Redirigiendo al acceso original de articulo en 22 segundos...
Inicio  /  Sustainability  /  Vol: 10 Núm: 4 Par: April (2018)  /  Artículo
ARTÍCULO
TITULO

Simulating and Predicting the Impacts of Light Rail Transit Systems on Urban Land Use by Using Cellular Automata: A Case Study of Dongguan, China

Jinyao Lin    
Tongli Chen and Qiazi Han    

Resumen

The emergence of Light Rail Transit systems (LRTs) could exert considerable impacts on sustainable urban development. It is crucial to predict the potential land use changes since LRTs are being increasingly built throughout the world. While various land use and land cover change (LUCC) models have been developed during the past two decades, the basic assumption for LUCC prediction is the continuation of present trends in land use development. It is therefore unreasonable to predict potential urban land use changes associated with LRTs simply based on earlier trends because the impacts of LRT investment may vary greatly over time. To tackle this challenge, our study aims to share the experiences from previous lines with newly planned lines. Dongguan, whose government decided to build LRTs around 2008, was selected as the study area. First, we assessed the impacts of this city’s first LRT (Line R2) on three urban land use types (i.e., industrial development, commercial and residential development, and rural development) at different periods. The results indicate that Line R2 exerted a negative impact on industrial development and rural development, but a positive impact on commercial and residential development during the planning stage of this line. Second, such spatial impacts (the consequent land use changes) during this stage were simulated by using artificial neural network cellular automata. More importantly, we further predicted the potential impacts of Line R1, which is assumed to be a newly planned line, based on the above calibrated model and a traditional method respectively. The comparisons between them demonstrate the effectiveness of our method, which can easily take advantage of the experiences from other LRTs. The proposed method is expected to provide technical support for sustainable urban and transportation planning.

 Artículos similares

       
 
Seyed Amirhossein Hosseini and Omar Smadi    
One of the most important components of pavement management systems is predicting the deterioration of the network through performance models. The accuracy of the prediction model is important for prioritizing maintenance action. This paper describes how... ver más
Revista: Infrastructures

 
Ramandeep Kaur M. Malhi, Akash Anand, Prashant K. Srivastava, G. Sandhya Kiran, George P. Petropoulos and Christos Chalkias    
Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropog... ver más

 
Khalid Alotaibi, Abdul Razzaq Ghumman, Husnain Haider, Yousry Mahmoud Ghazaw and Md. Shafiquzzaman    
Future predictions of rainfall patterns in water-scarce regions are highly important for effective water resource management. Global circulation models (GCMs) are commonly used to make such predictions, but these models are highly complex and expensive. ... ver más
Revista: Water

 
Yang Cao, Jing Zhang, Mingxiang Yang, Xiaohui Lei, Binbin Guo, Liu Yang, Zhiqiang Zeng and Jiashen Qu    
The China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool model (CMADS) have been widely applied in recent years because of their accuracy. An evaluation of the accuracy and efficiency of the Soil and Water Assessment ... ver más
Revista: Water

 
Soon-Kun Choi, Jaehak Jeong and Min-Kyeong Kim    
The Agricultural Policy/Environmental eXtender (APEX) model is widely used for evaluating agricultural conservation efforts and their effects on soil and water. A key component of APEX application in Korea is simulating the water quality impacts of rice ... ver más
Revista: Water