Analysis of Road Network Pattern Considering Population Distribution and Central Business District

Zhao, Fangxia; Sun, Huijun; Wu, Jianjun; Gao, Ziyou; Liu, Ronghui

doi:10.1371/journal.pone.0151676

Cited by 18 publications

(6 citation statements)

References 34 publications

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“…The VISSIM parameters need to be calibrated and validated with the investigation data to ensure that the simulation is accurate [54, 55]. Traffic data, capacity and geometric measures were collected during the investigation to calibrate the simulation model in VISSIM [56].…”

Section: Datamentioning

confidence: 99%

Evaluating the sustainable traffic flow operational features of an exclusive spur dike U-turn lane design

Shao

Han

et al. 2019

PLoS ONE

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The traditional U-turn design has significantly improved traffic operations for relieving traffic congestion. However, the U-turn diversion and merge segments still cause traffic conflicts and delays. In this paper, an exclusive spur dike U-turn lane (ESUL) is proposed with the aim of addressing the disadvantages of the traditional U-turn design. ESUL provides a separate U-turn lane to diverge, decelerate, U-turn, accelerate and merge without interacting with through traffic. The effectiveness of ESUL is demonstrated through a field data investigation, simulation and analysis with VISSIM software. The proposed design is evaluated in terms of three parameters: travel time, delay and number of stops. Compared to the traditional U-turn design, ESUL can reduce travel time by 29.15%, delay by 66.70% and the number of stops by 100% at most. The results showed that ESUL has better performance than the traditional U-turn design and could be implemented to reduce traffic congestion and the potential hazards caused by U-turn maneuvers.

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Section: Datamentioning

confidence: 99%

Evaluating the sustainable traffic flow operational features of an exclusive spur dike U-turn lane design

Shao

Han

et al. 2019

PLoS ONE

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“…In addition, a research study investigating the correlation between street networks and traffic flow in the city center revealed that urban road traffic networks can be broadly classified into several types, such as square grid, belt, radial, circular radial, and free-form [ 39 , 40 ]. However, the physical road infrastructure is not limited to a single configuration, but often consists of a combination of two or more basic road networks [ 41 – 43 ]. Accordingly, Syukri et al, utilized Geographic Information Systems (GIS) to determine the spatial availability of transportation in order to reveal the principles that govern road network accessibility [ 44 ].…”

Section: Introductionmentioning

confidence: 99%

River valley urban network and morphology: A study on the urban morphology evolution of Lanzhou

Yang,

Qian,

et al. 2024

PLoS ONE

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The present study investigates the dynamic evolution characteristics of urban spatial morphology by analyzing real road network data from 2000, 2010, and 2020, along with nighttime lighting data employing spatial analysis methods and spatial syntax models. Accordingly, two separate dimensions of urban morphology: internal and external, are covered. First, the integration and synergy of interior morphology features are analyzed using spatial syntactic modeling. Subsequently, the spatial compactness, fractal dimension, and level of center of gravity shift of the city are assessed by combining the nighttime lighting data with the earlier dataset. This analysis facilitated the deep exploration of the spatiotemporal evolution of the city’s external morphology. Building upon this foundation, the interaction between the "internal and external" domains was analyzed further. The main findings of the study reveal a synchronous pattern of urban expansion throughout the evolution of urban spatial morphology. Furthermore, the urban form was observed to undergo a progressive transformation, transitioning from a "single core" morphology to a "primary and secondary double core" morphology. Over time, this development progressed and evolved into a "belt-like multi-core" structure. Additionally, the coupling characteristics further validate the relationship between the structure of the road network and the urban morphology in river valley-type cities. In particular, accessibility of dense and horizontally distributed transportation network was found to significantly influence the spatial development of these cities. As observed, the findings provides valuable insights into understanding the characteristics of internal and external associations regarding urban spatial patterns.

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“…Researchers have shown that the majority of the world’s population is concentrated in low-altitude plains [ 27 ], and the two types of HSI proposed in previous research would be not applicable to study the urban population distribution in such low-altitude areas. Road network density (RND) is an important variable for simulating urban population distribution, as the road grid bureau is the spatial structure of urban population distribution [ 28 ]. Considering the importance of RND in simulating social and economic parameters (such as population and gross domestic product), there are few studies utilizing RND for correction to improve the HSI.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method of Determining Human Population Distribution Based on Luojia 1-01 Nighttime Light Imagery and Road Network Data—A Case Study of the City of Shenzhen

Zhou

Zheng

et al. 2020

Sensors

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Previously published studies on population distribution were based on the provincial level, while the number of urban-level studies is more limited. In addition, the rough spatial resolution of traditional nighttime light (NTL) data has limited their fine application in current small-scale population distribution research. For the purpose of studying the spatial distribution of populations at the urban scale, we proposed a new index (i.e., the road network adjusted human settlement index, RNAHSI) by integrating Luojia 1-01 (LJ 1-01) NTL data, the enhanced vegetation index (EVI), and road network density (RND) data based on population density relationships to depict the spatial distribution of urban human settlements. The RNAHSI updated the high-resolution NTL data and combined the RND data on the basis of human settlement index (HSI) data to refine the spatial pattern of urban population distribution. The results indicated that the mean relative error (MRE) between the population estimation data based on the RNAHSI and the demographic data was 34.80%, which was lower than that in the HSI and WorldPop dataset. This index is suitable primarily for the study of urban population distribution, as the RNAHSI can clearly highlight human activities in areas with dense urban road networks and can refine the spatial heterogeneity of impervious areas. In addition, we also drew a population density map of the city of Shenzhen with a 100 m spatial resolution for 2018 based on the RNAHSI, which has great reference significance for urban management and urban resource allocation.

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Analysis of Road Network Pattern Considering Population Distribution and Central Business District

Cited by 18 publications

References 34 publications

Evaluating the sustainable traffic flow operational features of an exclusive spur dike U-turn lane design

Evaluating the sustainable traffic flow operational features of an exclusive spur dike U-turn lane design

River valley urban network and morphology: A study on the urban morphology evolution of Lanzhou

An Improved Method of Determining Human Population Distribution Based on Luojia 1-01 Nighttime Light Imagery and Road Network Data—A Case Study of the City of Shenzhen

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