The Distribution Pattern of the Railway Network in China at the County Level

Evaluation of the railway network distribution and its impacts on social and economic development has great significance for building an efficient and comprehensive railway system. To address the lack of evaluation indicators to assess the railway network distribution pattern at the macro scale, this study selects eight indicators—railway network density, railway network proximity, the shortest travel time, train frequency, population, Gross Domestic Product (GDP), the gross industrial value above designated size, and fixed asset investment—as the basis of an integrated railway network distribution index which is used to characterize China’s railway network distribution using geographical information system (GIS) technology. The research shows that, in 2015, the railway network distribution was low in almost half of China’s counties and that there were obvious differences in distribution between counties in the east and west. In addition, multiple dense areas of railway network distribution were identified. The results suggest that it might be advisable to strengthen the connections between large and small cities in the eastern region and that the major urban agglomerations in the midwest could focus on strengthening the construction of railway facilities to increase the urban vitality of the western region. This study can be used to guide the optimization of railway network structures and provide a macro decision-making reference for the planning and evaluation of major railway projects in China.

[1]  Hyunwoo Lim,et al.  Intermodal Freight Transportation and Regional Accessibility in the United States , 2008 .

[2]  Stephan Huber,et al.  Calculate Travel Time and Distance with Openstreetmap Data Using the Open Source Routing Machine (OSRM) , 2016 .

[3]  Zhang Youquan,et al.  Monitoring and Analysis of Land Subsidence Along Beijing-Tianjin Inter-City Railway , 2016, Journal of the Indian Society of Remote Sensing.

[4]  Fengjun Jin,et al.  Impacts on accessibility of China’s present and future HSR network , 2014 .

[5]  Fengjun Jin,et al.  Evolution of regional transport dominance in China 1910–2012 , 2015, Journal of Geographical Sciences.

[6]  Chi-Yin Chow,et al.  Efficient evaluation of shortest travel-time path queries through spatial mashups , 2017, GeoInformatica.

[7]  P Rietveld,et al.  The Accessibility of European Cities: Theoretical Framework and Comparison of Approaches , 1998 .

[8]  F. Jie,et al.  The scientific foundation of Major Function Oriented Zoning in China , 2009 .

[9]  John Armstrong,et al.  New routes on old railways: increasing rail’s mode share within the constraints of the existing railway network , 2016 .

[10]  Andres Monzon,et al.  Has HSR improved territorial cohesion in Spain? An accessibility analysis of the first 25 years: 1990–2015 , 2019, Spatial Implications and Planning Criteria for High-Speed Rail Cities and Regions.

[11]  Fahui Wang,et al.  Spatiotemporal evolution of China's railway network in the 20th century: An accessibility approach (vol 43, pg 765, 2009) , 2009 .

[12]  Linchuan Yang,et al.  The implications of high-speed rail for Chinese cities: Connectivity and accessibility , 2018, Transportation Research Part A: Policy and Practice.

[13]  Paolo Beria,et al.  Measuring the long-distance accessibility of Italian cities , 2017 .

[14]  David M Levinson,et al.  Density and Dispersion: The Co-Development of Land Use and Rail in London , 2007 .

[15]  Renzhong Guo,et al.  Study on Population Distribution Pattern at the County Level of China , 2018, Sustainability.

[16]  Qingming Zhan,et al.  Accessibility analysis of urban emergency shelters: Comparing gravity model and space syntax , 2011, 2011 International Conference on Remote Sensing, Environment and Transportation Engineering.

[17]  Kay W. Axhausen,et al.  Graph-Theoretical Analysis of the Swiss Road and Railway Networks Over Time , 2009 .

[18]  Yong Fan,et al.  Distribution Characteristics of the Transportation Network in China at the County Level , 2019, IEEE Access.

[19]  Fengjun Jin,et al.  China’s regional transport dominance: Density, proximity, and accessibility , 2010 .

[20]  Rongrong Li,et al.  Exploring the impact of high speed railways on the spatial redistribution of economic activities - Yangtze River Delta urban agglomeration as a case study , 2016 .

[21]  Kees Maat,et al.  The impact of urban proximity, transport accessibility and policy on urban growth: A longitudinal analysis over five decades , 2019 .

[22]  Maria Manta Conroy,et al.  Accessibility Measures and the Social Evaluation of Urban Structure , 1977 .

[23]  J. M. Morris,et al.  Accessibility indicators for transport planning , 1979 .

[24]  Bert van Wee,et al.  Accessibility evaluation of land-use and transport strategies: review and research directions , 2004 .

[25]  Wang Li'e,et al.  Spatial Pattern & Quantitative Relationship of Industrial Structure of Shandong Peninsula Urban Agglomeration , 2016, 2016 3rd International Conference on Information Science and Control Engineering (ICISCE).

[26]  W. G. Hansen How Accessibility Shapes Land Use , 1959 .

[27]  Zhiyuan Zhao,et al.  Exploring Railway Network Dynamics in China from 2008 to 2017 , 2018, ISPRS Int. J. Geo Inf..

[28]  Daniel Franke,et al.  Job accessibility modelling in Prague Functional Urban Area , 2017, 2017 Smart City Symposium Prague (SCSP).

[29]  David M Levinson,et al.  Modeling the Growth of Transportation Networks: A Comprehensive Review , 2007 .