An entropy-based analysis of lane changing behavior: An interactive approach

Koşun, Çağlar; Ozdemir, Serhan

doi:10.1080/15389588.2016.1204446

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…In literature, there are numerous approaches examining the complexity and dynamics of traffic flow and one of them is the use of entropy-based methods. In terms of this, recent various studies, e.g., [1][2][3][4][5][6] would be typical in the vehicular traffic arena. Of these, for example, Kosun and Ozdemir [1] focus on the platoon formation of vehicles and propose an upper and a lower limit of Tsallis q entropic index.…”

Section: Introductionmentioning

confidence: 98%

Examining vehicular speed characteristics through divergences from prior distributions

Koşun

2021

SN Appl. Sci.

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A variety of approaches, within literature, has been conducted to interpret vehicular speed characteristics. This study turns the attention to the entropy-based approaches, and thus focuses on the maximum entropy method of statistical mechanics and the Kullback–Leibler (KL) divergence approach to examining the vehicular speeds. The vehicle speeds at the selected highway are analyzed in order to find out the disparities among them. However, it is turned out that the speed dynamics could not be distinguished over the speed distributions; hence the maximization of Shannon entropy seems insufficient to compare the speed distributions of each data set. For this reason, the KL divergence approach was performed. This approach displays the comparison, among the speed distributions, based on two prior distribution models, i.e., uniform and Gauss. The examination of the trends of KL divergences obtained from both distributions was made. It was concluded that the KL divergence values for the highway speed data sets ranged between about 0.53 and 0.70 for the uniform case, while for the Gaussian case the obtained values are between 0.16 and 0.33. The KL divergence trends for the real speeds were obtained analogous for both cases, but they differed significantly when the synthetic data sets were employed. As a result, the KL divergence approach proves suitable as an appropriate indicator to compare the speed distributions.

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Section: Introductionmentioning

confidence: 98%

Examining vehicular speed characteristics through divergences from prior distributions

Koşun

2021

SN Appl. Sci.

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“…Entropy analysis has been applied to traffic and transportation planning since the 1980s [ 12 , 13 ]. Previous studies applied entropy-based methods to identify different levels of the orderliness of traffic flow in a roadway network for the purposes of incident detection, roadway safety analysis, and driving behavior analysis [ 11 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction

Li²,

Song³

et al. 2022

Entropy

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Three different types of entropy weight methods (EWMs), i.e., EWM-A, EWM-B, and EWM-C, have been used by previous studies for integrating prediction models. These three methods use very different ideas on determining the weights of individual models for integration. To evaluate the performances of these three EWMs, this study applied them to developing integrated short-term traffic flow prediction models for signalized intersections. At first, two individual models, i.e., a k-nearest neighbors (KNN)-algorithm-based model and a neural-network-based model (Elman), were developed as individual models to be integrated using EWMs. These two models were selected because they have been widely used for traffic flow prediction and have been approved to be able to achieve good performance. After that, three integrated models were developed by using the three different types of EWMs. The performances of the three integrated models, as well as the individual KNN and Elman models, were compared. We found that the traffic flow predicted with the EWM-C model is the most accurate prediction for most of the days. Based on the model evaluation results, the advantages of using the EWM-C method were deliberated and the problems with the EWM-A and EWM-B methods were also discussed.

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An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model

Feng,

Wei,

et al. 2024

Traffic Injury Prevention

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An entropy-based analysis of lane changing behavior: An interactive approach

Cited by 4 publications

References 25 publications

Examining vehicular speed characteristics through divergences from prior distributions

Examining vehicular speed characteristics through divergences from prior distributions

Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction

An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model

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