Big Data Analysis of Beijing Urban Rail Transit Fares Based on Passenger Flow

Gao, Honghu; Liu, Shifeng; Cao, Guangmei; Zhao, Pengfei; Zhang, Jianhai; Zhang, Peng

doi:10.1109/access.2020.2991069

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…Urban transportation is one of the most important infrastructure services to maintain the importance of the city and the life of the city [1]. e development of multilevel, threedimensional, and smart modern transportation will be the goal of urban planning and development.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Zhang

2022

Mathematical Problems in Engineering

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Based on spatial data dynamic analysis technology, the characteristics of urban rail transit passenger flow are analyzed. This study analyzes the passenger flow characteristics of urban rail transit from the perspective of operation and planning by using the dynamic analysis technology of spatial data, combined with social and economic development. Based on the analysis of research data from the study of passenger traffic and passenger traffic data measurement, the characteristics of passenger traffic from urban railways are derived from the time distribution, spatial distribution, and transfer of passengers waiting on the platform. The final length of line 3 is 39.7 m, the passenger volume is 102, with 11 people/day, the passenger flow density is 1.8/2.3, and the high section in peak hours is 2.4/3.2. By considering the various influencing factors, efforts should be made to develop urban rail transit projects to promote the growth of social and economic benefits.

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Section: Introductionmentioning

confidence: 99%

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Zhang

2022

Mathematical Problems in Engineering

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“…Furthermore, by scrutinizing patterns in travel habits and consumer behaviors, transit authorities can craft more effective fare strategies and promotional offerings. Such initiatives incentivize passengers to select optimal transfer options, minimizing costs and bolstering the overall efficiency of the transfer process [21][22][23].…”

Section: Social Media and Travel Behavior Researchmentioning

confidence: 99%

Exploring Passenger Satisfaction in Multimodal Railway Hubs: A Social Media-Based Analysis of Travel Behavior in China’s Major Rail Stations

Zeng,

Wang,

Gao

et al. 2024

Sustainability

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This study investigates the dynamics of passenger satisfaction and sustainable urban mobility within the context of multimodal railway hubs, focusing on travel behaviors at major stations in China. Against the backdrop of rapid urbanization and the nation’s initiatives to improve transportation efficiency, this research employs social media data analysis to assess passenger sentiment across six key transportation hubs in Eastern China. Utilizing methodological approaches such as keyword frequency analysis and semantic categorization of 39,061 Dianping reviews, supplemented by network visualizations with Gephi, this study reveals insights into factors influencing passenger satisfaction beyond travel efficiency. Signage quality, facility availability, queueing, and crowding emerge as significant determinants of passenger behavior. The study underscores the importance of strategic improvements in station design, navigational aids, and facility management, grounded in real-time data analytics and passenger feedback, to enhance overall passenger satisfaction and promote sustainable urban mobility. This research contributes to advancing understanding of passenger behavior and informs efforts aimed at improving urban transportation systems to meet the evolving needs of passengers and cities.

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“…Scholars have considered a variety of connection methods and divided the attraction range of urban RTS into reasonable and irrational attraction ranges [37]. Regarding the attraction range of sites, scholars believed that the range should be between 400 m and 800 m according to the actual situation, and 800 m is widely used at present [33,38,39]. Firstly, the number of passenger and land types around urban rail stations is counted.…”

Section: Data Processingmentioning

confidence: 99%

Clustering of Passenger Flow and Land-Use of Beijing Urban Rail Transit Stations Based on Multi-Source Data

2024

Teh. vjesn.

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With the deepening of transit-oriented development (TOD), the construction of high-density, multi-functional urban spatial structures around public transportation stations has become a significant focus in urban development planning. This study is aimed to explore the spatial and temporal cluster patterns and the relationship between taxi passenger flow and land use types around urban rail transit stations. Taking Beijing as an example, this study extracts the time series of pick-up and drop-off points from the taxi GPS track dataset and employs the affinity propagation (AP) method to conduct the spatial and temporal cluster analysis on these taxi pick-up and drop-off points. Then, based on the urban POI dataset, this study classifies these stations into four functional types by adopting the K-means method. Finally, the partial least squares (PLS) method has been used to explore the relationship between the taxi passenger flow and the land use types under different time intervals. The result indicates that: first, there is a regional aggregation effect for the taxi passenger source in the rail transit station area, and the cluster areas are mainly located in Chaoyang and Haidian districts, as well as along the circular metro line. Besides, there is a significant rush hour in the morning and evening for this passenger flow. Second, the commercial, residential, and comprehensive transit stations are mainly located in the central urban districts of Beijing with balanced spatial distribution, while the industrial transit stations are dispersed to the outer suburbs of the city, which conform to Alonso's spatial distribution of land. Third, there is a significant positive correlation between taxi passenger sources in the rail transit station area and land use types containing transportation service, public facility and daily life service. This study analyses the population mobility and land type cluster form around the urban rail transit from the micro-perspective, which verifies and supplements Alonso's transportation location theory. Therefore, this study contributes to the rational planning of urban spatial resource allocation and the construction of the urban rail transit network.

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Big Data Analysis of Beijing Urban Rail Transit Fares Based on Passenger Flow

Cited by 9 publications

References 26 publications

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Exploring Passenger Satisfaction in Multimodal Railway Hubs: A Social Media-Based Analysis of Travel Behavior in China’s Major Rail Stations

Clustering of Passenger Flow and Land-Use of Beijing Urban Rail Transit Stations Based on Multi-Source Data

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