On Evaluating Floating Car Data Quality for Knowledge Discovery

Cerqueira, Vítor; Moreira-Matias, Luís; Khiari, Jihed; Lint, Hans van

doi:10.1109/tits.2018.2867834

Cited by 12 publications

(4 citation statements)

References 41 publications

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“…Also, this evaluation includes sample size, rate, spatial coverage and existence of additional data type (i.e., weather). In the research study of Vitor et al [36] Missing Data , Where RFC is a complementary Gaussian error function, P is a number of packets lost, and G is granularity.  Reliability: The reliability covers the dataset objectivity, and it is computed as; Reliability , Where: (at)awake trace ratio, (aT)awake trip ratio, (rt)reachable trace ratio, and (rT)reachable trip ratio.…”

Section: Artifacts Repairing Methodsmentioning

confidence: 99%

“…i) statistical-based approach, ii) logical approach, iii) outlier-detection approach, and iv) trajectory-based approach. The statistical-based approach is developed emphasizing time-series, prediction, trip detection, quantitative patterns, machine learning [39][40][41][42][43][44][45][46][47][48]. The existing logical-based approaches are reported to consider velocity constraints, reduction of travel distance, and human navigational system [49][50][51].…”

Section: Existing Research Trendsmentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

Prabha

Kabadi²

2020

IJECE

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Reliability in the utilization of the Global Positioning System (GPS) data demands a higher degree of accuracy with respect to time and positional information required by the user. However, various extrinsic and intrinsic parameters disrupt the data transmission phenomenon from GPS satellite to GPS receiver which always questions the trustworthiness of such data. Therefore, this manuscript offers a comprehensive insight into the data preprocessing methodologies evolved and adopted by present-day researchers. The discussion is carried out with respect to standard methods of data cleaning as well as diversified existing research-based approaches. The review finds that irrespective of a good number of work carried out to address the problem of data cleaning, there are critical loopholes in almost all the existing studies. The paper extracts open end research problems as well as it also offers an evidential insight using use-cases where it is found that still there is a critical need to investigate data cleaning methods.

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Section: Artifacts Repairing Methodsmentioning

confidence: 99%

Section: Existing Research Trendsmentioning

confidence: 99%

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

Prabha

Kabadi²

2020

IJECE

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“…However, the study does not compare the accuracy of FCDbased predictions with that obtained using data from fixed traffic detectors, and it is then not possible to estimate the amount of FCD needed to outperform predictions with fixed traffic detectors. The study in [27] analyzes the effect of different quality indicators of the FCD on different tasks, including the prediction of the travel time. However, the analysis does not cover the impact of the penetration rate of FCD devices on the traffic prediction.…”

Section: Related Workmentioning

confidence: 99%

On the Impact of Floating Car Data and Data Fusion on the Prediction of the Traffic Density, Flow and Speed Using an Error Recurrent Convolutional Neural Network

Mena-Oreja

Gozálvez

2021

IEEE Access

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Traffic prediction helps mitigate the impact of traffic congestion. The accuracy of traffic predictions depends on the availability of the data used for the prediction as well as the prediction model. Data from fixed traffic detectors is only available at certain locations. On the other hand, connected vehicles can provide Floating Car Data (FCD) at any location and time. However, FCD may not be available at all vehicles, and this can impact predictions since the FCD may not reflect the state of all traffic. This impact is larger when predicting traffic density or flow, and existing studies generally use FCD to predict traffic speed or travel time only. This study proposes a traffic prediction model that can accurately predict the three fundamental traffic variables (traffic density, flow, and speed) using FCD and an error recurrent convolutional neural network that takes as input the three variables. These are estimated using FCD and data from induction loops. These estimates depend on the penetration rate of FCD, so we propose a method to locally and dynamically estimate this penetration rate. This method improves the estimation of the traffic variables, and hence their prediction. The proposed model is used to analyze the impact of the FCD penetration rate on the prediction of the traffic variables. We show how our proposal reduces the amount of FCD needed to improve the prediction obtained with data from traffic detectors. We show that our proposal only requires FCD from 4% of the vehicles to improve the prediction accuracy achieved with traffic detectors. Augmenting this percentage increases the accuracy of our model for the three traffic variables. However, we also show that our prediction model reduces the FCD sample size (or FCD penetration rate) needed to achieve prediction accuracy levels close to that obtained if all vehicles provided FCD.

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“…Unfortunately this method requires several steps as well as non-trivial parameters. There are many other approaches that can be applied to detect this type of error, like those based on statistical [ 15 ], logical [ 16 ], data outliers [ 17 ] and clustering methods [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile

Peralta

Soria

Nicolis

et al. 2023

Sensors

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In the last decade, a large amount of data from vehicle location sensors has been generated due to the massification of GPS systems to track them. This is because these sensors usually include multiple variables such as position, speed, angular position of the vehicle, etc., and, furthermore, they are also usually recorded in very short time intervals. On the other hand, routes are often generated so that they do not correspond to reality, due to artifacts such as buildings, bridges, or sensor failures and where, due to the large amount of data, visual analysis of human expert is unable to detect genuinely anomalous routes. The presence of such abnormalities can lead to faulty sensors being detected which may allow sensor replacement to reliably track the vehicle. However, given the reliability of the available sensors, there are very few examples of such anomalies, which can make it difficult to apply supervised learning techniques. In this work we propose the use of unsupervised deep neural network models based on stacked autoencoders to detect anomalous routes in vehicles within Santiago de Chile. The results show that the proposed model is capable of effectively detecting anomalous paths in real data considering validation given by an expert user, reaching a performance of 82.1% on average. As future work, we propose to incorporate the use of Long Short-Term Memory (LSTM) and attention-based networks in order to improve the detection of anomalous trajectories.

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On Evaluating Floating Car Data Quality for Knowledge Discovery

Cited by 12 publications

References 41 publications

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

On the Impact of Floating Car Data and Data Fusion on the Prediction of the Traffic Density, Flow and Speed Using an Error Recurrent Convolutional Neural Network

Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile

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