geographic Information Systems or GIS (dynamic segmentation [18]) and advanced methods like artificial intelligence and neural networks [19,20], Markov chains [21], and Bayesian models [22]. Recent evolutions also include the estimation of travel time distributions [23] and distinguishing specific turning delays per intersection movement [24,25].…”

Section: Introductionmentioning

confidence: 99%

Measuring Delays for Bicycles at Signalized Intersections Using Smartphone GPS Tracking Data

Gillis

Gautama

Gheluwe

et al. 2020

IJGI

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The article describes an application of global positioning system (GPS) tracking data (floating bike data) for measuring delays for cyclists at signalized intersections. For selected intersections, we used trip data collected by smartphone tracking to calculate the average delay for cyclists by interpolation between GPS locations before and after the intersection. The outcomes were proven to be stable for different strategies in selecting the GPS locations used for calculation, although GPS locations too close to the intersection tended to lead to an underestimation of the delay. Therefore, the sample frequency of the GPS tracking data is an important parameter to ensure that suitable GPS locations are available before and after the intersection. The calculated delays are realistic values, compared to the theoretically expected values, which are often applied because of the lack of observed data. For some of the analyzed intersections, however, the calculated delays lay outside of the expected range, possibly because the statistics assumed a random arrival rate of cyclists. This condition may not be met when, for example, bicycles arrive in platoons because of an upstream intersection. This justifies that GPS-based delays can form a valuable addition to the theoretically expected values.

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Section: Introductionmentioning

confidence: 99%

Measuring Delays for Bicycles at Signalized Intersections Using Smartphone GPS Tracking Data

Gillis

Gautama

Gheluwe

et al. 2020

IJGI

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“…The Bayesian method could be a useful tool for the information fusion. Because of its strong ability at data fusion [9], it has received considerable attention in various engineering fields [10,11].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

2019

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Residual life estimation is an important problem in reliability engineering. Traditional methods, which are based on time-to-failure distribution, have limitations for components of on-orbit satellites characterized as high reliability with small sample size. Various types of reliability information can be collected during test and operation, including historical lifetime data, degradation data, similar data, expert information, etc. Therefore, making full use of multi-source information is meaningful for improving estimation precision. However, research on residual life estimation by fusing multi-source information is rare. No study has examined the overall process of fusing all of the different kinds of information. In this paper, a Bayesian method is presented to estimate the residual life of Weibull-distributed components of on-orbit satellites by fusing all the collected information. Prior distributions are determined using different kinds of information. After fusing the field data, posterior distributions can be obtained corresponding to each prior distribution. Then, the joint posterior distribution is the weighted sum of these posterior distributions with weights calculated using the second Maximum Likelihood Estimation (ML-II) method. Consistency is tested to guarantee the safety of the information fusion. Furthermore, residual life is estimated by the proposed sample-based method including both the Bayesian estimate and credible interval (CI). A Monte Carlo simulation study is conducted to demonstrate the proposed methods and shows that the Bayesian method is satisfactory and robust. Finally, a published dataset of the momentum wheel in a satellite is analyzed to illustrate the application of the method.

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“…Response to the pre-defined variable state in this study contributes to traffic management decision-making procedure. Travel time estimation requires accurate vehicle count data from loop detectors and vehicles' locations (from GPS), whether it is measured from road traffic or freeways [34], [35], [36]. Speed estimation could be generated with the availability of vehicle volume data and GPS collected from vehicles, phones, or navigator devices [38], [51].…”

Section: A Tfa Attributesmentioning

confidence: 99%

“…Content may change prior to final publication. [36] propose a framework of travel time estimation by combining Bayesian and GMM to enhance sensor accuracy data that contributes to travel time accuracy. From three case studies engaged in this study, MAPE shows the range of 3.46% to 16.3%.…”

Section: ) Bayesianmentioning

confidence: 99%

“…JPDA is mainly implemented at data level fusion and used mostly during the early stage of data processing in traffic data association [110], [149], and motions [111]. Bayesian works well in achieving estimation value in travel time [34], [36], speed [37], and traffic flow [112]. KNN is another method that performs well at data level fusion to produce travel time [35], traffic state prediction [116], and fusing incoming traffic direction [114].…”

Section: A Analysismentioning

confidence: 99%

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Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

et al. 2021

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Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.INDEX TERMS Intelligent transportation systems, traffic flow analysis, data fusion; real-time processing, multi-sensor, heterogeneous data, machine learning.

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Modified Bayesian data fusion model for travel time estimation considering spurious data and traffic conditions

Cited by 29 publications

References 26 publications

Measuring Delays for Bicycles at Signalized Intersections Using Smartphone GPS Tracking Data

Measuring Delays for Bicycles at Signalized Intersections Using Smartphone GPS Tracking Data

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

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