Influence of vehicular traffic on environmental noise spectrum in the tourist route of Santa Marta City

Jiménez-Uribe, Dámaris A.; Daniels, Darwin; González-Álvarez, Álvaro; Vélez-Pereira, Andrés M.

doi:10.1016/j.egyr.2019.11.008

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Detecting and counting of wild animals is important for ecology and wildlife management [6]. Classifying vehicles into autobuses, automobiles, motorcycles, and trucks is useful for traffic noise analysis [7].…”

Section: Introductionmentioning

confidence: 99%

Count Estimation With a Low-Accuracy Machine Learning Model

Sei

Ohsuga

2021

IEEE Internet Things J.

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Many Internet-of-Things (IoT) systems use machine learning techniques, such as deep neural networks. IoT systems can predict attributes, such as age, sex, car speed, human walking speed, and types of animals, using machine learning techniques. Although the functionality of machine learning is undeniable, the prediction accuracy is not always high. When a machine learning model is used to recognize several objects in an object counting system, the estimated count will have a significant error because of the accumulation of the recognition error of each object. In this study, a count estimation method that uses a confusion matrix generated in the training phase was proposed. The proposed method consists of an iterative Bayesian technique with the confusion matrix for count estimation and mitigating over-iterations technique for reducing estimated errors. The proposed method can be used even for a low-accuracy machine learning model. Experiments with synthetic and real data sets were conducted to demonstrate the functionality of the proposed method. The estimation errors of the proposed method were reduced by 64.3% in average compared to the baseline method in the experiments.

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Section: Introductionmentioning

confidence: 99%

Count Estimation With a Low-Accuracy Machine Learning Model

Sei

Ohsuga

2021

IEEE Internet Things J.

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“…Furthermore, continuous improvements to transportation conditions also resulted in a continual enhancement of tourists' tourism experience and transportation satisfaction [37]. Meanwhile, environmental [38][39][40], noise [41,42], and congestion [43] problems in the transportation network have affected the sustainable development of tourism. In China, as the tourism industry developed, scholars in the 1980s began to explore the relationship between transportation and tourism [44].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Complex Transportation Network and Its Tourism Utilization Potential: A Case Study of Guizhou Expressways

Sihong

Liu

et al. 2020

Complexity

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Transportation is an example of a typical, open, fluid complex network system. Expressways are one form of complex transportation networks, and expressway service areas serve as infrastructure nodes in the expressway transportation network; hence, their construction has a significant impact on tourism development and utilization. Domestic and foreign studies on complex transportation networks have mostly been conducted from the perspective of railways, air transport, and urban transportation but seldom on expressway transportation networks. This study employed complex network theory, social network analysis, kernel density analysis, and bivariate autocorrelation to characterize the spatial structure of expressway transport networks in terms of geographical centrality. By innovating the coupling of geographical centrality and passenger flow centrality in clustering, the study also quantitatively analyzed the differences between the geographical advantage and actual passenger flow advantage of China’s Guizhou expressway transportation network to analyze the tourism utilization potential of expressway service areas. We found that (1) the geographical centrality of the Guizhou expressway transportation network ranged from −1.28 to 3.33, and its distribution shows a single-core, polyconcentric dispersed spatial structure; (2) the passenger-car flow rate ranged from 15,000 to 3.66 million, and its distribution showed a dual-core, polycentric dispersed structure that is weakly concentric; and (3) there was a positive correlation of 0.22 between the geographical centrality and passenger flow of the Guizhou expressway transportation network, which showed seven cluster types—“high-high,” “moderately high-high,” “low-high,” “moderately low-high,” “high-low,” “moderately high-low,” and “low-low”—for which seven corresponding models of tourism development were proposed. This study broadens the practical application of traffic network complexity research and provides a scientific basis for upgrading and transforming the Guizhou expressway transportation network as well as for developing composite tourism uses for expressway service areas.

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