Analysis of Land Use Optimization of Metro Station Areas Based on Two-Way Balanced Ridership in Xi’an

Zhao, Meng; Tong, Haiyan; Li, Bo; Duan, Yaqiong; Li, Yubai; Wang, Jianpo; Lei, Kexin

doi:10.3390/land11081124

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…In practice, considering the differences in urban development and residents' walking speed, physical strength and many other factors, the acceptable metro access distance varies among residents in different cities. Regarding previous studies (Cardozo et al 2012;Gan et al 2020a;Li et al 2020;Zhao et al 2022), the scope of PCA of metro stations in this paper radiates 800 m outward centering on the stations.…”

Section: 11overview Of the Study Areamentioning

confidence: 74%

Nonlinear relationships between built environmental characteristics and ridership in Xi'an metrostation

Hou

Duan

et al. 2023

Preprint

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In recent years, Xi'an metro construction has been progressing rapidly, becoming the primary mode of urban green public transportation. Since the ridership of the metro is closely linked to the characteristics of its surrounding built environment, a key problem in promoting the benign development between the two is to explore the spatiotemporal distributional difference in ridership and its influencing factors. In this study, the "5D" characteristics of built environment are described by density, diversity, design, destination and distance variables. The spatiotemporal distribution characteristics of ridership are analyzed via Arc GIS and Python, while the nonlinear relationships between ridership and built environment of 106 metro stations of downtown Xi'an, as well as relevant threshold effects are revealed via Shapley additive explanations with gradient boosted decision tree (GBDT-SHAP). The results show that: (1) Xi'an metro travel presents a medium-short spatiotemporal distribution, and the ridership network is characterized by strong center-spillover. (2) The nonlinear relationship between built environment and ridership is ubiquitous and presents a threshold effect. The impact threshold of bus stop density on ridership is 4-6 pcs/km2, the impact threshold of road network density is roughly 4-5 km/km2, and the effective threshold of building density does not exceed 20%. (3) The positive impact of POI facility density on peak ridership is stronger than that at flat hours. Variables like land use mixture, population density and distance from downtown have a time-driven effect on the ridership, whose importance and influence change with time. This study provides a better understanding of the spatiotemporal impact of Xi'an's built environment on metro travel, which is of profound significance for the coordinated development between the city and metro construction.

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Section: 11overview Of the Study Areamentioning

confidence: 74%

Nonlinear relationships between built environmental characteristics and ridership in Xi'an metrostation

Hou

Duan

et al. 2023

Preprint

Self Cite

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“…Furthermore, tools for simulating and predicting land use along public transportation routes have evolved based on these advancements. For instance, the widespread adoption of the cellular automata model has contributed to the development of the CA-Markov model, facilitating the quantitative analysis of land-use dynamics around subway stations 16,17 . However, current scholarly focus tends to encompass the entire subway lines, with fewer studies concentrating on small-scale land use and the spatial structure around individual stations 18 .…”

Section: Introductionmentioning

confidence: 99%

AI spatio-temporal prediction of industrial heritage land-use influenced by dynamic passenger flow at metro stations

Fei,

Ke,

Fangzhou

et al. 2024

Preprint

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Efficient land development in megacities’ industrial heritage areas is heavily influenced by the influx of mass passenger flows from new subway stations. To address this, we developed a dynamic passenger flow-oriented land use prediction model of subway stations. This model iterates a simulation model for dynamic passenger flow based on tourists and residents with an artificial neural network for land-use prediction. By enhancing the Kappa coefficient to 0.86, our model accurately simulated pedestrian flow density from stations to streets. We conducted experiments to predict inefficient land-use scenarios and compared them with the current state in national industrial heritage areas. The results demonstrated that the AnyLogic-Markov-FLUS Coupled Model outperformed expert experience in objectively assessing dynamic passenger flow impacts on the carrying capacity of old city neighborhoods during peak and off-peak periods at subway stations. This model can assist in resilient urban space planning and decision making regarding mixed land use.

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“…The location and construction of subway stations will have an impact on the surrounding land. Therefore, the use of land for subway stations will affect the surrounding planning and construction [49]. The network analysis was used to identify the loopholes and key stations in the subway and bus networks, respectively, which could provide guidance for developing cities in China and other countries [50].…”

Section: Introductionmentioning

confidence: 99%

A Coupling Model for Measuring the Substitution of Subways for Buses during Snowstorms: A Case Study of Shenyang, China

Wu,

et al. 2024

Sustainability

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The development of integrated public transportation networks has received widespread attention in recent years. Especially in global northern cities, improving the substitution of subways for buses could meet population travel demand during snowstorms, which minimizes the impact of snowstorms on the public transportation network. Furthermore, the development of rail transit is conducive to the intensive and efficient use of land resources. Therefore, in this study, we selected a northern Chinese city, Shenyang, as a case study. For obtaining the population travel demand, we collected the actual population flow data in the morning and evening peaks during snowstorms. The network analysis was used to identify the loopholes and key stations in the subway and bus networks, respectively. A coupling model was built to measure the coupling value of each station in the subway and bus networks, according to its population travel demand and supply capacity, which was further used to measure the substitution of subways for buses in the morning and evening peaks during snowstorms. The results indicate that some subway stations were in a coupling state, while their surrounding bus stations were in a decoupling state. These subway stations could replace the bus stations to reduce the impact and damage of snowstorms on public transportation network. However, some subway stations and the surrounding bus stations were all in a decoupling state, which were under great pressure to meet the population commuting demand during snowstorms. This study can provide insight into optimizing public transportation network planning and design in many northern regions and help to coordinate land and transportation utilization.

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Analysis of Land Use Optimization of Metro Station Areas Based on Two-Way Balanced Ridership in Xi’an

Cited by 4 publications

References 20 publications

Nonlinear relationships between built environmental characteristics and ridership in Xi'an metrostation

Nonlinear relationships between built environmental characteristics and ridership in Xi'an metrostation

AI spatio-temporal prediction of industrial heritage land-use influenced by dynamic passenger flow at metro stations

A Coupling Model for Measuring the Substitution of Subways for Buses during Snowstorms: A Case Study of Shenyang, China

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