Capacity Issues in Sweden - Applications and Research

Bergh, T; Nordqvist, Karin; Strömgren, Per; Davidsson, F; Bång, Karl-Lennart; Carlsson, Anna

doi:10.1016/j.trpro.2016.06.004

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…The average traffic growth is 2% per year. Gravel road conditions change dynamically; therefore, they need maintenance three to four times a year, usually carried out in the summer when the roads are snow-free [ 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of the Acoustics of Loose Gravel

Saeed

Nyberg

Alam

et al. 2021

Sensors

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Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.

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Section: Introductionmentioning

confidence: 99%

Classification of the Acoustics of Loose Gravel

Saeed

Nyberg

Alam

et al. 2021

Sensors

View full text Add to dashboard Cite

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Evaluation of Sustainable Design Method for Three-Lane Entrance Ramps on Expressways in Urban Areas: A Case Study of Xi’an, China

Liu,

Pan,

Song

et al. 2023

IEEE Access

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Owing to the challenges faced by two-lane entrance ramps in merging areas on expressways in handling the growing traffic volume, designers are considering implementing three-lane entrance ramps. This approach alleviates capacity bottlenecks and facilitates efficient, safe, and green operations. However, according to the traditional design strategy, the long auxiliary lanes will bring large land occupation and construction consumption, creating obstacles to the reconstruction. To address this issue, this study proposes two new strategies and discusses the applicability of each scheme. A VISSIM microsimulation model was developed and calibrated using the collected data, and five evaluation indicators assessing environmental protection, capacity, traffic efficiency, safety, and construction consumption were selected. This study also presents a comprehensive evaluation(CE) method that combines the CRITIC (Criteria Importance Through Intercriteria Correlation) method and TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution), which considers the association between indicators and offers a more rational evaluation, aiming to recommend optimal designs for various traffic cases. The results indicate that two-lane ramps are still the preferred choice for lower traffic volumes and merging ratios, whereas three-lane ramps demonstrate excellent potential for application under heavy traffic conditions. The CE reveals substantial improvements in optimal designs, with capacity, delay, CO emissions, and conflicts showing maximum enhancements of approximately 20%, 45%, 5%, and 55%, respectively.

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