Simulating the effect of urban light rail transit on urban development by coupling cellular automata and conjugate gradients

Urban light rail transit systems have a significant potential to alter future urban development. We developed a new cellular automata model (CA(CG)) based on conjugate gradients, and applied it to 1) simulate historical urban development at Jinhua of China, and 2) project future development scenarios incorporating the effect of future light rail transit stations (LRTS). The model produced a realistic urban pattern for 2018 with overall accuracy exceeding 95%, Kappa coefficient exceeding 70% and figure-of-merit exceeding 32%, indicating the model's ability to accurately capture urban dynamics. We predicted three different scenarios: a benchmark scenario of business as usual (BAU), a scenario focusing on LRTS, and an individual-factor-based scenario (ILRTS). The results show that the annual urban development intensity has the strongest correlation with LRT stations for LRTS-scenario, followed by ILRTS-scenario and BAU-scenario. The model can be readily applied elsewhere to assess the impact of urban infrastructure on future development.