Impacts of Nighttime-Only Truck Traffic Regulation on Pavement Performance

2015 ◽  
Vol 21 (1) ◽  
pp. 04014032
Author(s):  
Shi Qiu ◽  
Danny X. Xiao ◽  
Kelvin C. P. Wang ◽  
Wenjuan Wang ◽  
Mike M. Moravec
Keyword(s):  
Pavement Performance ◽  
Truck Traffic ◽  
Traffic Regulation

scholarly journals Evaluation of Pavement Performance due to Overload Single-Trip Permit Truck Traffic in Wisconsin

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hani H. Titi ◽  
Nicholas J. Coley ◽  
Valbon Latifi
Keyword(s):  
Field Work ◽  
Test Site ◽  
Field Investigation ◽  
Pavement Performance ◽  
Truck Traffic ◽  
Traffic Loads ◽  
Pavement Deterioration ◽  
Design Software ◽  
Surface Distress ◽  
Pavement Me Design

This study investigated the impacts of overweight (OW) permit truck traffic on flexible pavement performance in Wisconsin using field investigation and analysis utilizing the AASHTOWare Pavement ME Design software. A database of overweight single-trip permit truck records was analysed to produce a network of Wisconsin corridors heavily travelled by OW trucks. Four Wisconsin highways were selected for investigation due to high levels of OW truck traffic. The research included field work (traffic counts and visual pavement surface distress surveys) and AASHTOWare Pavement ME Design. Comprehensive analyses were conducted to evaluate pavement performance due to normal traffic loads as well as normal traffic loads plus the OW truck traffic loads. The use of mechanistic-empirical (ME) pavement analyses provided a methodology for estimating the proportion of pavement deterioration attributable to OW truck traffic. OW axle load distributions were developed and integrated with baseline truck traffic levels to develop axle load spectra and other traffic input parameters for the ME pavement analysis. The predicted total pavement deterioration levels from the AASHTOWare Pavement ME Design software were generally consistent with the levels of deterioration observed. The proportion of pavement damage and deterioration attributable to OW truck traffic was predicted to constitute a relatively minor proportion of total deterioration, with most distress indices showing relative increases of approximately 0.5% to 4%, with a few outliers. However, due to the small proportion of OW vehicles relative to the overall traffic levels, the OW vehicles were generally predicted to cause up to ten times the per-truck damage as compared with a typical legal-weight truck, depending on the distress mode and the test site.

Efek dari Pemberitaan CCTV Lalu Lintas Terhadap Sikap Disiplin Berlalu Lintas

2018 ◽  
Vol 1 (2) ◽  
pp. 116-137
Author(s):  
Redi Panuju ◽  
Shintya Oktaviany Aury
Keyword(s):  
Mass Communication ◽  
Quantitative Methods ◽  
Communication Theory ◽  
Hypodermic Needle ◽  
Urban Village ◽  
Underlying Theory ◽  
Traffic Regulation ◽  
The Press ◽  
Traffic Violations ◽  
The Way

Abstract Dishub Surabaya and traffic Unit Polrestabes Surabaya partners  do speeding tickets through CCTV to reduce traffic violations in traffic and prevent accidents frequently happened. For the socialization of the press role is required so that residents will know  this new traffic regulation. The way of the spread CCTV by the way to traffic gridlock around the continuously to be done in make the community become disciplined in the traffic signs and regulations. The underlying theory is a theory of mass communication, theory of hypodermic needle theory, S-O-R theory, discipline theory, theories of adolescence. The methodology that was used this research is quantitative methods with type of correlational quantitative. In this research that are samples are teenagers klampis semalang 96 respondents. From the results of statistics done shows that significantly is the cctv news traffic to discipline attitude traffic in teenagers Klampis Semalang Urban village Klampis Ngasem in Sukolilo Surabaya city. Keywords : news, cctv traffic, discipline traffic

scholarly journals Actin and Microtubules Differently Contribute to Vacuolar Targeting Specificity during the Export from the ER

Membranes ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 299
Author(s):  
Monica De Caroli ◽  
Fabrizio Barozzi ◽  
Luciana Renna ◽  
Gabriella Piro ◽  
Gian-Pietro Di Sansebastiano
Keyword(s):  
Spatial Targeting ◽  
Traffic Regulation ◽  
Emergent Features ◽  
Vacuolar Sorting ◽  
Er Export ◽  
Golgi Network ◽  
Relationship Of ◽  
The Relationship ◽  
Fine Tune

Plants rely on both actin and microtubule cytoskeletons to fine-tune sorting and spatial targeting of membranes during cell growth and stress adaptation. Considerable advances have been made in recent years in the comprehension of the relationship between the trans-Golgi network/early endosome (TGN/EE) and cytoskeletons, but studies have mainly focused on the transport to and from the plasma membrane. We address here the relationship of the cytoskeleton with different endoplasmic reticulum (ER) export mechanisms toward vacuoles. These emergent features of the plant endomembrane traffic are explored with an in vivo approach, providing clues on the traffic regulation at different levels beyond known proteins’ functions and interactions. We show how traffic of vacuolar markers, characterized by different vacuolar sorting determinants, diverges at the export from the ER, clearly involving different components of the cytoskeleton.

Predicting Pavement Roughness Using Deep Learning Algorithms

2021 ◽  
pp. 036119812110237
Author(s):  
Qingwen Zhou ◽  
Egemen Okte ◽  
Imad L. Al-Qadi
Keyword(s):  
Neural Network ◽  
Service Life ◽  
Pavement Performance ◽  
Series Data ◽  
Statistical Regression ◽  
Network Algorithm ◽  
Pavement Roughness ◽  
Impacting Factors ◽  
Neural Network Algorithm ◽  
Maintenance Activities

Transportation agencies should measure pavement performance to appropriately strategize road preservation, maintenance, and rehabilitation activities. The international roughness index (IRI), which is a means to quantify pavement roughness, is a primary performance indicator. Many attempts have been made to correlate pavement roughness to other pavement performance parameters. Most existing correlations, however, are based on traditional statistical regression, which requires a hypothesis for the data. In this study, a novel approach was developed to predict asphalt concrete (AC) pavement IRI, utilizing datasets extracted from the Long-Term Pavement Performance (LTPP) database. IRI prediction is categorized by two models: (i) IRI progression over the pavement’s service life without maintenance/rehabilitation and (ii) the drop in IRI after maintenance. The first model utilizes the recurrent neural network algorithm, which deals with time-series data. Therefore, historical traffic data, environmental information, and distress (rutting, fatigue cracking, and transverse cracking) measurements were extracted from the LTPP database. A long short-term memory network was used to solve the vanishing gradient problem. Finally, an optimal model was achieved by setting the sequence length to 2 years. The second model utilizes an artificial neural network algorithm to correlate the impacting factors to the IRI value after maintenance. The impacting factors include maintenance activities; initial (new construction), milled, and overlaid AC thicknesses; as well as IRI value before maintenance activities. Combining the two models allows for the prediction of IRI values over AC pavement’s service life.

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