Application of Finite Mixture of Logistic Regression for Heterogeneous Merging Behavior Analysis

Li, Gen

doi:10.1155/2018/1436521

Cited by 23 publications

(20 citation statements)

References 41 publications

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“…If the value of hypothesis hθ(w) ≥ 0.5, then it shows that z = 1 implying that the person has Cardiovascular disease and if hθ(w) < 0.5, then z = 0 implying that the person is healthy. Therefore, under the condition 0 ≤ hθ(w) ≤ 1, the prediction of Logistic Regression is done [30]. The Logistic Regression function is expressed as:…”

Section: A Logistic Regressionmentioning

confidence: 99%

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Prabhakar

Rajaguru²,

Lee

2019

IEEE Access

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One of the most useful methodologies which provide a specific waveform showing the pulsating peripheral blood flow in a non-invasive manner is Photoplethysmography (PPG). The design, application and implementation of a PPG system are quite inexpensive and have a very easy maintenance. Without having direct contact with the surface of the skin, PPG can easily take the measurements. Therefore, PPG has a good medical competency and due to its widespread availability, it has a lot of advantages. A PPG signal can sometimes be substituted or complemented by an Electrocardiography (ECG) signal as it can provide Heart Rate Variability (HRV) analysis. In this work, an in-depth analysis of classification of Cardiovascular Disease (CVD) is done with the help of Capnobase dataset. Initially, metaheuristic optimization algorithms are utilized as dimensionality reduction techniques and then the dimensionally reduced values are classified with the help of different classifiers for the classification of CVD. The results show that for the PPG normal cases, a high classification accuracy of 99.48% is obtained when Chi square Probability Density Function (PDF) optimized values are classified with Artificial Neural Networks (ANN) and a second highest classification accuracy of 98.96% is obtained when Chicken swarm optimized values are classified with Naïve Bayesian Classifier (NBC). Similarly when the PPG abnormal cases or PPG with CVD cases are concerned, a high classification accuracy of 99.48% is obtained when Chi square PDF optimized values are classified with Logistic Regression and a second highest classification accuracy of 98.96% is obtained when Chi square CDF optimized values are classified with Gaussian Support Vector Machine (SVM) and when Chicken swarm optimized values are classified with NBC.

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Section: A Logistic Regressionmentioning

confidence: 99%

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Prabhakar

Rajaguru²,

Lee

2019

IEEE Access

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“…For details on the data processing, one can refer to Ref. [5], [6], [66] After smoothing, filtering and consistency checking, trajectories of 388 merging vehicles were extracted from the dataset.…”

Section: B Data Processingmentioning

confidence: 99%

“…In previous studies, drivers are always assumed to be homogeneous and consistent during the driving process, which raises questions regarding the performance of microscopic traffic simulation models in representing reality [9]- [14]. Studies have demonstrated wide heterogeneity among drivers in terms of driving behavior [5], [6], [15], [16]. Different drivers may behave differently under the same traffic conditions and the same driver may even behave differently under varying traffic conditions.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Vehicle Merging Position Selection Behaviors Based on a Finite Mixture of Linear Regression Models

Pan

Yang

et al. 2019

IEEE Access

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Vehicle merging is a complex and tactical decision process. Merging position selection behavior has been largely ignored in microscopic traffic simulators. Driver heterogeneity has received substantial attention in recent years; however, few studies have focused on the heterogeneity in merging behaviors. To account for the heterogeneity among merging drivers during the merging process and to improve the accuracy of the merging model, a finite mixture of linear regression models was developed for describing the merging position selection model. BIC was used to determine the optimal number of classes, and Latent Gold 5.0 was used to estimate parameters. Based on the US101 data in the NGSIM project, which were provided by FHWA, a 3-class linear regression model was developed. The results demonstrate that the variables differ across the classes, and the sign of each variable may also differ among the classes; hence, the strategies that are used by drivers for merging position selection differ across the classes. Cooperative lane changing of the putative leading vehicle was found to have significant influence on the merging position selection behavior; thus, merging behavior is a two-dimensional behavior that may be influenced by both lateral and longitudinal factors. Compared with previous studies, the proposed model can naturally identify the heterogeneity among drivers and is much more accurate; therefore, the proposed model is a promising tool for microscopic traffic simulation and automatic driving systems or driver assistance systems.INDEX TERMS Microscopic traffic simulation, merging position selection behavior, finite mixture of linear regression model, heterogeneity, cooperative lane change.

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“…In the vicinity of these bottlenecks, the lane-changing behavior is shown to affect traffic throughput, safety, and turbulence (6)(7)(8)(9)(10)(11)(12). A limited body of research has shown that heterogeneity exists within the merging and diverging behaviors of drivers (13)(14)(15). However, they do not take into account the behavior of truck drivers who are a significant part of traffic on truck-dominated motorways.…”

mentioning

confidence: 99%

Categorizing Merging and Diverging Strategies of Truck Drivers at Motorway Ramps and Weaving Sections using a Trajectory Dataset

Sharma

Snelder

Tavasszy

et al. 2020

Transportation Research Record

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Lane-changing models are essential components for microscopic simulation. Although the literature recognizes that different classes of vehicles have different ways of performing lane-change maneuvers, lane change behavior of truck drivers is an overlooked research area. We propose that truck drivers are heterogeneous in their lane change behavior too and that inter-driver differences within truck drivers exist. We explore lane changing behavior of truck drivers using a trajectory data set collected around motorway bottlenecks in the Netherlands which include on-ramp, off-ramp, and weaving sections. Finite mixture models are used to categorize truck drivers with respect to their merging and diverging maneuvers. Indicator variables include spatial, temporal, kinematic, and gap acceptance characteristics of lane-changing maneuvers. The results suggest that truck drivers can be categorized into two and three categories with respect to their merging and diverging behaviors, respectively. The majority of truck drivers show a tendency to merge or diverge at the earliest possible opportunity; this type of behavior leads to most of the lane change activity at the beginning of motorway bottlenecks, thus contributing to the raised level of turbulence. By incorporating heterogeneity within the lane-changing component, the accuracy and realism of existing microscopic simulation packages can be improved for traffic and safety-related assessments.

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Application of Finite Mixture of Logistic Regression for Heterogeneous Merging Behavior Analysis

Cited by 23 publications

References 41 publications

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Modeling Vehicle Merging Position Selection Behaviors Based on a Finite Mixture of Linear Regression Models

Categorizing Merging and Diverging Strategies of Truck Drivers at Motorway Ramps and Weaving Sections using a Trajectory Dataset

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