The short-distance continuous diversion area plays a crucial role within mountainous urban expressway systems, significantly enhancing the efficiency of specialized road sections through capacity analysis. This study develops a capacity calculation model tailored to the diversion area's unique characteristics and principal capacity-influencing factors. Initially, the research focuses on a specific short-distance continuous diversion area of a mountainous urban expressway, employing video trajectory tracking technology to gather trajectory data. This data serves as the basis for analyzing road and traffic characteristics. Subsequently, the model computes the capacity influenced by eight variables, including diversion point spacing and deceleration lane length, using VISSIM simulation experiments. A gray correlation analysis identifies key factors, which guide the establishment of the model's fundamental structure through two-factor surface fitting results. Mathematical statistical methods are then applied to resolve the model's parameters, culminating in a robust capacity calculation model. The findings reveal that diversion point spacing, along with primary and secondary diversion ratios, significantly influence capacity. Notably, the capacity exhibits a marked quadratic polynomial relationship with the primary diversion ratio and diversion point spacing, and a linear relationship with the secondary diversion ratio. The model's validity is confirmed through a case study at the diversion area north of Huacun Interchange in Chongqing Municipality, where the discrepancy between calculated and actual capacities is under 5%, underscoring the model's high accuracy. These results offer valuable theoretical and methodological support for the planning, design, and traffic management of diversion areas.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11463751 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0306881 | PLOS |
Phys Rev E
October 2024
Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
PLoS One
October 2024
Key Laboratory of Spatio-Temporal Information in Mountainous Urban Areas, Chongqing Municipality, Chongqing, P. R. China.
J Epidemiol Glob Health
September 2024
Department of Biogeography, University of Bayreuth, Universitaetsstr. 30, 95447, Bayreuth, Germany.
BMJ Open
February 2024
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
Objectives: This study aimed to assess the healthcare-seeking behaviour and related factors of people with acute respiratory symptoms in the rural areas of central and western China to estimate the disease burden of influenza more accurately.
Design: Cross-sectional survey.
Settings: Fifty-two communities/villages in the Wanzhou District, Chongqing, China, a rural area in southwest China, from May 2022 to July 2022.
Sci Total Environ
April 2024
National Isotope Center, GNS Science, Lower Hutt 5040, New Zealand; CIRES, University of Colorado, Boulder, Colorado 80305, USA.
A two-year (March 2021 to February 2023) continuous atmospheric CO and a one-year regular atmospheric CO measurement records were measured at the northern foot of the Qinling Mountains in Xi'an, China, aiming to study the temporal characteristics of atmospheric CO and the contributions from the sources of fossil fuel CO (CO) and biological CO (CO) fluxes. The two-year mean CO mole fraction was 442.2 ± 16.
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