Abstract
Urban planners have always concerned about the relationship between macroeconomic trend of development and transport infrastructure planning. In the past, urban planners presented qualitative analysis for the construction of transport infrastructure depended on experience. Based on the acquired data of transport infrastructure system and urban macroeconomic, we take the urban development indicators as independent variable and transport infrastructure system indicators as dependent variable. Then, we extract factors and reduce the interference of weak correlation by the method of principal component analysis (PCA). In the aspect of establishing linkage model, BP neural network simplifies topology structures well by adjusting the discrete input, and the minimum error between actual value and predicted value was used as training objectives. Finally, we get a quantitative analysis of the relationship between urban macroeconomic and transport infrastructure. We take Changsha city in China as a case, reducing dimension by PCA and extracting 8 main indicators from original 14 indicators of urban macroeconomic. The precision reaches to 0.99992 and error value is less than 5%. We work out the best weight matrix W 1 and the offset value matrix B 1, B 2 for urban development and transport system in Changsha city. It provides quantitative methodology for policymakers in transport system planning.
Key subject area: Transportation Planning
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Button, K. 2002. City management and urban environmental indicators 40: 217–233.
Lei, X., J. Zhang, and J. Li. 2012. A system dynamics model for urban low-carbon transport and simulation in the city of Shanghai, China. Advances in Information Sciences & Service Sciences 4 (1).
Cascetta, E. 2013. Transportation systems engineering: theory and methods. Springer Science & Business Media.
Mihyeon Jeon, C., and A. Amekudzi. 2005. Addressing sustainability in transportation systems: Definitions, indicators, and metrics. Journal of Infrastructure Systems 11 (1): 31–50.
Zhou, S., and X. Yan. 2005. The relationship between urban structure and traffic demand in Guangzhou. Acta Geographica Sinica 1: 14.
Button, K. 2002. City management and urban environmental indicators. Ecological Economics 40 (2): 217–233.
Haase, D., N. Schwarz, and Others. 2009. Simulation models on human nature interactions in urban landscapes: A review including spatial economics, system dynamics, cellular automata and agent-based approaches. Living Reviews in Landscape Research 3 (2): 1–45.
Bartholomew, K. 2007. Land use-transportation scenario planning: promise and reality. Transportation 34 (4): 397–412.
O’Sullivan, D., and P. Torrens. 2001. Cellular models of urban systems. In Theory and practical issues on cellular automata, ed. S. Bandini and T. Worsch, 108–116. London: Springer.
Næss, P. 2006. Urban structure matters. Routlegde, Abingdon.
Richardson, B.C. 2005. Sustainable transport: Analysis frameworks. Journal of Transport Geography 13 (1): 29–39.
Bunting, T. E., and P. Filion. 1999. Dispersed city form in Canada: A Kitchener CMA case example. The Canadian Geographer 43 (3): 268–287.
Meyer, M.D. 1999. Demand management as an element of transportation policy: Using carrots and sticks to influence travel behavior. Transportation Research Part A: Policy and Practice 33 (7–8): 575–599.
Giuliano, G., and J. Dargay. 2006. Car ownership, travel and land use: A comparison of the US and Great Britain. Transportation Research Part A: Policy and Practice 40 (2): 106–124.
Moudon, A., P. Hess, M. Snyder, et al. 1997. Effects of site design on pedestrian travel in mixed-use, medium-density environments. Transportation Research Record: Journal of the Transportation Research Board 1578: 48–55.
Cronbach, L. J. 1951. Coefficient alpha and the internal structure of tests. Psychometrica. External Resources Pubmed/Medline (NLM) ISI Web of Science CrossRef (DOI) 16: 297–334.
Hotelling, H. 1933. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24 (6): 417.
Rumelhart, D.E., G.E. Hinton, and R.J. Williams. 1988. Learning representations by back-propagating errors. Cognitive Modeling 5: 3.
Huang, G., Q. Zhu, and C. Siew. 2006. Extreme learning machine: Theory and applications. Neurocomputing 70 (1–3): 489–501.
Kalogirou, S.A. 2000. Applications of artificial neural-networks for energy systems. Applied Energy 67 (1–2): 17–35.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media Singapore
About this paper
Cite this paper
Ling, L., Li, F., Cao, L. (2018). Analyzing the Relationship Between Urban Macroeconomic Development and Transport Infrastructure System Based on Neural Network. In: Wang, W., Bengler, K., Jiang, X. (eds) Green Intelligent Transportation Systems. GITSS 2016. Lecture Notes in Electrical Engineering, vol 419. Springer, Singapore. https://doi.org/10.1007/978-981-10-3551-7_61
Download citation
DOI: https://doi.org/10.1007/978-981-10-3551-7_61
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3550-0
Online ISBN: 978-981-10-3551-7
eBook Packages: EngineeringEngineering (R0)